Home Page for Nancy M. Amato | Parasol Laboratory


Picture Nancy M. Amato
Abel Bliss Professor and Interim Director, Siebel School of Computing and Science
Parasol Laboratory url: http://parasollab.web.illinois.edu/~namato/
Siebel School of Computing and Data Science email:
University of Illinois at Urbana-Champaign office: 3229 Siebel Center
Urbana, IL 61801, USA tel: +1-217-333-3426


Mentoring & Other Resources


WAFR 2024


The 16th International Workshop on the Algorithmic Foundations of Robotics.
Chicago, USA, October 7-9 2024.

Mind in Vitro: Computing with Living Neurons


This NSF Expedition in Computing will develop the science and technology to fabricate, model and program systems based on living neurons. Interfacing with muscles and sensors, the behavior of these machines will evolve as they probe their environment, explore and respond to it.

Robot Task and Motion Planning


Motion planning has applications in robotics, games/virtual reality, computer-aided design/virtual prototyping, and bioinformatics. Our research is focused on developing motion planning algorithms and applying them to a wide range of problems.

STAPL: Standard Template Adaptive Parallel Library


The Standard Template Adaptive Parallel Library is a framework for developing parallel programs in C++. It is designed to work on both shared and distributed memory parallel computers and its core is a library of ISO Standard C++ components with interfaces similar to the (sequential) ISO C++ standard library.

Computational Biology


Our group is investigating applications of sampling-based motion planning methods to protein folding, ligand binding (i.e., drug docking, which arises in drug design), RNA folding, and neuroscience.

LCPC 2022


The 35th International Workshop on Languages and Compilers for Parallel Computing.
Chicago, Illinois, October 12-14, 2022

US National Robotics Roadmap Workshop - Chicago


The US National Robotics Roadmap was first created in 2009. It has since been used by government agencies, universities and companies as a reference about where robotics is going.
Chicago, Illinois, September 11-12, 2019

Excursions in Algorithmics


Excursions in Algorithmics: A late festschrift for Franco P. Preparata.
Brown University, Providence, RI, October 27-28, 2006.

Publications (incomplete)

Extended Reality System for Robotic Learning from Human Demonstration, Isaac Ngui, Courtney McBeth, Grace He, André Corrêa Santos, Luciano Soares, Marco Morales, Nancy M. Amato, ArXiv Preprint, Sep 2024.
Keywords: Machine Learning, Robotics, Virtual Reality
Links : [ArXiv]

BibTex

@misc{ngui2024extendedrealityroboticlearning,
title={Extended Reality System for Robotic Learning from Human Demonstration},
author={Isaac Ngui and Courtney McBeth and Grace He and André Corrêa Santos and Luciano Soares and Marco Morales and Nancy M. Amato},
year={2024},
eprint={2409.12862},
archivePrefix={arXiv},
primaryClass={cs.RO},
url={https://arxiv.org/abs/2409.12862},
}


Abstract

Many real-world tasks are intuitive for a human to perform, but difficult to encode algorithmically when utilizing a robot to perform the tasks. In these scenarios, robotic systems can benefit from expert demonstrations to learn how to perform each task. In many settings, it may be difficult or unsafe to use a physical robot to provide these demonstrations, for example, considering cooking tasks such as slicing with a knife. Extended reality provides a natural setting for demonstrating robotic trajectories while bypassing safety concerns and providing a broader range of interaction modalities. We propose the Robot Action Demonstration in Extended Reality (RADER) system, a generic extended reality interface for learning from demonstration. We additionally present its application to an existing state-of-the-art learning from demonstration approach and show comparable results between demonstrations given on a physical robot and those given using our extended reality system.


Scalable Multi-Robot Motion Planning Using Guidance-Informed Hypergraphs, Courtney McBeth, James Motes, Isaac Ngui, Marco Morales, Nancy M. Amato, ArXiv Preprint, Jun 2024.
Keywords: Motion Planning, Multi-Agent, Workspace Topology
Links : [ArXiv]

BibTex

@misc{mcbeth2024scalablemultirobotmotionplanning,
title={Scalable Multi-Robot Motion Planning Using Guidance-Informed Hypergraphs},
author={Courtney McBeth and James Motes and Isaac Ngui and Marco Morales and Nancy M. Amato},
year={2024},
eprint={2311.10176},
archivePrefix={arXiv},
primaryClass={cs.RO},
url={https://arxiv.org/abs/2311.10176},
}


Abstract

In this work, we present a multi-robot planning framework that leverages guidance about the problem to efficiently search the planning space. This guidance captures when coordination between robots is necessary, allowing us to decompose the intractably large multi-robot search space while limiting risk of inter-robot conflicts by composing relevant robot groups together while planning. Our framework additionally supports planning with kinodynamic constraints through our conflict resolution structure. This structure also improves the scalability of our approach by eliminating unnecessary work during the construction of motion solutions. We also provide an application of this framework to multiple mobile robot motion planning in congested environments using topological guidance. Our previous work has explored using topological guidance, which utilizes information about the robots' environment, in these multi-robot settings where a high degree of coordination is required of the full robot group. In real-world scenarios, this high level of coordination is not always necessary and results in excessive computational overhead. Here, we leverage our novel framework to achieve a significant improvement in scalability and show that our method efficiently finds paths for robot teams up to an order of magnitude larger than existing state-of-the-art methods in congested settings with narrow passages in the environment.


Adaptive Robot Coordination: A Subproblem-based Approach for Hybrid Multi-Robot Motion Planning, Irving Solis, James Motes, Mike Qin, Marco Morales, Nancy M. Amato, IEEE Robotics and Automation Letters, Vol: 9, Issue: 8, pp. 7238-7245, Jun 2024. DOI: 10.1109/LRA.2024.3420548
Keywords: Motion Planning, Multi-Agent Systems, Sampling-Based Motion Planning
Links : [Published] [ArXiv] [Video]

BibTex

@ARTICLE{10577245,
author={Solis, Irving and Motes, James and Qin, Mike and Morales, Marco and Amato, Nancy M.},
journal={IEEE Robotics and Automation Letters},
title={Adaptive Robot Coordination: A Subproblem-Based Approach for Hybrid Multi-Robot Motion Planning},
year={2024},
volume={9},
number={8},
pages={7238-7245},
keywords={Robots;Robot kinematics;Planning;Space exploration;Probabilistic logic;Mobile robots;Collision avoidance;Path planning for multiple mobile robots or agents;multi-robot systems;motion and path planning},
doi={10.1109/LRA.2024.3420548}}


Abstract

This work presents Adaptive Robot Coordination (ARC), a novel hybrid framework for multi-robot motion planning (MRMP) that employs local subproblems to resolve inter-robot conflicts. ARC creates subproblems centered around conflicts, and the solutions represent the robot motions required to resolve these conflicts. The use of subproblems enables an innovative, cost-effective hybrid exploration of the multi-robot planning space by dynamically coupling and decoupling necessary subsets of robots only when required and in specific physical locations. This allows ARC to adapt the levels of coordination efficiently by planning in decoupled spaces, where robots can operate independently, and in coupled spaces, where coordination is essential. ARC is probabilistically complete, can be used for any robot, and produces cost-efficient solutions in reduced planning times. Through extensive evaluation across representative scenarios with different robots requiring various levels of coordination, ARC demonstrates its ability to provide simultaneous scalability and precise coordination. ARC is the only method capable of solving all the scenarios and is competitive with coupled, decoupled, and hybrid baselines.


Hierarchical Annotated Skeleton-Guided Tree-based Motion Planning, Diane Uwacu, Ananya Yammanuru, Keerthana Nallamotu, Vasu Chalasani, Marco Morales, Nancy M. Amato, arXiv Preprint, Sep 2023.
Keywords: Lazy Planning, Motion Planning, Workspace Topology
Links : [ArXiv]

BibTex

@misc{uwacu2023hierarchical,
title={Hierarchical Annotated Skeleton-Guided Tree-based Motion Planning},
author={Diane Uwacu and Ananya Yammanuru and Keerthana Nallamotu and Vasu Chalasani and Marco Morales and Nancy M. Amato},
year={2023},
eprint={2309.10801},
archivePrefix={arXiv},
primaryClass={cs.RO}
}


Abstract

We present a hierarchical tree-based motion planning strategy, HAS-RRT, guided by the workspace skeleton to solve motion planning problems in robotics and computational biology. Relying on the information about the connectivity of the workspace and the ranking of available paths in the workspace, the strategy prioritizes paths indicated by the workspace guidance to find a valid motion plan for the moving object efficiently. In instances of suboptimal guidance, the strategy adapts its reliance on the guidance by hierarchically reverting to local exploration of the planning space. We offer an extensive comparative analysis against other tree-based planning strategies and demonstrate that HAS-RRT reliably and efficiently finds low-cost paths. In contrast to methods prone to inconsistent performance across different environments or reliance on specific parameters, HAS-RRT is robust to workspace variability.


Scalable Multi-robot Motion Planning for Congested Environments With Topological Guidance, Courtney McBeth, James Motes, Diane Uwacu, Marco Morales, Nancy M. Amato, In IEEE Robotics and Automation Letters, Aug 2023. DOI: 10.1109/LRA.2023.3312980
Keywords: Motion Planning, Multi-Agent, Workspace Topology
Links : [Published] [ArXiv] [Video]

BibTex

@ARTICLE{10243143,
author={McBeth, Courtney and Motes, James and Uwacu, Diane and Morales, Marco and Amato, Nancy M.},
journal={IEEE Robotics and Automation Letters},
title={Scalable Multi-robot Motion Planning for Congested Environments With Topological Guidance},
year={2023},
volume={},
number={},
pages={1-8},
doi={10.1109/LRA.2023.3312980}}


Abstract

Multi-robot motion planning (MRMP) is the problem of finding collision-free paths for a set of robots in a continuous state space. The difficulty of MRMP increases with the number of robots and is exacerbated in environments with narrow passages that robots must pass through, like warehouse aisles where coordination between robots is required. In single-robot settings, topology-guided motion planning methods have shown improved performance in these constricted environments. In this work, we extend an existing topology-guided single-robot motion planning method to the multi-robot domain to leverage the improved efficiency provided by topological guidance. We demonstrate our method's ability to efficiently plan paths in complex environments with many narrow passages, scaling to robot teams of size up to 25 times larger than existing methods in this class of problems. By leveraging knowledge of the topology of the environment, we also find higher-quality solutions than other methods.


Hypergraph-Based Multi-robot Task and Motion Planning, James Motes, Tan Chen, Timothy Bretl, Marco Morales, Nancy M. Amato, IEEE Transactions on Robotics (TRO), Aug 2023. DOI: 10.1109/TRO.2023.3297011
Keywords: Multi-Agent, Task Planning
Links : [Published] [Manuscript] [Video]

BibTex

@article{lee2021parallel,
title={Parallel hierarchical composition conflict-based search for optimal multi-agent pathfinding},
author={Lee, Hannah and Motes, James and Morales, Marco and Amato, Nancy M},
journal={IEEE Robotics and Automation Letters},
volume={6},
number={4},
pages={7001--7008},
year={2021},
publisher={IEEE}
}


Abstract

In this article, we present a multi-robot task and motion planning method that, when applied to the rearrangement of objects by manipulators, results in solution times up to three orders of magnitude faster than the existing methods and successfully plans for problems with up to 20 objects, more than three times as many objects as comparable methods. We achieve this improvement by decomposing the planning space to consider manipulators alone, objects, and manipulators holding objects. We represent this decomposition with a hypergraph where vertices are decomposed elements of the planning spaces and hyperarcs are transitions between elements. The existing methods use graph-based representations where vertices are full composite spaces and edges are transitions between these. Using the hypergraph reduces the representation size of the planning space for multimanipulator object rearrangement, the number of hypergraph vertices scales linearly with the number of either robots or objects, while the number of hyperarcs scales quadratically with the number of robots and linearly with the number of objects. In contrast, the number of vertices and edges in graph-based representations scales exponentially in the number of robots and objects. We show that similar gains can be achieved for other multi-robot task and motion planning problems.


Discrete State-Action Abstraction via the Successor Representation, Amnon Attali, Pedro Cisneros-Velarde, Marco Morales, Nancy M. Amato, arXiv preprint, May 2023.
Keywords: Abstraction
Links : [ArXiv]

BibTex

@article{attali2022discrete,
title={Discrete State-Action Abstraction via the Successor Representation},
author={Attali, Amnon and Cisneros-Velarde, Pedro and Morales, Marco and Amato, Nancy M},
journal={arXiv preprint arXiv:2206.03467},
year={2022}
}


Abstract

While the difficulty of reinforcement learning problems is typically related to the complexity of their state spaces, Abstraction proposes that solutions often lie in simpler underlying latent spaces. Prior works have focused on learning either a continuous or dense abstraction, or require a human to provide one. Information-dense representations capture features irrelevant for solving tasks, and continuous spaces can struggle to represent discrete objects. In this work we automatically learn a sparse discrete abstraction of the underlying environment. We do so using a simple end-to-end trainable model based on the successor representation and max-entropy regularization. We describe an algorithm to apply our model, named Discrete State-Action Abstraction (DSAA), which computes an action abstraction in the form of temporally extended actions, i.e., Options, to transition between discrete abstract states. Empirically, we demonstrate the effects of different exploration schemes on our resulting abstraction, and show that it is efficient for solving downstream tasks.


Hierarchical Planning With Annotated Skeleton Guidance, Diane Uwacu, Ananya Yammanuru, Marco Morales, Nancy M. Amato, IEEE Robotics and Automation Letters (RA-L), Vol: 7, Issue: 4, pp. 11055-11061, Oct 2022. DOI: 10.1109/LRA.2022.3196885
Keywords: Lazy Evaluation, Motion Planning, Workspace Topology
Links : [Published] [Manuscript] [Video]

BibTex

@ARTICLE{9851528,
author={Uwacu, Diane and Yammanuru, Ananya and Morales, Marco and Amato, Nancy M.},
journal={IEEE Robotics and Automation Letters},
title={Hierarchical Planning With Annotated Skeleton Guidance},
year={2022},
volume={7},
number={4},
pages={11055-11061},
doi={10.1109/LRA.2022.3196885}}


Abstract

We present a hierarchical skeleton-guided motion planning algorithm to guide mobile robots. A good skeleton maps the connectivity of the subspace of c-space containing significant degrees of freedom and is able to guide the planner to find the desired solutions fast. However, sometimes the skeleton does not closely represent the free c-space, which often misleads current skeleton-guided planners. The hierarchical skeleton-guided planning strategy gradually relaxes its reliance on the workspace skeleton as C-space is sampled, thereby incrementally returning a sub-optimal path, a feature that is not guaranteed in the standard skeleton-guided algorithm. Experimental comparisons to the standard skeleton guided planners and other lazy planning strategies show significant improvement in roadmap construction run time while maintaining path quality for multi-query problems in cluttered environments.


Evaluating Guiding Spaces for Motion Planning, Amnon Attali, Stav Ashur, Isaac Burton Love, Courtney McBeth, James Motes, Diane Uwacu, Marco Morales, Nancy M. Amato, IROS 2022, Workshop for Evaluating Motion Planning Performance, Kyoto, Japan, Oct 2022.
Keywords: Algorithms, Guidance
Links : [ArXiv]

BibTex

@misc{https://doi.org/10.48550/arxiv.2210.08640,
doi = {10.48550/ARXIV.2210.08640},

url = {https://arxiv.org/abs/2210.08640},

author = {Attali, Amnon and Ashur, Stav and Love, Isaac Burton and McBeth, Courtney and Motes, James and Uwacu, Diane and Morales, Marco and Amato, Nancy M.},

keywords = {Robotics (cs.RO), Artificial Intelligence (cs.AI), FOS: Computer and information sciences, FOS: Computer and information sciences},

title = {Evaluating Guiding Spaces for Motion Planning},

publisher = {arXiv},

year = {2022},

copyright = {Creative Commons Attribution 4.0 International}
}


Abstract

Randomized sampling based algorithms are widely used in robot motion planning due to the problem's intractability, and are experimentally effective on a wide range of problem instances. Most variants do not sample uniformly at random, and instead bias their sampling using various heuristics for determining which samples will provide more information, or are more likely to participate in the final solution. In this work, we define the motion planning guiding space, which encapsulates many seemingly distinct prior works under the same framework. In addition, we suggest an information theoretic method to evaluate guided planning which places the focus on the quality of the resulting biased sampling. Finally, we analyze several motion planning algorithms in order to demonstrate the applicability of our definition and its evaluation.


Insights from an Industrial Collaborative Assembly Project: Lessons in Research and Collaboration, Tan Chen, Zhe Huang, James Motes, Junyi Geng, Quang Minh Ta, Holly Dinkel, Hameed Abdul-Rashid, Jessica Myers, Ye-Ji Mun, Wei-che Lin, Yuan-yung Huang, Sizhe Liu, Marco Morales, Nancy M Amato, Katherine Driggs-Campbell, Timothy Bretl, ICRA 2022 WORKSHOP ON COLLABORATIVE ROBOTS AND THE WORK OF THE FUTURE, Philadelphia, PA, USA, May 2022.
Keywords: Assembly, Industrial Applications, Interaction
Links : [ArXiv]

BibTex

@article{chen2022insights,
title={Insights from an Industrial Collaborative Assembly Project: Lessons in Research and Collaboration},
author={Chen, Tan and Huang, Zhe and Motes, James and Geng, Junyi and Ta, Quang Minh and Dinkel, Holly and Abdul-Rashid, Hameed and Myers, Jessica and Mun, Ye-Ji and Lin, Wei-che and others},
journal={arXiv preprint arXiv:2205.14340},
year={2022}
}


Abstract

Significant progress in robotics reveals new opportunities to advance manufacturing. Next-generation industrial automation will require both integration of distinct robotic technologies and their application to challenging industrial environments. This paper presents lessons from a collaborative assembly project between three academic research groups and an industry partner. The goal of the project is to develop a flexible, safe, and productive manufacturing cell for sub-centimeter precision assembly. Solving this problem in a high-mix, low-volume production line motivates multiple research thrusts in robotics. This work identifies new directions in collaborative robotics for industrial applications and offers insight toward strengthening collaborations between institutions in academia and industry on the development of new technologies.


Parallel Hierarchical Composition Conflict-Based Search, Hannah Lee, James Motes, Marco Morales, Nancy M. Amato, IEEE/RSJ International Conference on Intelligent Robots and Systems, Vol: 6, Issue: 4, pp. 7001-7008, Prague, Czech Republic, Jul 2021. DOI: 10.1109/LRA.2021.3096476.
Keywords: Multi-Agent, Parallel Planning, Path Planning
Links : [Published]

BibTex

@article{lee2021parallel,
title={Parallel Hierarchical Composition Conflict-Based Search for Optimal Multi-Agent Pathfinding},
author={Lee, Hannah and Motes, James and Morales, Marco and Amato, Nancy M},
journal={IEEE Robotics and Automation Letters},
volume={6},
number={4},
pages={7001--7008},
year={2021},
publisher={IEEE}
}


Abstract

In this letter, we present the following optimal multi-agent pathfinding (MAPF) algorithms: Hierarchical Composition Conflict-Based Search, Parallel Hierarchical Composition Conflict-Based Search, and Dynamic Parallel Hierarchical Composition Conflict-Based Search. MAPF is the task of finding an optimal set of valid path plans for a set of agents such that no agents collide with present obstacles or each other. The presented algorithms are an extension of Conflict-Based Search (CBS), where the MAPF problem is solved by composing and merging smaller, more easily manageable subproblems. Using the information from these subproblems, the presented algorithms can more efficiently find an optimal solution. Our three presented algorithms demonstrate improved performance for optimally solving MAPF problems consisting of many agents in crowded areas while examining fewer states when compared with CBS and its variant Improved Conflict-Based Search.


Avoidance Critical Probabilistic Roadmaps for Motion Planning in Dynamic Environments, Felipe Felix Arias, Brian Ichter, Aleksandra Faust, Nancy M. Amato, IEEE International Conference on Robotics and Automation (ICRA), Xien, China, Jun 2021. DOI: Published
Keywords: Machine Learning, Mobile Robots, Spatial Awareness
Links : [Published] [Video]

BibTex

@inproceedings{50155,
title = {Avoidance Critical Probabilistic Roadmaps for Motion Planning in Dynamic Environments},
author = {Felipe Felix Arias and Brian Andrew Ichter and Aleksandra Faust and Nancy M. Amato},
year = {2021},
booktitle = {IEEE International Conference on Robotics and Automation (ICRA)}
}


Abstract

Motion planning among dynamic obstacles is an essential capability towards navigation in the real-world. Sampling-based motion planning algorithms find solutions by approximating the robot’s configuration space through a graph representation, predicting or computing obstacles’ trajectories, and finding feasible paths via a pathfinding algorithm. In this work, we seek to improve the performance of these subproblems by identifying regions critical to dynamic environment navi- gation and leveraging them to construct sparse probabilistic roadmaps. Motion planning and pathfinding algorithms should allow robots to prevent encounters with obstacles, irrespective of their trajectories, by being conscious of spatial context cues such as the location of chokepoints (e.g., doorways). Thus, we propose a self-supervised methodology for learning to identify regions frequently used for obstacle avoidance from local environment features. As an application of this concept, we leverage a neural network to generate hierarchical probabilistic roadmaps termed Avoidance Critical Probabilistic Roadmaps (ACPRM). These roadmaps contain motion structures that enable efficient obstacle avoidance, reduce the search and planning space, and increase a roadmap’s reusability and coverage. ACPRMs are demonstrated to achieve up to five orders of magnitude improvement over grid-sampling in the multi-agent setting and up to ten orders of magnitude over a competitive baseline in the multi-query setting.


Representation-Optimal Multi-Robot Motion Planning using Conflict-Based Search, Irving Solis, James Motes, Read Sandström, Nancy M. Amato, IEEE Robotics and Automation Letters, Mar 2021. DOI: https://doi.org/10.1109/LRA.2021.3068910
Keywords: Industrial Applications, Motion Planning, Multi-Agent
Links : [Published] [Manuscript]

BibTex

@article{solis2019representation,
title={Representation-optimal multi-robot motion planning using conflict-based search},
author={Solis, Irving and Sandstr{\"o}m, Read and Motes, James and Amato, Nancy M},
journal={arXiv preprint arXiv:1909.13352},
year={2019}
}


Abstract

Multi-Agent Motion Planning (MAMP) is the problem of computing feasible paths for a set of agents each with individual start and goal states within a continuous state space. Existing approaches can be split into coupled methods which provide optimal solutions but struggle with scalability or decoupled methods which provide scalable solutions but offer no optimality guarantees. Recent work has explored hybrid approaches that leverage the advantages of both coupled and decoupled approaches in an easier discrete subproblem, Multi-Agent Pathfinding (MAPF). In this work, we adapt recent developments in hybrid MAPF to the continuous domain of MAMP. We demonstrate the scalability of our method to manage groups of up to 32 agents, demonstrate the ability to handle up to 8 high-DOF manipulators, and plan for heterogeneous teams. In all scenarios, our approach plans significantly faster while providing higher quality solutions.


Topology-Guided Roadmap Construction with Dynamic Region Sampling, Read Sandström, Diane Uwacu, Jory Denny, Nancy M. Amato, IEEE Robotics and Automation Letters (RA-L), vol. 5, no. 4,, pp. 6161-6168, virtual, Oct 2020. DOI: 10.1109/LRA.2020.3010487
Keywords: Sampling-Based Motion Planning, Workspace Topology
Links : [Published]

BibTex

@ARTICLE{9144398,
author={R. {Sandström} and D. {Uwacu} and J. {Denny} and N. M. {Amato}},
journal={IEEE Robotics and Automation Letters},
title={Topology-Guided Roadmap Construction With Dynamic Region Sampling},
year={2020},
volume={5},
number={4},
pages={6161-6168},}


Abstract

Many types of planning problems require discovery of multiple pathways through the environment, such as multi-robot coordination or protein ligand binding. The Probabilistic Roadmap (PRM) algorithm is a powerful tool for this case, but often cannot efficiently connect the roadmap in the presence of narrow passages. In this letter, we present a guidance mechanism that encourages the rapid construction of well-connected roadmaps with PRM methods. We leverage a topological skeleton of the workspace to track the algorithm's progress in both covering and connecting distinct neighborhoods, and employ this information to focus computation on the uncovered and unconnected regions. We demonstrate how this guidance improves PRM's efficiency in building a roadmap that can answer multiple queries in both robotics and protein ligand binding applications.


Using Guided Motion Planning to Study Binding Site Accessibility, Diane Uwacu, Abigail Ren, Shawna Thomas, Nancy M. Amato, Proceedings of the 11th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics, Issue: 109, (Virtual) New York, USA, Sep 2020. DOI: 10.1145/3388440.3414707
Keywords: Computational Biology, Ligand Binding, Motion Planning
Links : [Published] [Manuscript]

BibTex

@inbook{10.1145/3388440.3414707,
author = {Uwacu, Diane and Ren, Abigail and Thomas, Shawna and Amato, Nancy M.},
title = {Using Guided Motion Planning to Study Binding Site Accessibility},
year = {2020},
isbn = {9781450379649},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3388440.3414707},
abstract = {Computational methods are commonly used to predict protein-ligand interactions. These methods typically search for regions with favorable energy that geometrically fit the ligand, and then rank them as potential binding sites. While this general strategy can provide good predictions in some cases, it does not do well when the binding site is not accessible to the ligand. In addition, recent research has shown that in some cases protein access tunnels play a major role in the activity and stability of the protein's binding interactions. Hence, to fully understand the binding behavior of such proteins, it is imperative to identify and study their access tunnels. In this work, we present a motion planning algorithm that scores protein binding site accessibility for a particular ligand. This method can be used to screen ligand candidates for a protein by eliminating those that cannot access the binding site. This method was tested on two case studies to analyze effects of modifying a protein's access tunnels to increase activity and/or stability as well as study how a ligand inhibitor blocks access to the protein binding site.},
booktitle = {Proceedings of the 11th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics},
articleno = {109},
numpages = {10}
}




Abstract

Computational methods are commonly used to predict protein-ligand interactions. These methods typically search for regions with favorable energy that geometrically fit the ligand, and then rank them as potential binding sites. While this general strategy can provide good predictions in some cases, it does not do well when the binding site is not accessible to the ligand. In addition, recent research has shown that in some cases protein access tunnels play a major role in the activity and stability of the protein's binding interactions. Hence, to fully understand the binding behavior of such proteins, it is imperative to identify and study their access tunnels. In this work, we present a motion planning algorithm that scores protein binding site accessibility for a particular ligand. This method can be used to screen ligand candidates for a protein by eliminating those that cannot access the binding site. This method was tested on two case studies to analyze effects of modifying a protein's access tunnels to increase activity and/or stability as well as study how a ligand inhibitor blocks access to the protein binding site.


Dynamic Region-biased Rapidly-exploring Random Trees, Jory Denny and Read Sandstrom and Andrew Bregger and Nancy M. Amato, Algorithmic Foundations of Robotics XII. Springer Proceedings in Advanced Robotics (SPAR). The 2016 Workshop on the Algorithmic Foundations of Robotics (WAFR), Vol: 13, pp. 640-655, May 2020. DOI: 10.1007/978-3-030-43089-4_41
Keywords: Sampling-Based Motion Planning, Workspace Topology
Links : [Published]

BibTex

@article{denny-wafr-2016
, author = "Jory Denny and Read Sandstrom and Andrew Bregger and Nancy M. Amato"
, title = "Dynamic Region-biased Rapidly-exploring Random Trees"
, journal = "Algorithmic Foundations of Robotics XII. Springer Proceedings in Advanced Robotics (SPAR)"
, volume = "13"
, publisher = "Springer, Cham."
, pages = "640-655"
, year = "2020"
, doi = "10.1007/978-3-030-43089-4\_41"
, note = "Presented at the 2016 Workshop on the Algorithmic Foundations of Robotics (WAFR)."
}


Abstract

Current state-of-the-art motion planners rely on samplingbased planning to explore the problem space for a solution. However, sampling valid configurations in narrow or cluttered workspaces remains a challenge. If a valid path for the robot correlates to a path in the workspace, then the planning process can employ a representation of the workspace that captures its salient topological features. Prior approaches have investigated exploiting geometric decompositions of the workspace to bias sampling; while beneficial in some environments, complex narrow passages remain challenging to navigate. In this work, we present Dynamic Region-biased RRT, a novel samplingbased planner that guides the exploration of a Rapidly-exploring Random Tree (RRT) by moving sampling regions along an embedded graph that captures the workspace topology. These sampling regions are dynamically created, manipulated, and destroyed to greedily bias sampling through unexplored passages that lead to the goal. We show that our approach reduces online planning time compared with related methods on a set of maze-like problems.


Fast Collision Detection for Motion Planning Using Shape Primitive Skeletons, Mukulika Ghosh and Shawna Thomas and Nancy M. Amato, Algorithmic Foundations of Robotics XIII. Springer Proceedings in Advanced Robotics (SPAR). The 2018 Workshop on the Algorithmic Foundations of Robotics (WAFR), Vol: 14, pp. 36-51, Springer, Cham, May 2020. DOI: 10.1007/978-3-030-44051-0_3
Keywords: Collision Detection, Computational Geometry, Motion Planning
Links : [Published]

BibTex

@article{ghosh-wafr-2018
, author = "Mukulika Ghosh and Shawna Thomas and Nancy M. Amato"
, title = "Fast Collision Detection for Motion Planning Using Shape Primitive Skeletons"
, journal = "Algorithmic Foundations of Robotics XIII. Springer Proceedings in Advanced Robotics (SPAR)"
, publisher = "Springer, Cham."
, volume = "14"
, pages = "36-51"
, year = "2020"
, doi = "10.1007/978-3-030-44051-0_3"
}


Abstract

In many robotics applications, the environment (robots and obstacles) often have very complex geometries. These result in expensive primitive computations such as collision detection which in turn, affect the overall performance of these applications. Approximating the geometry is a common approach to optimize computation. Unlike other applications of geometric approximation where it is applied to one space (usually obstacle space), we approximate both obstacle and free workspace with a set of geometric shape primitives that are completely contained within the space and represent its topology (skeleton). We use these “shape primitive skeletons” to improve collision detection performance in motion planning algorithms. Our results show that the use of shape primitive skeletons improves the performance of standard collision detection methods in motion planning problems by 20–70% in our 2D and 3D test environments regardless of motion planning strategy. We also show how the same shape primitive skeletons can be used with robots of different sizes to improve the performance of collision detection operation.


Provably Optimal Parallel Transport Sweeps on Semi-Structured Grids, Michael P. Adams, Marvin L. Adams, W. Daryl Hawkins, Timmie G. Smith, Lawrence Rauchwerger, Nancy M. Amato, Teresa S. Bailey, Robert D. Falgout, Adam Kunen, Peter Brown, Journal of Computational Physics, Vol: 407, Apr 2020. DOI: 10.1016/j.jcp.2020.109234
Keywords: Parallel Algorithms, Scientific Computing
Links : [Published]

BibTex

@article{DBLP:journals/jcphy/AdamsAHSRABFKB20,
author = {Michael P. Adams and
Marvin L. Adams and
W. Daryl Hawkins and
Timmie G. Smith and
Lawrence Rauchwerger and
Nancy M. Amato and
Teresa S. Bailey and
Robert D. Falgout and
Adam Kunen and
Peter Brown},
title = {Provably optimal parallel transport sweeps on semi-structured grids},
journal = {J. Comput. Phys.},
volume = {407},
pages = {109234},
year = {2020},
url = {https://doi.org/10.1016/j.jcp.2020.109234},
doi = {10.1016/j.jcp.2020.109234},
timestamp = {Fri, 27 Mar 2020 14:16:32 +0100},
biburl = {https://dblp.org/rec/journals/jcphy/AdamsAHSRABFKB20.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}


Abstract

We have found provably optimal algorithms for full-domain discrete-ordinate transport sweeps on a class of grids in 2D and 3D Cartesian geometry that are regular at a coarse level but arbitrary within the coarse blocks. We describe these algorithms and show that they always execute the full eight-octant (or four-quadrant if 2D) sweep in the minimum possible number of stages for a given Px x Py x Pz partitioning. Computational results confirm that our optimal scheduling algorithms execute sweeps in the minimum possible stage count. Observed parallel efficiencies agree well with our performance model. Our PDT transport code has achieved approximately 68% parallel efficiency with > 1.5M parallel threads, relative to 8 threads, on a simple weak-scaling problem with only three energy groups, 10 direction per octant, and 4096 cells/core. We demonstrate similar efficiencies on a much more realistic set of nuclear-reactor test problems, with unstructured meshes that resolve fine geometric details. These results demonstrate that discrete-ordinates transport sweeps can be executed with high efficiency using more than 106 parallel processes.


Multi-Robot Task and Motion Planning with Subtask Dependencies, James Motes, Read Sandstrom, Hannah Lee, Shawna Thomas, Nancy M. Amato, IEEE Robotics and Automation Letters (RA-L), Vol: 5, Issue: 2, pp. 3338-3345, Feb 2020. DOI: 10.1109/LRA.2020.2976329
Keywords: Motion Planning, Multi-Agent, Task Planning
Links : [Published]

BibTex

@article{motes2020multi,
title={Multi-Robot Task and Motion Planning With Subtask Dependencies},
author={Motes, James and Sandstr{\"o}m, Read and Lee, Hannah and Thomas, Shawna and Amato, Nancy M},
journal={IEEE Robotics and Automation Letters},
volume={5},
number={2},
pages={3338--3345},
year={2020},
publisher={IEEE}
}


Abstract

We present a multi-robot integrated task and motion method capable of handling sequential subtask dependencies within multiply decomposable tasks. We map the multi-robot pathfinding method, Conflict Based Search, to task planning and integrate this with motion planning to create TMP-CBS. TMP-CBS couples task decomposition, allocation, and planning to support cases where the optimal solution depends on robot availability and inter-team conflict avoidance. We show improved planning time for simpler task sets and generate optimal solutions w.r.t. the state space representation for a broader range of problems than prior methods.


Interaction Templates for Multi-Robot Systems, James Motes, Read Sandstrom, Will Adams, Tobi Ogunyale, Shawna Thomas, Nancy M. Amato, IEEE Robotics and Automation Letters, Vol: 4, Issue: 3, pp. 2926-2933, Jun 2019. DOI: 10.1109/LRA.2019.2923386
Keywords: Interaction, Multi-Agent, Task Planning
Links : [Published]

BibTex

@article{motes2019interaction,
title={Interaction templates for multi-robot systems},
author={Motes, James and Sandstr{\"o}m, Read and Adams, Will and Ogunyale, Tobi and Thomas, Shawna and Amato, Nancy M},
journal={IEEE Robotics and Automation Letters},
volume={4},
number={3},
pages={2926--2933},
year={2019},
publisher={IEEE}
}


Abstract

This letter describes a framework for multi-robot problems that require or utilize interactions between robots. Solutions consider interactions on a motion planning level to determine the feasibility and cost of the multi-robot team solution. Modeling these problems with current integrated task and motion planning (TMP) approaches typically requires reasoning about the possible interactions and checking many of the possible robot combinations when searching for a solution. We present a multi-robot planning method called Interaction Templates (ITs), which moves certain types of robot interactions from the task planner to the motion planner. ITs model interactions between a set of robots with a small roadmap. This roadmap is then tiled into the environment and connected to the robots’ individual roadmaps. The resulting combined roadmap allows interactions to be considered by the motion planner. We apply ITs to homogeneous and heterogeneous robot teams under both required and optional cooperation scenarios, which previously required a task planning method. We show improved performance over a current TMP planning approach.


Feasibility Study of Robotic Needles with a Rotational Tip-Joint and Notch Patterns, Shivanand Pattanshetti, Read Sandström, Abhishek Kottala, Nancy M. Amato, Seok Chang Ryu, Proc. IEEE Int. Conf. on Robotics and Automation (ICRA), pp. 1534-1540, Montreal, QC, Canada, Canada, May 2019. DOI: 10.1109/ICRA.2019.8793574
Keywords: Medical Robotics, Motion Planning
Links : [Published]

BibTex

@inproceedings{pattanshetti2019feasibility,
title={Feasibility Study of Robotic Needles with a Rotational Tip-Joint and Notch Patterns},
author={Pattanshetti, Shivanand and Sandstr{\"o}m, Read and Kottala, Abhishek and Amato, Nancy M and Ryu, Seok Chang},
booktitle={2019 International Conference on Robotics and Automation (ICRA)},
pages={1534--1540},
year={2019},
organization={IEEE}
}


Abstract

In this paper, we present the design of a steerable needle with proximal notch patterns for compliance and an embedded rotational tip joint for articulation. The device is fabricated by laser machining NiTi tube so that an inner working channel exists (to enable delivery of fluids, drugs or microtools) and no assembly is required for the joints. We formulate its model based on the classical Cosserat Rod theory. This is extended with incremental state prediction and a simple spring model for tissue reaction to integrate into a planning algorithm based on Dynamic Region RRT which efficiently explores the needle's state space. The planner was initialized with a target zone and arbitrary anatomical obstacles before running simulations which propagated incremental state changes at every step while adhering to constraints based on the physical system. Finally, we demonstrate the steering capability of the needle through insertion tests into a phantom.


Using Motion Planning to Evaluate Protein Binding Site Accessibility, Diane Uwacu, Everett Yang, Shawna Thomas, Nancy M. Amato, Technical Report, TR18-001, Parasol Laboratory, Department of Computer Science, Texas A&M University, College Station TX 77848, USA, Jul 2018.
Keywords: Ligand Binding, Motion Planning, Workspace Topology
Links : [Manuscript]

BibTex

N/A


Abstract

Despite many efforts and considerable breakthroughs in ligand binding prediction, the best predictors still produce many false positives and a reliable, fully automated prediction framework has yet to be developed. Binding site accessibility is an important feature ignored by methods that classify binding based solely on the energetic or geometric properties of the final bound protein-ligand complex. To evaluate this necessity, we transform the ligand accessibility problem into a robot motion planning problem where the ligand is modeled as a flexible agent whose task is to travel from outside the protein to its binding site. We use Rapidly-exploring Random Graphs coupled with Mean Curve workspace skeletons to quickly and thoroughly explore a protein environment in order to produce valid paths for ligand motion. Path weights reflect the influences of intermolecular forces on the given ligand. Low weight paths are extracted and analyzed for characteristics of accessibility. In this paper, we show that our algorithm provides a mechanism to evaluate binding site accessibility for a ligand.


Sampling-based motion planning with reachable volumes for high-degree-of-freedom manipulators, Troy McMahon, Shawna Thomas, and Nancy M Amato, The International Journal of Robotics Research, Vol: 37, Issue: 7, pp. 779-817, Jun 2018. DOI: 10.1177/0278364918779555
Keywords: Manipulation Planning, Reachability Analysis, Sampling-Based Motion Planning
Links : [Published]

BibTex

@article{mcmahon2018sampling,
title={Sampling-based motion planning with reachable volumes for high-degree-of-freedom manipulators},
author={McMahon, Troy and Thomas, Shawna and Amato, Nancy M},
journal={The International Journal of Robotics Research},
volume={37},
number={7},
pages={779--817},
year={2018},
publisher={SAGE Publications Sage UK: London, England}
}


Abstract

Motion planning for constrained systems is a version of the motion planning problem in which the motion of a robot is limited by constraints. For example, one can require that a humanoid robot such as a PR2 remain upright by constraining its torso to be above its base or require that an object such as a bucket of water remain upright by constraining the vertices of the object to be parallel to the robot’s base. Grasping can be modeled by requiring that the end effectors of the robot be located at specified handle positions. Constraints might require that the robot remain in contact with a surface, or that certain joints of the robot remain in contact with each other (e.g., closed chains). Such problems are particularly difficult because the constraints form a manifold in C-space, and planning must be restricted to this manifold. High-degree-of-freedom motion planning and motion planning for constrained systems has applications in parallel robotics, grasping and manipulation, computational biology and molecular simulations, and animation. We introduce a new concept, reachable volumes, that are a geometric representation of the regions the joints and end effectors of a robot can reach, and use it to define a new planning space called RV-space where all points automatically satisfy a problem’s constraints. Visualizations of reachable volumes can enable operators to see the regions of workspace that different parts of the robot can reach. Samples and paths generated in RV-space naturally conform to constraints, making planning for constrained systems no more difficult than planning for unconstrained systems. Consequently, constrained motion planning problems that were previously difficult or unsolvable become manageable and in many cases trivial. We introduce tools and techniques to extend the state-of-the-art sampling-based motion planning algorithms to RV-space. We define a reachable volumes sampler, a reachable volumes local planner, and a reachable volumes distance metric. We showcase the effectiveness of RV-space by applying these tools to motion planning problems for robots with constraints on the end effectors and/or internal joints of the robot. We show that RV-based planners are more efficient than existing methods, particularly for higher-dimensional problems, solving problems with 1000 or more degrees of freedom for multi-loop and tree-like linkages.


Topological Nearest-Neighbor Filtering for Sampling-Based Planners, Read Sandstrom and Andrew Bregger and Ben Smith and Shawna Thomas and Nancy M. Amato, Proc. IEEE Int. Conf. on Robotics and Automation (ICRA), pp. 3053-3060, Brisbane, Australia, May 2018. DOI: 10.1109/ICRA.2018.8460896
Keywords: Sampling-Based Motion Planning, Workspace Topology
Links : [Published]

BibTex

@inproceedings{sandstrom-icra-2018
, author = "Read Sandstrom and Andrew Bregger and Ben Smith and Shawna Thomas and Nancy M. Amato"
, title = "Topological Nearest-Neighbor Filtering for Sampling-Based Planners"
, booktitle = "Proc. IEEE Int. Conf. Robot. Autom. (ICRA)"
, location = "Brisbane, Australia"
, month = "May"
, year = "2018"
, pages = "3053-3060"
, doi = "10.1109/ICRA.2018.8460896"
}


Abstract

Nearest-neighbor finding is a major bottleneck for sampling-based motion planning algorithms. The cost of finding nearest neighbors grows with the size of the roadmap, leading to significant slowdowns for problems which require many configurations to find a solution. Prior work has investigated relieving this pressure with quicker computational techniques, such as kd-trees or locality-sensitive hashing. In this work, we investigate an alternative direction for expediting this process based on workspace connectivity. We present an algorithm called Topological Nearest-Neighbor Filtering, which employs a workspace decomposition to select a topologically relevant set of candidate neighbor configurations as a pre-processing step for a nearest-neighbor algorithm. We investigate the application of this filter to several varieties of RRT and demonstrate that the filter improves both nearest-neighbor time and overall planning performance.


A General and Flexible Search Framework for Disassembly Planning, Timothy Ebinger, Sascha Kaden, Shawna Thomas, Robert Andre, Nancy M. Amato, and Ulrike Thomas, In Proc. IEEE Int. Conf. on Robotics and Automation (ICRA), pp. 1-8, Brisbane, Australia, May 2018. DOI: 10.1109/ICRA.2018.8460483
Keywords: Assembly, Sampling-Based Motion Planning
Links : [Published]

BibTex

@inproceedings{ebinger2018general,
title={A General and Flexible Search Framework for Disassembly Planning},
author={Ebinger, Timothy and Kaden, Sascha and Thomas, Shawna and Andre, Robert and Amato, Nancy M and Thomas, Ulrike},
booktitle={2018 IEEE International Conference on Robotics and Automation (ICRA)},
pages={1--8},
year={2018},
organization={IEEE}
}


Abstract

We present a new general framework for disassembly sequence planning. This framework is versatile allowing different types of search schemes (exhaustive vs. preemptive), various part separation techniques, and the ability to group parts, or not, into subassemblies to improve the solution efficiency and parallelism. This enables a truly hierarchical approach to disassembly sequence planning. We demonstrate two different search strategies using this framework that can either yield a single solution quickly or provide a spectrum of solutions from which an optimal may be selected. We also develop a method for subassembly identification based on collision information. Our results show improved performance over an iterative motion planning based method for finding a single solution and greater functionality through hierarchical planning and optimal solution search.


A General Region-Based Framework for Collaborative Planning, Jory Denny, Read Sandstrom, Nancy M. Amato, In Proc. Inter. Symp. on Robotics Research (ISRR 2015). Springer Proceedings in Advanced Robotics, Vol: 3, pp. 563-579, Genova, Italy, Jan 2018. DOI: 10.1007/978-3-319-60916-4_32
Keywords: Collaborative Planning, Motion Planning, Workspace Topology
Links : [Published]

BibTex

@Inbook{Denny2018,
author="Denny, Jory
and Sandstr{\"o}m, Read
and Amato, Nancy M.",
editor="Bicchi, Antonio
and Burgard, Wolfram",
title="A General Region-Based Framework for Collaborative Planning",
bookTitle="Robotics Research: Volume 2",
year="2018",
publisher="Springer International Publishing",
address="Cham",
pages="563--579",
abstract="Sampling-based planning is a common method for solving motion planning problems. However, this paradigm falters in difficult scenarios, such as narrow passages. In contrast, humans can frequently identify these challenges and can sometimes propose an approximate solution. A recent method called Region Steering takes advantage of this intuition by allowing a user to define regions in the workspace to weight the search space for probabilistic roadmap planners. In this work, we extend Region Steering into a generalized Region-Based framework that is suitable for any sampling-based planning approach. We explore three variants of our framework for graph-based, tree-based, and hybrid planning methods. We evaluate these variants in simulations as a proof of concept. Our results demonstrate the benefits of our framework in reducing overall planning time.",
isbn="978-3-319-60916-4",
doi="10.1007/978-3-319-60916-4_32",
url="https://doi.org/10.1007/978-3-319-60916-4_32"
}


Abstract

Sampling-based planning is a common method for solving motion planning problems. However, this paradigm falters in difficult scenarios, such as narrow passages. In contrast, humans can frequently identify these challenges and can sometimes propose an approximate solution. A recent method called Region Steering takes advantage of this intuition by allowing a user to define regions in the workspace to weight the search space for probabilistic roadmap planners. In this work, we extend Region Steering into a generalized Region-Based framework that is suitable for any sampling-based planning approach. We explore three variants of our framework for graph-based, tree-based, and hybrid planning methods. We evaluate these variants in simulations as a proof of concept. Our results demonstrate the benefits of our framework in reducing overall planning time.


Bounded Asynchrony and Nested Parallelism for Scalable Graph Processing, Adam Fidel, Nancy M. Amato, Lawrence Rauchwerger, In Proc. Supercomputing (SC), Doctoral Showcase Poster, Denver, CO, Nov 2017.
Keywords: Approximate Algorithms, Parallel Graph Algorithms, STAPL
Links : [Published]

BibTex

N/A


Abstract

Processing large-scale graphs has become a critical component in a variety of fields, from scientific computing to social analytics. The irregular access pattern for graph workloads, coupled with complex graph structures and large data sizes makes efficiently executing parallel graph workloads challenging. In this dissertation, we develop two broad techniques for improving the performance of general parallel graph algorithms: bounded asynchrony and nested parallelism. Increasing asynchrony in a bounded manner allows one to avoid costly global synchronization at scale, while still avoiding the penalty of unbounded asynchrony including redundant work. Using this technique, we scale a BFS workload to 98,304 cores where traditional methods stop scaling. Additionally, asynchrony enables a new family of approximate algorithms for applications tolerant to fixed amounts of error. Representing graph algorithms in a nested parallel manner enables the full use of available parallelism inherent in graph algorithms, while efficiently managing communication through nested sections.


Manipulation Planning with Directed Reachable Volumes, Troy McMahon, Read Sandstrom, Shawna Thomas, Nancy M. Amato, Proc. IEEE/RSJ Int. Conf. on Intelligent Robots and Systems (IROS), pp. 4026-4033, Vancouver, Canada, Sep 2017. DOI: 10.1109/IROS.2017.8206257
Keywords: Manipulation Planning, Reachability Analysis, Sampling-Based Motion Planning
Links : [Published]

BibTex

@inproceedings{mcmahon2017manipulation,
title={Manipulation planning with directed reachable volumes},
author={McMahon, Troy and Sandstr{\"o}m, Read and Thomas, Shawna and Amato, Nancy M},
booktitle={2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},
pages={4026--4033},
year={2017},
organization={IEEE}
}


Abstract

Motion planning for manipulators with rotational joints is challenging because the actuation range for each link is constrained by the placement and orientation of other links. Thus, finding paths that avoid self-collision is non-trivial. However, rotational joints are often used in industrial robots. We develop a reparameterization of the planning problem called directed reachable volumes that provides an explicit representation of the workspace regions that the joints and end effectors can reach given the placement and orientation of other links. This formulation, while similar in spirit to prior reachable volume work, does not rely on the same restrictive assumptions that preclude prior work from handling rotational joints. We provide primitive planning operations that can be used in the context of state-of-the-art motion planning methods. We present experimental validation of directed reachable volumes by demonstrating a simulated pick-and-place scenario using realistic robots with rotational joints.


Fast Approximate Distance Queries in Unweighted Graphs using Bounded Asynchrony, Adam Fidel, Francisco Coral, Colton Riedel, Nancy M. Amato, Lawrence Rauchwerger, Workshop on Languages and Compilers for Parallel Computing (LCPC 2016). Lecture Notes in Computer Science, vol 10136. Springer, Cham., pp. 40-54, Rochester NY, USA, Jan 2017. DOI: 10.1007/978-3-319-52709-3_4
Keywords: Approximate Algorithms, Parallel Graph Algorithms, STAPL
Links : [Published]

BibTex

@inproceedings{
author="Adam Fidel and Francisco Coral Sabido and Colton Riedel and Nancy M. Amato and Lawrence Rauchwerger",
title="Fast Approximate Distance Queries in Unweighted Graphs using Bounded Asynchrony",
year=2017,
booktitle="Languages and Compilers for Parallel Computing (LCPC 2016)",
series="Lecture Notes in Computer Science",
volume="10136",
publisher="Springer, Cham",
note = "DOI: https://doi.org/10.1007/978-3-319-52709-3_4"
}


Abstract

We introduce a new parallel algorithm for approximate breadth-first ordering of an unweighted graph by using bounded asynchrony to parametrically control both the performance and error of the algorithm. This work is based on the k-level asynchronous (KLA) paradigm that trades expensive global synchronizations in the level-synchronous model for local synchronizations in the asynchronous model, which may result in redundant work. Instead of correcting errors introduced by asynchrony and redoing work as in KLA, in this work we control the amount of work that is redone and thus the amount of error allowed, leading to higher performance at the expense of a loss of precision. Results of an implementation of this algorithm are presented on up to 32,768 cores, showing 2.27x improvement over the exact KLA algorithm and 3.8x improvement over the level-synchronous version with minimal error on several graph inputs.


On the theory of user-guided planning, Jory Denny, Jonathan Colbert, Qin Hongsen, Nancy M. Amato, 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 4794-4801, Oct 2016. DOI: 10.1109/IROS.2016.7759704
Keywords: Path Planning, Robotics, Sampling-Based Motion Planning
Links : [Published]

BibTex

@INPROCEEDINGS{7759704,
author={J. {Denny} and J. {Colbert} and H. {Qin} and N. M. {Amato}},
booktitle={2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},
title={On the theory of user-guided planning},
year={2016},
volume={},
number={},
pages={4794-4801},
doi={10.1109/IROS.2016.7759704}}


Abstract

Sampling-based techniques are often employed to solve various complex motion planning problems -the problem of computing a valid path under various robot and/or obstacle constraints. As these methods are random in nature, the probability of their success is directly related to the expansiveness, or openness, of the underlying planning space. However, little is known theoretically in qualifying the conditions under which user (human)-guided approaches improve the efficiency of sampling-based planners. In this paper, we classify and create simplistic models of common user-guided approaches, and we extend the concept of expansiveness to analyze these models to understand both when and how much user-guidance aids sampling-based planners.


Motion Planning using Hierarchical Aggregation of Workspace Obstacles, Mukulika Ghosh, Shawna Thomas, Marco Morales, Sam Rodriguez, and Nancy M. Amato, In Proc. IEEE/RSJ Int. Conf. on Intelligent Robots and Systems (IROS), pp. 5716-5721, Daejeon, Korea, Oct 2016. DOI: 10.1109/IROS.2016.7759841
Keywords: Motion Planning, Workspace Topology
Links : [Published]

BibTex

@inproceedings{ghosh2016motion,
title={Motion planning using hierarchical aggregation of workspace obstacles},
author={Ghosh, Mukulika and Thomas, Shawna and Morales, Marco and Rodriguez, Sam and Amato, Nancy M},
booktitle={2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},
pages={5716--5721},
year={2016},
organization={IEEE}
}


Abstract

Sampling-based motion planning is the state-of-the-art technique for solving challenging motion planning problems in a wide variety of domains. While generally successful, their performance suffers from increasing problem complexity. In many cases, the full problem complexity is not needed for the entire solution. We present a hierarchical aggregation framework that groups and models sets of obstacles based on the currently needed level of detail. The hierarchy enables sampling to be performed using the simplest and most conservative representation of the environment possible in that region. Our results show that this scheme improves planner performance irrespective of the underlying sampling method and input problem. In many cases, improvement is significant, with running times often less than 60% of the original planning time.


Adaptive Local Learning in Sampling Based Motion Planning for Protein Folding, Chinwe Ekenna and Shawna Thomas and Nancy M. Amato, BMC Syst Biol, Special Issue from the 2015 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Vol: 10, Issue: 49, pp. 165-179, Aug 2016. DOI: 10.1186/s12918-016-0297-9
Keywords: Computational Biology, Protein Folding
Links : [Published]

BibTex

@article{ekenna-bmcsb-2016
, author = "Chinwe Ekenna and Shawna Thomas and Nancy M. Amato"
, title = "Adaptive Local Learning in Sampling Based Motion Planning for Protein Folding"
, journal = "BMC Syst Biol, Special Issue from IEEE International Conference on Bioinformatics and Biomedicine (BIBM)"
, volume = "10"
, number = "49"
, pages = "165--179"
, year = "2016"
, month = "August"
, doi = "10.1186/s12918-016-0297-9"
}


Abstract

Background Simulating protein folding motions is an important problem in computational biology. Motion planning algorithms, such as Probabilistic Roadmap Methods, have been successful in modeling the folding landscape. Probabilistic Roadmap Methods and variants contain several phases (i.e., sampling, connection, and path extraction). Most of the time is spent in the connection phase and selecting which variant to employ is a difficult task. Global machine learning has been applied to the connection phase but is inefficient in situations with varying topology, such as those typical of folding landscapes. Results We develop a local learning algorithm that exploits the past performance of methods within the neighborhood of the current connection attempts as a basis for learning. It is sensitive not only to different types of landscapes but also to differing regions in the landscape itself, removing the need to explicitly partition the landscape. We perform experiments on 23 proteins of varying secondary structure makeup with 52–114 residues. We compare the success rate when using our methods and other methods. We demonstrate a clear need for learning (i.e., only learning methods were able to validate against all available experimental data) and show that local learning is superior to global learning producing, in many cases, significantly higher quality results than the other methods. Conclusions We present an algorithm that uses local learning to select appropriate connection methods in the context of roadmap construction for protein folding. Our method removes the burden of deciding which method to use, leverages the strengths of the individual input methods, and it is extendable to include other future connection methods.


The Impact of Approximate Methods on Local Learning in Motion Planning, Diane Uwacu, Chinwe Ekenna, Shawna Thomas, Nancy Amato, 1st International Workshop on Robot Learning and Planning (RLP 2016), in conjunction with RSS 2016, pp. 38-44, Ann Arbor, MI, Jun 2016.
Keywords: Machine Learning, Sampling-Based Motion Planning
Links : [ArXiv] [Manuscript]

BibTex

@inproceedings{uwacu-wrlp-2016
, author = "Diane Uwacu and Chinwe Ekenna and Shawna Thomas and Nancy M. Amato"
, title = "The Impact of Approximate Methods on Local Learning in Motion Planning"
, booktitle = "1st International Workshop on Robot Learning and Planning (RLP 2016), in conjunction with RSS 2016"
, month = "June"
, year = "2016"
, note = "http://chitsazlab.org/robotics/rlp2016/RLP_2016_paper_11.html"
}


Abstract

Machine learning methods have been applied to many motion planning algorithms including probabilistic roadmap methods (PRM). There are many variants of these methods and choosing the best one every time is hard and depends on local properties of the environment. A successful learning approach has been developed to offset this issue. This learning approach was applied to PRMs to help decide intelligently what method to utilize in dynamically created local regions of the environment or task space. It used exact neighbor finding approaches and removed the need to partition environments to get improved results. In this work we make further advances by introducing approximate neighbor finder methods. It has been established that approximate neighbor finding methods are faster than exact methods, still work well in connecting nodes to edges in PRMs, and that connection is robust to noise. We study what happens when noise is introduced into learning by using approximate methods instead of already studied exact methods. We show that the impact of noise on learning depends on how much learning needs to take place given the topology of the environment. Our results demonstrate a correlation between heterogeneity and the need for learning over a local region.


MPMD Framework for Offloading Load Balance Computation, Olga Pearce, Todd Gamblin, Bronis R. De Supinski, Martin Schulz, Nancy M. Amato, 2016 IEEE International Parallel and Distributed Processing Symposium, pp. 943-952, May 2016. DOI: 10.1109/IPDPS.2016.16
Keywords: Parallel Graph Algorithms
Links : [Published]

BibTex

@inproceedings{DBLP:conf/ipps/PearceGSSA16,
author = {Olga Pearce and
Todd Gamblin and
Bronis R. de Supinski and
Martin Schulz and
Nancy M. Amato},
title = {{MPMD} Framework for Offloading Load Balance Computation},
booktitle = {2016 {IEEE} International Parallel and Distributed Processing Symposium,
{IPDPS} 2016, Chicago, IL, USA, May 23-27, 2016},
pages = {943--952},
publisher = {{IEEE} Computer Society},
year = {2016},
url = {https://doi.org/10.1109/IPDPS.2016.16},
doi = {10.1109/IPDPS.2016.16},
timestamp = {Wed, 16 Oct 2019 14:14:51 +0200},
biburl = {https://dblp.org/rec/conf/ipps/PearceGSSA16.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}


Abstract

In many parallel scientific simulations, work is assigned to processors by decomposing a spatial domain consisting of mesh cells, particles, or other elements. When work per element changes, simulations can use dynamic load balance algorithms to distribute work to processors evenly. Typical SPMD simulations wait while a load balance algorithm runs on all processors, but this algorithm can itself become a bottleneck. We propose a novel approach based on two key observations: (1) application state typically changes slowly in SPMD physics simulations, so work assignments computed in the past still produce good load balance in the future, (2) we can decouple the load balance algorithm so that it runs concurrently with the application and more efficiently on a smaller number of processors. We then apply the work assignment "late", once it has been computed. We call this approach lazy load balancing. In this paper, we show that the rate of change in work distribution is slow for a Barnes-Hut benchmark and for ParaDiS, a dislocation dynamics simulation. We implement an MPMD framework to exploit this property to save resources by running a load balancing algorithm at higher parallel efficiency on a smaller number of processors. Using our framework, we explore the trade-offs of lazy load balancing and demonstrate performance improvements of up to 46%.


Asynchronous Nested Parallelism for Dynamic Applications in Distributed Memory, Ioannis Papadopoulos, Nathan Thomas, Adam Fidel, Dielli Hoxha, Nancy M. Amato, Lawrence Rauchwerger, In Wkshp. on Lang. and Comp. for Par. Comp. (LCPC), pp. 106-121, Chapel Hill, NC, Feb 2016. DOI: 10.1007/978-3-319-29778-1_7
Keywords: Runtime Systems, STAPL
Links : [Published]

BibTex

@InProceedings{10.1007/978-3-319-29778-1_7,
author="Papadopoulos, Ioannis
and Thomas, Nathan
and Fidel, Adam
and Hoxha, Dielli
and Amato, Nancy M.
and Rauchwerger, Lawrence",
editor="Shen, Xipeng
and Mueller, Frank
and Tuck, James",
title="Asynchronous Nested Parallelism for Dynamic Applications in Distributed Memory",
booktitle="Languages and Compilers for Parallel Computing",
year="2016",
publisher="Springer International Publishing",
address="Cham",
pages="106--121",
abstract="Nested parallelism is of increasing interest for both expressivity and performance. Many problems are naturally expressed with this divide-and-conquer software design approach. In addition, programmers with target architecture knowledge employ nested parallelism for performance, imposing a hierarchy in the application to increase locality and resource utilization, often at the cost of implementation complexity.",
isbn="978-3-319-29778-1"
}


Abstract

Nested parallelism is of increasing interest for both expressivity and performance. Many problems are naturally expressed with this divide-and-conquer software design approach. In addition, programmers with target architecture knowledge employ nested parallelism for performance, imposing a hierarchy in the application to increase locality and resource utilization, often at the cost of implementation complexity. While dynamic applications are a natural fit for the approach, support for nested parallelism in distributed systems is generally limited to well-structured applications engineered with distinct phases of intra-node computation and inter-node communication. This model makes expressing irregular applications difficult and also hurts performance by introducing unnecessary latency and synchronizations. In this paper we describe an approach to asynchronous nested parallelism which provides uniform treatment of nested computation across distributed memory. This approach allows efficient execution while supporting dynamic applications which cannot be mapped onto the machine in the rigid manner of regular applications. We use several graph algorithms as examples to demonstrate our library’s expressivity, flexibility, and performance.


Planning Motions for Shape-Memory Alloy Sheets, Mukulika Ghosh, Daniel Tomkins, Jory Denny, Sam Rodriguez, Marco Morales Aguirre, Nancy M. Amato, Origami, Vol: 6, Issue: 6, pp. 501-511, Dec 2015. DOI: 10.1090/MBK/095.2/13
Keywords: Computational Geometry, Motion Planning
Links : [Published]

BibTex

@inproceedings{Ghosh2015PlanningMF,
title={Planning motions for shape-memory alloy sheets},
author={Mukulika Ghosh and D. Tomkins and J. Denny and S. Rodr{\'i}guez and M. Morales and N. Amato},
year={2015}
}


Abstract

Shape Memory Alloys (SMAs) are smart materials that can remember predefined shapes. A deformed SMA can transition to a trained shape by applying temperature changes to portions of the material. This reconfigurable property allows SMAs to be used in aeronautics, medicine, and other fields where dynamic re-engineering or actuation of components is required. In this work, we plan the motion of an SMA robot modeled as inflexible regions connected by flexible joints. In this work, we adapt an existing state-of-the-art motion planning algorithm to model the folding of an SMA robot from an unfolded flat state to a folded shape under feasibility constraints such as collision free motion and gravitational stability. Our results validate our model and algorithm by folding interesting 3D shapes using gravitationally stable motions, show flexibility in modeling various planning problems and significantly improved motions in comparable time to not using stability constraints.


An Algorithmic Approach to Communication Reduction in Parallel Graph Algorithms (Conference Best Paper Finalist), Harshvardhan, Adam Fidel, Nancy M. Amato, Lawrence Rauchwerger, In Proc. IEEE Int.Conf. on Parallel Architectures and Compilation Techniques (PACT), pp. 201-212, San Francisco, CA, Oct 2015. DOI: 10.1109/PACT.2015.34
Keywords: Parallel Graph Algorithms, STAPL
Links : [Published]

BibTex

@INPROCEEDINGS{7429306,
author={ {Harshvardhan} and A. {Fidel} and N. M. {Amato} and L. {Rauchwerger}},
booktitle={2015 International Conference on Parallel Architecture and Compilation (PACT)},
title={An Algorithmic Approach to Communication Reduction in Parallel Graph Algorithms},
year={2015},
volume={},
number={},
pages={201-212},}


Abstract

Graph algorithms on distributed-memory systems typically perform heavy communication, often limiting their scalability and performance. This work presents an approach to transparently (without programmer intervention) allow fine-grained graph algorithms to utilize algorithmic communication reduction optimizations. In many graph algorithms, the same information is communicated by a vertex to its neighbors, which we coin algorithmic redundancy. Our approach exploits algorithmic redundancy to reduce communication between vertices located on different processing elements. We employ algorithm-aware coarsening of messages sent during vertex visitation, reducing both the number of messages and the absolute amount of communication in the system. To achieve this, the system structure is represented by a hierarchical graph, facilitating communication optimizations that can take into consideration the machine's memory hierarchy. We also present an optimization for small-world scale-free graphs wherein hub vertices (i.e., vertices of very large degree) are represented in a similar hierarchical manner, which is exploited to increase parallelism and reduce communication. Finally, we present a framework that transparently allows fine-grained graph algorithms to utilize our hierarchical approach without programmer intervention, while improving scalability and performance. Experimental results of our proposed approach on 131,000+ cores show improvements of up to a factor of 8 times over the non-hierarchical version for various graph mining and graph analytics algorithms.


Improved Roadmap Connection via Local Learning for Sampling Based Planners, Chinwe Ekenna, Diane Uwacu, Shawna Thomas, Nancy Amato, Proc. IEEE/RSJ Int. Conf. Intel. Rob. Syst. (IROS), pp. 3227-3234, Hamburg, Germany, Oct 2015. DOI: 10.1109/IROS.2015.7353825
Keywords: Machine Learning, Motion Planning, Sampling-Based Motion Planning
Links : [Published]

BibTex

@INPROCEEDINGS{7353825, author={C. {Ekenna} and D. {Uwacu} and S. {Thomas} and N. M. {Amato}}, booktitle={2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)}, title={Improved roadmap connection via local learning for sampling based planners}, year={2015}, volume={}, number={}, pages={3227-3234},}


Abstract

Probabilistic Roadmap Methods (PRMs) solve the motion planing problem by constructing a roadmap (or graph) that models the motion space when feasible local motions exist. PRMs and variants contain several phases during roadmap generation i.e., sampling, connection, and query. Some work has been done to apply machine learning to the connection phase to decide which variant to employ, but it uses a global learning approach that is inefficient in heterogeneous situations. We present an algorithm that instead uses local learning: it only considers the performance history in the vicinity of the current connection attempt and uses this information to select good candidates for connection. It thus removes any need to explicitly partition the environment which is burdensome and typically difficult to do. Our results show that our method learns and adapts in heterogeneous environments, including a KUKA youBot with a fixed and mobile base. It finds solution paths faster for single and multi-query scenarios and builds roadmaps with better coverage and connectivity given a fixed amount of time in a wide variety of input problems. In all cases, our method outperforms the previous adaptive connection method and is comparable or better than the best individual method.


Studying Learning Techniques in Different Phases of PRM Construction, Chinwe Ekenna, Diane Uwacu, Shawna Thomas, Nancy Amato, In Machine Learning in Planning and Control of Robot Motion Workshop (IROS-MLPC), Hamburg, Germany, Oct 2015. DOI: 10.1109/IROS.2015.7353825
Keywords: Machine Learning, Sampling-Based Motion Planning
Links : [Published]

BibTex

no bib for now.


Abstract

Probabilistic Roadmap Methods (PRMs) solve the motion planning problem in two phases by sampling free configurations and connecting them together to build a map that is used to find a valid path. Existing algorithms are highly sensitive to the topology of the problem, and their efficiency depends on applying them to a compatible problem. Reinforcement learning has been applied to motion planning and rewards the action performed by planners during either sampling or connection, but not both. Previous work computed a global reward and action scheme, which saw a setback when heterogeneous environments were concerned. Local learning (connection) was recently introduced to offset this weakness identified during global learning, and there was some improvement in planner performance. These different learning schemes (global and local) have shown strengths and weaknesses individually. In this paper, we investigate local learning for sampling. We study what type of learning to apply when, and how the two phases of PRM roadmap construction interact, which has not been investigated before. We show the performance using each scheme on a KUKAyouBot, an 8 degree of freedom robot, and analyze what happens when they are all combined during roadmap construction


Decoy Database Improvement for Protein Folding, Hsin-Yi (Cindy) Yeh, Aaron Lindsey, Chih-Peng Wu, Shawna Thomas, and Nancy M. Amato, Journal of Computational Biology, Vol: 22, Issue: 9, pp. 823-836, Sep 2015. DOI: 10.1089/cmb.2015.0116
Keywords: Computational Biology, Protein Folding
Links : [Published]

BibTex

@article{yeh-jcb-2015
, author = "Hsin-Yi Yeh and Aaron Lindsey and Chih-Peng Wu and Shawna Thomas and Nancy M. Amato"
, title ="Decoy Database Improvement for Protein Folding"
, journal = "Journal of Computational Biology"
, volume = "22"
, number = "9"
, year = "2015"
, pages = "823-836"
, doi = "10.1089/cmb.2015.0116"
}


Abstract

Predicting protein structures and simulating protein folding are two of the most important problems in computational biology today. Simulation methods rely on a scoring function to distinguish the native structure (the most energetically stable) from non-native structures. Decoy databases are collections of non-native structures used to test and verify these functions. We present a method to evaluate and improve the quality of decoy databases by adding novel structures and removing redundant structures. We test our approach on 20 different decoy databases of varying size and type and show significant improvement across a variety of metrics. We also test our improved databases on two popular modern scoring functions and show that for most cases they contain a greater or equal number of native-like structures than the original databases, thereby producing a more rigorous database for testing scoring functions.


Composing Algorithmic Skeletons to Express High-Performance Scientific Applications, Mani Zandifar, Mustafa Abdujabbar, Alireza Majidi, David Keyes, Nancy M. Amato, Lawrence Rauchwerger, In Proc. ACM Int. Conf. Supercomputing (ICS), pp. 415-424, Newport Beach, CA, USA. Conference Best Paper Award., Jun 2015. DOI: 10.1145/2751205.2751241
Keywords: Algorithmic Skeletons, Data Flow, STAPL
Links : [Published]

BibTex

@inproceedings{10.1145/2751205.2751241,
author = {Zandifar, Mani and Abdul Jabbar, Mustafa and Majidi, Alireza and Keyes, David and Amato, Nancy M. and Rauchwerger, Lawrence},
title = {Composing Algorithmic Skeletons to Express High-Performance Scientific Applications},
year = {2015},
isbn = {9781450335591},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2751205.2751241},
doi = {10.1145/2751205.2751241},
abstract = {Algorithmic skeletons are high-level representations for parallel programs that hide the underlying parallelism details from program specification. These skeletons are defined in terms of higher-order functions that can be composed to build larger programs. Many skeleton frameworks support efficient implementations for stand-alone skeletons such as map, reduce, and zip for both shared-memory systems and small clusters. However, in these frameworks, expressing complex skeletons that are constructed through composition of fundamental skeletons either requires complete reimplementation or suffers from limited scalability due to required global synchronization. In the STAPL Skeleton Framework, we represent skeletons as parametric data flow graphs and describe composition of skeletons by point-to-point dependencies of their data flow graph representations. As a result, we eliminate the need for reimplementation and global synchronizations in composed skeletons. In this work, we describe the process of translating skeleton-based programs to data flow graphs and define rules for skeleton composition. To show the expressivity and ease of use of our framework, we show skeleton-based representations of the NAS EP, IS, and FT benchmarks. To show reusability and applicability of our framework on real-world applications we show an N-Body application using the FMM (Fast Multipole Method) hierarchical algorithm. Our results show that expressivity can be achieved without loss of performance even in complex real-world applications.},
booktitle = {Proceedings of the 29th ACM on International Conference on Supercomputing},
pages = {415–424},
numpages = {10},
keywords = {high-performance computing, data flow programming, distributed systems, algorithmic skeletons, patterns},
location = {Newport Beach, California, USA},
series = {ICS '15}
}


Abstract

Algorithmic skeletons are high-level representations for parallel programs that hide the underlying parallelism details from program specification. These skeletons are defined in terms of higher-order functions that can be composed to build larger programs. Many skeleton frameworks support efficient implementations for stand-alone skeletons such as map, reduce, and zip for both shared-memory systems and small clusters. However, in these frameworks, expressing complex skeletons that are constructed through composition of fundamental skeletons either requires complete reimplementation or suffers from limited scalability due to required global synchronization. In the STAPL Skeleton Framework, we represent skeletons as parametric data flow graphs and describe composition of skeletons by point-to-point dependencies of their data flow graph representations. As a result, we eliminate the need for reimplementation and global synchronizations in composed skeletons. In this work, we describe the process of translating skeleton-based programs to data flow graphs and define rules for skeleton composition. To show the expressivity and ease of use of our framework, we show skeleton-based representations of the NAS EP, IS, and FT benchmarks. To show reusability and applicability of our framework on real-world applications we show an N-Body application using the FMM (Fast Multipole Method) hierarchical algorithm. Our results show that expressivity can be achieved without loss of performance even in complex real-world applications.


STAPL-RTS: An Application Driven Runtime System, Ioannis Papadopoulos, Nathan Thomas, Adam Fidel, Nancy M. Amato, Lawrence Rauchwerger, In Proc. ACM Int. Conf. Supercomputing (ICS), pp. 425-434, Newport Beach, CA, USA, Jun 2015. DOI: 10.1145/2751205.2751233
Keywords: Communication Library, Runtime Systems, STAPL
Links : [Published]

BibTex

@inproceedings{10.1145/2751205.2751233,
author = {Papadopoulos, Ioannis and Thomas, Nathan and Fidel, Adam and Amato, Nancy M. and Rauchwerger, Lawrence},
title = {STAPL-RTS: An Application Driven Runtime System},
year = {2015},
isbn = {9781450335591},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2751205.2751233},
doi = {10.1145/2751205.2751233},
abstract = {Modern HPC systems are growing in complexity, as they move towards deeper memory hierarchies and increasing use of computational heterogeneity via GPUs or other accelerators. When developing applications for these platforms, programmers are faced with two bad choices. On one hand, they can explicitly manage all machine resources, writing programs decorated with low level primitives from multiple APIs (e.g. Hybrid MPI / OpenMP applications). Though seemingly necessary for efficient execution, it is an inherently non-scalable way to write software. Without a separation of concerns, only small programs written by expert developers actually achieve this efficiency. Furthermore, the implementations are rigid, difficult to extend, and not portable. Alternatively, users can adopt higher level programming environments to abstract away these concerns. Extensibility and portability, however, often come at the cost of lost performance. The mapping of a user's application onto the system now occurs without the contextual information that was immediately available in the more coupled approach.In this paper, we describe a framework for the transfer of high level, application semantic knowledge into lower levels of the software stack at an appropriate level of abstraction. Using the STAPL library, we demonstrate how this information guides important decisions in the runtime system (STAPL-RTS), such as multi-protocol communication coordination and request aggregation. Through examples, we show how generic programming idioms already known to C++ programmers are used to annotate calls and increase performance.},
booktitle = {Proceedings of the 29th ACM on International Conference on Supercomputing},
pages = {425–434},
numpages = {10},
keywords = {data flow, shared memory, remote method invocation, distributed memory, parallel programming, runtime systems, application driven optimizations},
location = {Newport Beach, California, USA},
series = {ICS '15}
}


Abstract

Modern HPC systems are growing in complexity, as they move towards deeper memory hierarchies and increasing use of computational heterogeneity via GPUs or other accelerators. When developing applications for these platforms, programmers are faced with two bad choices. On one hand, they can explicitly manage all machine resources, writing programs decorated with low level primitives from multiple APIs (e.g. Hybrid MPI / OpenMP applications). Though seemingly necessary for efficient execution, it is an inherently non-scalable way to write software. Without a separation of concerns, only small programs written by expert developers actually achieve this efficiency. Furthermore, the implementations are rigid, difficult to extend, and not portable. Alternatively, users can adopt higher level programming environments to abstract away these concerns. Extensibility and portability, however, often come at the cost of lost performance. The mapping of a user's application onto the system now occurs without the contextual information that was immediately available in the more coupled approach. In this paper, we describe a framework for the transfer of high level, application semantic knowledge into lower levels of the software stack at an appropriate level of abstraction. Using the STAPL library, we demonstrate how this information guides important decisions in the runtime system (STAPL-RTS), such as multi-protocol communication coordination and request aggregation. Through examples, we show how generic programming idioms already known to C++ programmers are used to annotate calls and increase performance.


A Hybrid Approach To Processing Big Data Graphs on Memory-Restricted Systems, Harshvardhan, Brandon West, Adam Fidel, Nancy M. Amato, Lawrence Rauchwerger, In Proc. Int. Par. and Dist. Proc. Symp. (IPDPS), pp. 799-808, Hyderabad, India, May 2015. DOI: 10.1109/IPDPS.2015.28
Keywords: Big Data, Parallel Graph Algorithms, STAPL
Links : [Published]

BibTex

@INPROCEEDINGS{7161566, author={ {Harshvardhan} and B. {West} and A. {Fidel} and N. M. {Amato} and L. {Rauchwerger}}, booktitle={2015 IEEE International Parallel and Distributed Processing Symposium}, title={A Hybrid Approach to Processing Big Data Graphs on Memory-Restricted Systems}, year={2015}, volume={}, number={}, pages={799-808},}


Abstract

With the advent of big-data, processing large graphs quickly has become increasingly important. Most existing approaches either utilize in-memory processing techniques that can only process graphs that fit completely in RAM, or disk-based techniques that sacrifice performance. In this work, we propose a novel RAM-Disk hybrid approach to graph processing that can scale well from a single shared-memory node to large distributed-memory systems. It works by partitioning the graph into sub graphs that fit in RAM and uses a paging-like technique to load sub graphs. We show that without modifying the algorithms, this approach can scale from small memory-constrained systems (such as tablets) to large-scale distributed machines with 16, 000+ cores.


Reachable Volume RRT, Troy McMahon, Shawna L. Thomas, Nancy M. Amato, 2015 IEEE International Conference on Robotics and Automation (ICRA), pp. 2977-2984, Seattle, Washington, USA, May 2015. DOI: 10.1109/ICRA.2015.7139607
Keywords: Manipulation Planning, Reachability Analysis, Sampling-Based Motion Planning
Links : [Published]

BibTex

@INPROCEEDINGS{7139607, author={T. {McMahon} and S. {Thomas} and N. M. {Amato}}, booktitle={2015 IEEE International Conference on Robotics and Automation (ICRA)}, title={Reachable volume RRT}, year={2015}, volume={}, number={}, pages={2977-2984}, doi={10.1109/ICRA.2015.7139607}}


Abstract

Reachable volumes are a new technique that allows one to efficiently restrict sampling to feasible/reachable regions of the planning space even for high degree of freedom and highly constrained problems. However, they have so far only been applied to graph-based sampling-based planners. In this paper we develop the methodology to apply reachable volumes to tree-based planners such as Rapidly-Exploring Random Trees (RRTs). In particular, we propose a reachable volume RRT called RVRRT that can solve high degree of freedom problems and problems with constraints. To do so, we develop a reachable volume stepping function, a reachable volume expand function, and a distance metric based on these operations. We also present a reachable volume local planner to ensure that local paths satisfy constraints for methods such as PRMs. We show experimentally that RVRRTs can solve constrained problems with as many as 64 degrees of freedom and unconstrained problems with as many as 134 degrees of freedom. RVRRTs can solve problems more efficiently than existing methods, requiring fewer nodes and collision detection calls. We also show that it is capable of solving difficult problems that existing methods cannot.


A Hierarchical Approach to Reducing Communication in Parallel Graph Algorithms, Harshvardhan, Nancy M. Amato, Lawrence Rauchwerger, In Proc. ACM SIGPLAN Symp. Prin. Prac. Par. Prog. (PPOPP), pp. 285-286, San Francisco, CA, USA, Feb 2015. DOI: 10.1145/2688500.2700994
Keywords: Parallel Graph Algorithms, STAPL
Links : [Published]

BibTex

@inproceedings{10.1145/2688500.2700994,
author = {Harshvardhan and Amato, Nancy M. and Rauchwerger, Lawrence},
title = {A Hierarchical Approach to Reducing Communication in Parallel Graph Algorithms},
year = {2015},
isbn = {9781450332057},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2688500.2700994},
doi = {10.1145/2688500.2700994},
abstract = { Large-scale graph computing has become critical due to the ever-increasing size of data. However, distributed graph computations are limited in their scalability and performance due to the heavy communication inherent in such computations. This is exacerbated in scale-free networks, such as social and web graphs, which contain hub vertices that have large degrees and therefore send a large number of messages over the network. Furthermore, many graph algorithms and computations send the same data to each of the neighbors of a vertex. Our proposed approach recognizes this, and reduces communication performed by the algorithm without change to user-code, through a hierarchical machine model imposed upon the input graph. The hierarchical model takes advantage of locale information of the neighboring vertices to reduce communication, both in message volume and total number of bytes sent. It is also able to better exploit the machine hierarchy to further reduce the communication costs, by aggregating traffic between different levels of the machine hierarchy. Results of an implementation in the STAPL GL shows improved scalability and performance over the traditional level-synchronous approach, with 2.5\texttimes{}-8\texttimes{} improvement for a variety of graph algorithms at 12,000+ cores. },
booktitle = {Proceedings of the 20th ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming},
pages = {285–286},
numpages = {2},
keywords = {Distributed Computing, Graph Analytics, Parallel Graph Processing, Big Data},
location = {San Francisco, CA, USA},
series = {PPoPP 2015}
}

@article{10.1145/2858788.2700994,
author = {Harshvardhan and Amato, Nancy M. and Rauchwerger, Lawrence},
title = {A Hierarchical Approach to Reducing Communication in Parallel Graph Algorithms},
year = {2015},
issue_date = {August 2015},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
volume = {50},
number = {8},
issn = {0362-1340},
url = {https://doi.org/10.1145/2858788.2700994},
doi = {10.1145/2858788.2700994},
abstract = { Large-scale graph computing has become critical due to the ever-increasing size of data. However, distributed graph computations are limited in their scalability and performance due to the heavy communication inherent in such computations. This is exacerbated in scale-free networks, such as social and web graphs, which contain hub vertices that have large degrees and therefore send a large number of messages over the network. Furthermore, many graph algorithms and computations send the same data to each of the neighbors of a vertex. Our proposed approach recognizes this, and reduces communication performed by the algorithm without change to user-code, through a hierarchical machine model imposed upon the input graph. The hierarchical model takes advantage of locale information of the neighboring vertices to reduce communication, both in message volume and total number of bytes sent. It is also able to better exploit the machine hierarchy to further reduce the communication costs, by aggregating traffic between different levels of the machine hierarchy. Results of an implementation in the STAPL GL shows improved scalability and performance over the traditional level-synchronous approach, with 2.5\texttimes{}-8\texttimes{} improvement for a variety of graph algorithms at 12,000+ cores. },
journal = {SIGPLAN Not.},
month = jan,
pages = {285–286},
numpages = {2},
keywords = {Distributed Computing, Parallel Graph Processing, Big Data, Graph Analytics}
}


Abstract

Large-scale graph computing has become critical due to the ever-increasing size of data. However, distributed graph computations are limited in their scalability and performance due to the heavy communication inherent in such computations. This is exacerbated in scale-free networks, such as social and web graphs, which contain hub vertices that have large degrees and therefore send a large number of messages over the network. Furthermore, many graph algorithms and computations send the same data to each of the neighbors of a vertex. Our proposed approach recognizes this, and reduces communication performed by the algorithm without change to user-code, through a hierarchical machine model imposed upon the input graph. The hierarchical model takes advantage of locale information of the neighboring vertices to reduce communication, both in message volume and total number of bytes sent. It is also able to better exploit the machine hierarchy to further reduce the communication costs, by aggregating traffic between different levels of the machine hierarchy. Results of an implementation in the STAPL GL shows improved scalability and performance over the traditional level-synchronous approach, with 2.5×-8× improvement for a variety of graph algorithms at 12,000+ cores.


Efficient, Reachability-based, Parallel Algorithms for Finding Strongly Connected Components, Daniel Tomkins, Timmie Smith, Nancy M. Amato, Lawrence Rauchwerger, Technical Report, TR15-002, Parasol Laboratory, Department of Computer Science, Texas A&M University, College Station, TX 77843-3112, Jan 2015.
Keywords: Parallel Graph Algorithms, STAPL
Links : [Published]

BibTex

N/A


Abstract

Large, complex graphs are increasingly used to represent unstructured data in scientific applications. Applications such as discrete ordinates methods for radiation transport require these graphs to be directed with all cycles eliminated, where cycles are identified by finding the graph.s strongly connected components (SCCs). Deterministic parallel algorithms identify SCCs in polylogarithmic time, but the work of the algorithms on sparse graphs exceeds the linear sequential complexity bound. Randomized algorithms based on reachability — the ability to get from one vertex to another along a directed path — are generally favorable for sparse graphs, but either do not perform well on all graphs or introduce substantial overheads. This paper introduces two new algorithms, SCCMulti and SCCMulti-PriorityAware, which perform well on all graphs without significant overhead. We also provide a characterization that compares reachabilitybased SCC algorithms. Experimental results demonstrate scalability for both algorithms to 393,216 of cores on several types of graphs.


Faster Parallel Traversal of Scale Free Graphs at Extreme Scale with Vertex Delegates, Roger Pearce, Maya Gokhale, Nancy M. Amato, International Conference for High Performance Computing, Networking, Storage and Analysis (SC), pp. 549-559, New Orleans, Louisiana, USA, Nov 2014. DOI: 10.1109/SC.2014.50
Keywords: Parallel Graph Algorithms
Links : [Published]

BibTex

@inproceedings{DBLP:conf/sc/PearceGA14,
author = {Roger A. Pearce and
Maya B. Gokhale and
Nancy M. Amato},
editor = {Trish Damkroger and
Jack J. Dongarra},
title = {Faster Parallel Traversal of Scale Free Graphs at Extreme Scale with
Vertex Delegates},
booktitle = {International Conference for High Performance Computing, Networking,
Storage and Analysis, {SC} 2014, New Orleans, LA, USA, November 16-21,
2014},
pages = {549--559},
publisher = {{IEEE} Computer Society},
year = {2014},
url = {https://doi.org/10.1109/SC.2014.50},
doi = {10.1109/SC.2014.50},
timestamp = {Mon, 20 Apr 2020 17:14:43 +0200},
biburl = {https://dblp.org/rec/conf/sc/PearceGA14.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}


Abstract

At extreme scale, irregularities in the structure of scale-free graphs such as social network graphs limit our ability to analyze these important and growing datasets. A key challenge is the presence of high-degree vertices (hubs), that leads to parallel workload and storage imbalances. The imbalances occur because existing partitioning techniques are not able to effectively partition high-degree vertices. We present techniques to distribute storage, computation, and communication of hubs for extreme scale graphs in distributed memory supercomputers. To balance the hub processing workload, we distribute hub data structures and related computation among a set of delegates. The delegates coordinate using highly optimized, yet portable, asynchronous broadcast and reduction operations. We demonstrate scalability of our new algorithmic technique using Breadth-First Search (BFS), Single Source Shortest Path (SSSP), K-Core Decomposition, and Page-Rank on synthetically generated scale-free graphs. Our results show excellent scalability on large scale-free graphs up to 131K cores of the IBM BG/P, and outperform the best known Graph500 performance on BG/P Intrepid by 15%.


Validation of Full-Domain Massively Parallel Transport Sweep Algorithms, eresa S. Bailey, W. Daryl Hawkins, Marvin L. Adams, Peter N. Brown, Adam J. Kunen, Michael P. Adams, Timmie Smith, Nancy M. Amato, and Lawrence Rauchwerger, Transactions of the American Nuclear Society, Vol: 111, Issue: 1, pp. 699-702, Anaheim, CA, USA, Nov 2014.
Keywords: Parallel Algorithms, Scientific Computing, STAPL
Links : [Published]

BibTex

@article{bailey--tans-2014
, author = "Teresa S. Bailey and W. Daryl Hawkins and Marvin L. Adams and Peter N. Brown and Adam J. Kunen and Michael P. Adams and Timmie Smith and Nancy M. Amato and Lawrence Rauchwerger"
, title = "Validation of Full-Domain Massively Parallel Transport Sweep Algorithms"
, journal = "Trans. American Nuclear Society"
, volume = "111"
, month = "November"
, year = "2014"
, pages = "699-702"
, note = "2014 ANS Annual Winter Meeting; Nov 9-13, 2014, Anaheim, CA, USA"
}


Abstract

The scalability of sweep algorithms has been a somewhat disputed topic recently. Some papers have predicted that these algorithms will scale if applied appropriately, while others have claimed that sweep algorithms have limited scalability. We compare the parallel performance models for sweep algorithms to actual computational performance using two distinctly different discrete ordinates transport codes. We have run both codes beyond 1Million MPI ranks on Lawrence Livermore National Laboratory’s (LLNL) BGQ machines (SEQUOIA and VULCAN).


The STAPL Skeleton Framework, Mani Zandifar, Nathan Thomas, Nancy M. Amato, Lawrence Rauchwerger, In Wkshp. on Lang. and Comp. for Par. Comp. (LCPC), pp. 176-190, Hillsboro, OR, USA, Sep 2014. DOI: 10.1007/978-3-319-17473-0_12
Keywords: Data Flow, Parallel Libraries, STAPL
Links : [Published]

BibTex

@InProceedings{10.1007/978-3-319-17473-0_12,
author="Zandifar, Mani
and Thomas, Nathan
and Amato, Nancy M.
and Rauchwerger, Lawrence",
editor="Brodman, James
and Tu, Peng",
title="The stapl Skeleton Framework",
booktitle="Languages and Compilers for Parallel Computing",
year="2015",
publisher="Springer International Publishing",
address="Cham",
pages="176--190",
abstract="This paper describes the stapl Skeleton Framework, a high-level skeletal approach for parallel programming. This framework abstracts the underlying details of data distribution and parallelism from programmers and enables them to express parallel programs as a composition of existing elementary skeletons such as map, map-reduce, scan, zip, butterfly, allreduce, alltoall and user-defined custom skeletons.",
isbn="978-3-319-17473-0"
}


Abstract

This paper describes the stapl Skeleton Framework, a high-level skeletal approach for parallel programming. This framework abstracts the underlying details of data distribution and parallelism from programmers and enables them to express parallel programs as a composition of existing elementary skeletons such as map, map-reduce, scan, zip, butterfly, allreduce, alltoall and user-defined custom skeletons. Skeletons in this framework are defined as parametric data flow graphs, and their compositions are defined in terms of data flow graph compositions. Defining the composition in this manner allows dependencies between skeletons to be defined in terms of point-to-point dependencies, avoiding unnecessary global synchronizations. To show the ease of composability and expressivity, we implemented the NAS Integer Sort (IS) and Embarrassingly Parallel (EP) benchmarks using skeletons and demonstrate comparable performance to the hand-optimized reference implementations. To demonstrate scalable performance, we show a transformation which enables applications written in terms of skeletons to run on more than 100,000 cores.


The anatomy of a distributed motion planning roadmap, Sam Ade Jacobs, Nancy M. Amato, 2014 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 3019-3026, Chicago, Illinois, USA, Sep 2014. DOI: 10.1109/IROS.2014.6942979
Keywords: Motion Planning, Parallel Planning, Parallel Processing
Links : [Manuscript]

BibTex

@INPROCEEDINGS{6942979, author={S. A. {Jacobs} and N. M. {Amato}}, booktitle={2014 IEEE/RSJ International Conference on Intelligent Robots and Systems}, title={The anatomy of a distributed motion planning roadmap}, year={2014}, volume={}, number={}, pages={3019-3026}, doi={10.1109/IROS.2014.6942979}}


Abstract

In this paper, we evaluate and compare the quality and structure of roadmaps constructed from parallelizing sampling-based motion planning algorithms against that of roadmaps constructed using sequential planner. Also, we make an argument and provide experimental results that show that motion planning problems involving heterogenous environments (common in most realistic and large-scale motion planning) is a natural fit for spatial subdivision-based parallel processing. Spatial subdivision-based parallel processing approach is suited for heterogeneous environments because it allows for local adaption in solving a global problem while taking advantage of scalability that is possible with parallel processing.


Sampling Based Motion Planning with Reachable Volumes: Application to Manipulators and Closed Chain Systems, Troy McMahon, Shawna L. Thomas, Nancy M. Amato, 2014 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 3705-3712, Chicago, Illinois, USA, Sep 2014. DOI: 10.1109/IROS.2014.6943082
Keywords: Manipulation Planning, Reachability Analysis, Sampling-Based Motion Planning
Links : [Published]

BibTex

@INPROCEEDINGS{6943082, author={T. {McMahon} and S. {Thomas} and N. M. {Amato}}, booktitle={2014 IEEE/RSJ International Conference on Intelligent Robots and Systems}, title={Sampling based motion planning with reachable volumes: Application to manipulators and closed chain systems}, year={2014}, volume={}, number={}, pages={3705-3712}, doi={10.1109/IROS.2014.6943082}}


Abstract

Reachable volumes are a geometric representation of the regions the joints of a robot can reach. They can be used to generate constraint satisfying samples for problems including complicated linkage robots (e.g. closed chains and graspers). They can also be used to assist robot operators and to help in robot design.We show that reachable volumes have an O(1) complexity in unconstrained problems as well as in many constrained problems. We also show that reachable volumes can be computed in linear time and that reachable volume samples can be generated in linear time in problems without constraints. We experimentally validate reachable volume sampling, both with and without constraints on end effectors and/or internal joints. We show that reachable volume samples are less likely to be invalid due to self-collisions, making reachable volume sampling significantly more efficient for higher dimensional problems. We also show that these samples are easier to connect than others, resulting in better connected roadmaps. We demonstrate that our method can be applied to 262-dof, multi-loop, and tree-like linkages including combinations of planar, prismatic and spherical joints. In contrast, existing methods either cannot be used for these problems or do not produce good quality solutions.


KLA: A New Algorithmic Paradigm for Parallel Graph Computations (Conference Best Paper), Harshvardhan, Adam Fidel, Nancy M. Amato, Lawrence Rauchwerger, In Proc. IEEE Int.Conf. on Parallel Architectures and Compilation Techniques (PACT), pp. 27-38, Edmonton, AB, Canada, Aug 2014. DOI: 10.1145/2628071.2628091
Keywords: Parallel Algorithms, Parallel Graph Algorithms, STAPL
Links : [Published]

BibTex

@inproceedings{10.1145/2628071.2628091,
author = {Harshvardhan and Fidel, Adam and Amato, Nancy M. and Rauchwerger, Lawrence},
title = {KLA: A New Algorithmic Paradigm for Parallel Graph Computations},
year = {2014},
isbn = {9781450328098},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2628071.2628091},
doi = {10.1145/2628071.2628091},
abstract = {This paper proposes a new algorithmic paradigm - k-level asynchronous (KLA) - that bridges level-synchronous and asynchronous paradigms for processing graphs. The KLA paradigm enables the level of asynchrony in parallel graph algorithms to be parametrically varied from none (level-synchronous) to full (asynchronous). The motivation is to improve execution times through an appropriate trade-off between the use of fewer, but more expensive global synchronizations, as in level-synchronous algorithms, and more, but less expensive local synchronizations (and perhaps also redundant work), as in asynchronous algorithms. We show how common patterns in graph algorithms can be expressed in the KLA pardigm and provide techniques for determining k, the number of asynchronous steps allowed between global synchronizations. Results of an implementation of KLA in the STAPL Graph Library show excellent scalability on up to 96K cores and improvements of 10x or more over level-synchronous and asynchronous versions for graph algorithms such as breadth-first search, PageRank, k-core decomposition and others on certain classes of real-world graphs.},
booktitle = {Proceedings of the 23rd International Conference on Parallel Architectures and Compilation},
pages = {27–38},
numpages = {12},
keywords = {parallel algorithms, big data, distributed computing, graph analytics, asynchronous graph algorithms},
location = {Edmonton, AB, Canada},
series = {PACT '14}
}


Abstract

This paper proposes a new algorithmic paradigm - k-level asynchronous (KLA) - that bridges level-synchronous and asynchronous paradigms for processing graphs. The KLA paradigm enables the level of asynchrony in parallel graph algorithms to be parametrically varied from none (level-synchronous) to full (asynchronous). The motivation is to improve execution times through an appropriate trade-off between the use of fewer, but more expensive global synchronizations, as in level-synchronous algorithms, and more, but less expensive local synchronizations (and perhaps also redundant work), as in asynchronous algorithms. We show how common patterns in graph algorithms can be expressed in the KLA pardigm and provide techniques for determining k, the number of asynchronous steps allowed between global synchronizations. Results of an implementation of KLA in the STAPL Graph Library show excellent scalability on up to 96K cores and improvements of 10× or more over level-synchronous and asynchronous versions for graph algorithms such as breadth-first search, PageRank, k-core decomposition and others on certain classes of real-world graphs.


From Petascale to the Pocket: Adaptively Scaling Parallel Programs for Mobile SoCs, Adam Fidel, Nancy M. Amato, Lawrence Rauchwerger, In Proc. IEEE Int.Conf. on Parallel Architectures and Compilation Techniques (PACT), SRC Poster, pp. 511-512, Edmonton, AB, Canada, Aug 2014. DOI: 10.1145/2628071.2671426
Keywords: Parallel Programming, STAPL
Links : [Published]

BibTex

@inproceedings{10.1145/2628071.2671426,
author = {Fidel, Adam and Amato, Nancy M. and Rauchwerger, Lawrence},
title = {From Petascale to the Pocket: Adaptively Scaling Parallel Programs for Mobile SoCs},
year = {2014},
isbn = {9781450328098},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2628071.2671426},
doi = {10.1145/2628071.2671426},
booktitle = {Proceedings of the 23rd International Conference on Parallel Architectures and Compilation},
pages = {511–512},
numpages = {2},
keywords = {energy-efficient computing, parallel libraries, shared memory optimization},
location = {Edmonton, AB, Canada},
series = {PACT '14}
}


Abstract

With resource-constrained mobile and embedded devices being outfitted with multicore processors, there exists a need to allow existing parallel programs to be scaled down to efficiently utilize these devices. We study the marriage of programming models originally designed for distributed-memory supercomputers with smaller scale parallel architectures that are shared-memory and generally resource-constrained.


Processing Big Data Graphs on Memory-Restricted Systems, Harshvardhan, Nancy M. Amato, Lawrence Rauchwerger, In Proc. IEEE Int.Conf. on Parallel Architectures and Compilation Techniques (PACT), pp. 517-518, Edmonton, AB, Canada, Aug 2014. DOI: 10.1145/2628071.2671429
Keywords: Big Data, Out-Of-Core Graph Algorithms, Parallel Graph Algorithms
Links : [Published]

BibTex

@inproceedings{10.1145/2628071.2671429,
author = {Harshvardhan and Amato, Nancy M. and Rauchweger, Lawrence},
title = {Processing Big Data Graphs on Memory-Restricted Systems},
year = {2014},
isbn = {9781450328098},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2628071.2671429},
doi = {10.1145/2628071.2671429},
abstract = {With the advent of big-data, processing large graphs quickly has become increasingly important. Most existing approaches either utilize in-memory processing techniques, which can only process graphs that fit completely in RAM, or disk-based techniques that sacrifice performance.In this work, we propose a novel RAM-Disk hybrid approach to graph processing that can scale well from a single shared-memory node to large distributed-memory systems. It works by partitioning the graph into subgraphs that fit in RAM and uses a paging-like technique to load subgraphs. We show that without modifying the algorithms, this approach can scale from small memory-constrained systems (such as tablets) to large-scale distributed machines with 16,000+ cores.},
booktitle = {Proceedings of the 23rd International Conference on Parallel Architectures and Compilation},
pages = {517–518},
numpages = {2},
keywords = {out-of-core graph algorithms, distributed computing, graph analytics, big data, parallel graph processing},
location = {Edmonton, AB, Canada},
series = {PACT '14}
}


Abstract

With the advent of big-data, processing large graphs quickly has become increasingly important. Most existing approaches either utilize in-memory processing techniques, which can only process graphs that fit completely in RAM, or disk-based techniques that sacrifice performance. Contribution. In this work, we propose a novel RAM-Disk hybrid approach to graph processing that can scale well from a single shared-memory node to large distributed-memory systems. It works by partitioning the graph into subgraphs that fit in RAM and uses a paging-like technique to load subgraphs. We show that without modifying the algorithms, this approach can scale from small memory-constrained systems (such as tablets) to large-scale distributed machines with 16, 000+ cores.


A Region-Based Strategy for Collaborative Roadmap Construction, Jory Denny, Read Sandstrom, Nicole Julian, Nancy M. Amato, Algorithmic Foundations of Robotics XI: Selected Contributions of the Eleventh International Workshop on the Algorithmic Foundations of Robotics, pp. 125-141, Istanbul, Turkey, Aug 2014. DOI: 10.1007/978-3-319-16595-0_8
Keywords: Collaborative Planning, Sampling-Based Motion Planning
Links : [Published]

BibTex

@Inbook{Denny2015,
author="Denny, Jory
and Sandstr{\"o}m, Read
and Julian, Nicole
and Amato, Nancy M.",
editor="Akin, H. Levent
and Amato, Nancy M.
and Isler, Volkan
and van der Stappen, A. Frank",
title="A Region-Based Strategy for Collaborative Roadmap Construction",
bookTitle="Algorithmic Foundations of Robotics XI: Selected Contributions of the Eleventh International Workshop on the Algorithmic Foundations of Robotics",
year="2015",
publisher="Springer International Publishing",
address="Cham",
pages="125--141",
abstract="Motion planningMotion planninghasDenny, JoryseenJulian, NicolemuchAmato, NancyattentionSandstrom, Readover the past two decades. A great deal of progress has been made in sampling-based planningSampling-based planning, whereby a planner builds an approximate representation of the planning space. While these planners have demonstrated success in many scenarios, there are still difficult problems where they lack robustness or efficiency, e.g., certain types of narrow spaces. Conversely, human intuition can often determine an approximate solution to these problems quite effectively, but humans lack the speed and precision necessary to perform the corresponding low-level tasks (such as collision checking) in a timely manner. In this work, we introduce a novel strategy called Region Steering in which the user and a PRM planner work cooperatively to map the space while maintaining the probabilistic completeness property of the PRM planner. Region Steering utilizes two-way communication to integrate the strengths of both the user and the planner, thereby overcoming the weaknesses inherent to relying on either one alone. In one communication direction, a user can input regions, or bounding volumes in the workspace, to bias sampling towards or away from these areas. In the other direction, the planner displays its progress to the user and colors the regions based on their perceived usefulness. We demonstrate that Region Steering provides roadmap customizability, reduced mapping time, and smaller roadmap sizes compared with fully automated PRMs, e.g., Gaussian PRM.",
isbn="978-3-319-16595-0",
doi="10.1007/978-3-319-16595-0_8",
url="https://doi.org/10.1007/978-3-319-16595-0_8"
}


Abstract

Motion planning has seen much attention over the past two decades. A great deal of progress has been made in sampling-based planning, whereby a planner builds an approximate representation of the planning space. While these planners have demonstrated success in many scenarios, there are still difficult problems where they lack robustness or efficiency, e.g., certain types of narrow spaces. Conversely, human intuition can often determine an approximate solution to these problems quite effectively, but humans lack the speed and precision necessary to perform the corresponding low-level tasks (such as collision checking) in a timely manner. In this work, we introduce a novel strategy called Region Steering in which the user and a PRM planner work cooperatively to map the space while maintaining the probabilistic completeness property of the PRM planner. Region Steering utilizes two-way communication to integrate the strengths of both the user and the planner, thereby overcoming the weaknesses inherent to relying on either one alone. In one communication direction, a user can input regions, or bounding volumes in the workspace, to bias sampling towards or away from these areas. In the other direction, the planner displays its progress to the user and colors the regions based on their perceived usefulness. We demonstrate that Region Steering provides roadmap customizability, reduced mapping time, and smaller roadmap sizes compared with fully automated PRMs, e.g., Gaussian PRM.


Load balancing n-body simulations with highly non-uniform density, Olga Pearce, Todd Gamblin, Bronis R. de Supinski, Tom Arsenlis, Nancy M. Amato, International Conference on Supercomputing (ICS), pp. 113-122, Muenchen, Germany, Jun 2014. DOI: 10.1145/2597652.2597659
Keywords: Parallel Programming
Links : [Published]

BibTex

@inproceedings{DBLP:conf/ics/PearceGSAA14,
author = {Olga Pearce and
Todd Gamblin and
Bronis R. de Supinski and
Tom Arsenlis and
Nancy M. Amato},
editor = {Arndt Bode and
Michael Gerndt and
Per Stenstr{\"{o}}m and
Lawrence Rauchwerger and
Barton P. Miller and
Martin Schulz},
title = {Load balancing n-body simulations with highly non-uniform density},
booktitle = {2014 International Conference on Supercomputing, ICS'14, Muenchen,
Germany, June 10-13, 2014},
pages = {113--122},
publisher = {{ACM}},
year = {2014},
url = {https://doi.org/10.1145/2597652.2597659},
doi = {10.1145/2597652.2597659},
timestamp = {Tue, 06 Nov 2018 11:07:02 +0100},
biburl = {https://dblp.org/rec/conf/ics/PearceGSAA14.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}


Abstract

N-body methods simulate the evolution of systems of particles (or bodies). They are critical for scientific research in fields as diverse as molecular dynamics, astrophysics, and material science. Most load balancing techniques for N-body methods use particle count to approximate computational work. This approximation is inaccurate, especially for systems with high density variation, because work in an N-body simulation is proportional to the particle density, not the particle count. In this paper, we demonstrate that existing techniques do not perform well at scale when particle density is highly non-uniform, and we propose a load balance technique that efficiently assigns load in terms of interactions instead of particles. We use adaptive sampling to create an even work distribution more amenable to partitioning, and to reduce partitioning overhead. We implement and evaluate our approach on a Barnes-Hut algorithm and a large-scale dislocation dynamics application, ParaDiS. Our method achieves up to 26% improvement in overall performance of Barnes-Hut and 18% in ParaDiS.


Sampling-Based Motion Planning with Reachable Volumes: Theoretical Foundations, Troy McMahon, Shawna Thomas, Nancy M. Amato, In Proc. IEEE International Conference on Robotics and Automation (ICRA), Hong Kong, China, Jun 2014. DOI: 10.1109/ICRA.2014.6907820
Keywords: Manipulation Planning, Reachability Analysis, Sampling-Based Motion Planning
Links : [Published]

BibTex

@INPROCEEDINGS{6907820, author={T. {McMahon} and S. {Thomas} and N. M. {Amato}}, booktitle={2014 IEEE International Conference on Robotics and Automation (ICRA)}, title={Sampling-based motion planning with reachable volumes: Theoretical foundations}, year={2014}, volume={}, number={}, pages={6514-6521}, doi={10.1109/ICRA.2014.6907820}}


Abstract

We introduce a new concept, reachable volumes, that denotes the set of points that the end effector of a chain or linkage can reach. We show that the reachable volume of a chain is equivalent to the Minkowski sum of the reachable volumes of its links, and give an efficient method for computing reachable volumes. We present a method for generating configurations using reachable volumes that is applicable to various types of robots including open and closed chain robots, tree-like robots, and complex robots including both loops and branches. We also describe how to apply constraints (both on end effectors and internal joints) using reachable volumes. Unlike previous methods, reachable volumes work for spherical and prismatic joints as well as planar joints. Visualizations of reachable volumes can allow an operator to see what positions the robot can reach and can guide robot design. We present visualizations of reachable volumes for representative robots including closed chains and graspers as well as for examples with joint and end effector constraints.


MARRT: Medial Axis Biased Rapidly-Exploring Random Trees, Jory Denny, Evan Greco, Shawna L. Thomas, Nancy M. Amato, 2014 IEEE International Conference on Robotics and Automation (ICRA), pp. 90-97, Hong Kong, China, Jun 2014. DOI: 10.1109/ICRA.2014.6906594
Keywords: Motion Planning, Sampling-Based Motion Planning
Links : [Published]

BibTex

@INPROCEEDINGS{6906594, author={J. {Denny} and E. {Greco} and S. {Thomas} and N. M. {Amato}}, booktitle={2014 IEEE International Conference on Robotics and Automation (ICRA)}, title={MARRT: Medial Axis biased rapidly-exploring random trees}, year={2014}, volume={}, number={}, pages={90-97}, doi={10.1109/ICRA.2014.6906594}}


Abstract

Motion planning is a difficult and widely studied problem in robotics. Current research aims not only to find feasible paths, but to ensure paths have certain properties, e.g., shortest or safest paths. This is difficult for current state-of-the-art sampling-based techniques as they typically focus on simply finding any path. Despite this difficulty, sampling-based techniques have shown great success in planning for a wide range of applications. Among such planners, Rapidly-Exploring Random Trees (RRTs) search the planning space by biasing exploration toward unexplored regions. This paper introduces a novel RRT variant, Medial Axis RRT (MARRT), which biases tree exploration to the medial axis of free space by pushing all configurations from expansion steps towards the medial axis. We prove that this biasing increases the tree's clearance from obstacles. Improving obstacle clearance is useful where path safety is important, e.g., path planning for robots performing tasks in close proximity to the elderly. Finally, we experimentally analyze MARRT, emphasizing its ability to effectively map difficult passages while increasing obstacle clearance, and compare it to contemporary RRT techniques.


Spark PRM: Using RRTs within PRMs to efficiently explore narrow passages, Kensen Shi, Jory Denny, Nancy M. Amato, 2014 IEEE International Conference on Robotics and Automation (ICRA), pp. 4659-4666, Hong Kong, China, Jun 2014. DOI: 10.1109/ICRA.2014.6907540
Keywords: Motion Planning, Sampling-Based Motion Planning
Links : [Published]

BibTex

@inproceedings{shi-icra-2014
, author = "Kensen Shi and Jory Denny and Nancy M. Amato"
, title = "Spark PRM: Using RRTs within PRMs to efficiently explore narrow passages"
, booktitle = "IEEE International Conference on Robotics and Automation (ICRA)"
, location = "Hong Kong"
, month = "May"
, year = "2014"
, pages = "4659-4666"
, doi =10.1109/ICRA.2014.6907540"
}


Abstract

Probabilistic RoadMaps (PRMs) have been successful for many high-dimensional motion planning problems. However, they encounter difficulties when mapping narrow passages. While many PRM sampling methods have been proposed to increase the proportion of samples within narrow passages, such difficult planning areas still pose many challenges. We introduce a novel algorithm, Spark PRM, that sparks the growth of Rapidly-expanding Random Trees (RRTs) from narrow passage samples generated by a PRM. The RRT rapidly generates further narrow passage samples, ideally until the passage is fully mapped. After reaching a terminating condition, the tree stops growing and is added to the roadmap. Spark PRM is a general method that can be applied to all PRM variants. We study the benefits of Spark PRM with a variety of sampling strategies in a wide array of environments. We show significant speedups in computation time over RRT, Sampling-based Roadmap of Trees (SRT), and various PRM variants.


UMAPRM: Uniformly Sampling the Medial Axis, Hsin-Yi (Cindy) Yeh, Jory Denny, Aaron Lindsey, Shawna L. Thomas, Nancy M. Amato, 2014 IEEE International Conference on Robotics and Automation (ICRA), pp. 5798 -5803, Hong Kong, China, Jun 2014. DOI: 10.1109/ICRA.2014.6907711
Keywords: Motion Planning, Sampling-Based Motion Planning
Links : [Published]

BibTex

@INPROCEEDINGS{6907711, author={H. C. {Yeh} and J. {Denny} and A. {Lindsey} and S. {Thomas} and N. M. {Amato}}, booktitle={2014 IEEE International Conference on Robotics and Automation (ICRA)}, title={UMAPRM: Uniformly sampling the medial axis}, year={2014}, volume={}, number={}, pages={5798-5803}, doi={10.1109/ICRA.2014.6907711}}


Abstract

Maintaining clearance, or distance from obstacles, is a vital component of successful motion planning algorithms. Maintaining high clearance often creates safer paths for robots. Contemporary sampling-based planning algorithms that utilize the medial axis, or the set of all points equidistant to two or more obstacles, produce higher clearance paths. However, they are biased heavily toward certain portions of the medial axis, sometimes ignoring parts critical to planning, e.g., specific types of narrow passages. We introduce Uniform Medial Axis Probabilistic RoadMap (UMAPRM), a novel planning variant that generates samples uniformly on the medial axis of the free portion of C space . We theoretically analyze the distribution generated by UMAPRM and show its uniformity. Our results show that UMAPRM's distribution of samples along the medial axis is not only uniform but also preferable to other medial axis samplers in certain planning problems. We demonstrate that UMAPRM has negligible computational overhead over other sampling techniques and can solve problems the others could not, e.g., a bug trap. Finally, we demonstrate UMAPRM successfully generates higher clearance paths in the examples.


Reciprocally-Rotating Velocity Obstacles, Andrew Giese, Daniel Latypov, Nancy M. Amato, 2014 IEEE International Conference on Robotics and Automation (ICRA), pp. 3234-3241, Hong Kong, China, Jun 2014. DOI: 10.1109/ICRA.2014.6907324
Keywords: Motion Planning, Multi-Agent
Links : [Published]

BibTex

@INPROCEEDINGS{6907324, author={A. {Giese} and D. {Latypov} and N. M. {Amato}}, booktitle={2014 IEEE International Conference on Robotics and Automation (ICRA)}, title={Reciprocally-Rotating Velocity Obstacles}, year={2014}, volume={}, number={}, pages={3234-3241}, doi={10.1109/ICRA.2014.6907324}}


Abstract

Modern multi-agent systems frequently use high-level planners to extract basic paths for agents and then rely on local collision avoidance to ensure that the agents reach their destinations without colliding with one another or dynamic obstacles. One state-of-the-art local collision avoidance technique is Optimal Reciprocal Collision Avoidance (ORCA). Despite being fast and efficient for circular-shaped agents, ORCA may deadlock when polygonal shapes are used. To address this shortcoming, we introduce Reciprocally-Rotating Velocity Obstacles (RRVO). RRVO generalizes ORCA by introducing a notion of rotation for polygonally-shaped agents. This generalization permits more realistic motion than ORCA and does not suffer from as much deadlock. In this paper, we present the theory of RRVO and show empirically that it does not suffer from the deadlock issue ORCA has, permits agents to reach goals faster, and has a comparable collision rate at the cost of performance overhead quadratic in the (typically small) user-defined parameter δ.


Using Load Balancing to Scalably Parallelize Sampling-Based Motion Planning Algorithms, Adam Fidel, Sam Ade Jacobs, Shishir Sharma, Nancy M. Amato, Lawrence Rauchwerger, 2014 IEEE 28th International Parallel and Distributed Processing Symposium, pp. 573-582, Phoenix, AZ, USA, May 2014. DOI: 10.1109/IPDPS.2014.66
Keywords: Parallel Algorithms, Parallel Planning, Sampling-Based Motion Planning
Links : [Published]

BibTex

@INPROCEEDINGS{6877290, author={A. {Fidel} and S. A. {Jacobs} and S. {Sharma} and N. M. {Amato} and L. {Rauchwerger}}, booktitle={2014 IEEE 28th International Parallel and Distributed Processing Symposium}, title={Using Load Balancing to Scalably Parallelize Sampling-Based Motion Planning Algorithms}, year={2014}, volume={}, number={}, pages={573-582}, doi={10.1109/IPDPS.2014.66}}


Abstract

Motion planning, which is the problem of computing feasible paths in an environment for a movable object, has applications in many domains ranging from robotics, to intelligent CAD, to protein folding. The best methods for solving this PSPACE-hard problem are so-called sampling-based planners. Recent work introduced uniform spatial subdivision techniques for parallelizing sampling-based motion planning algorithms that scaled well. However, such methods are prone to load imbalance, as planning time depends on region characteristics and, for most problems, the heterogeneity of the sub problems increases as the number of processors increases. In this work, we introduce two techniques to address load imbalance in the parallelization of sampling-based motion planning algorithms: an adaptive work stealing approach and bulk-synchronous redistribution. We show that applying these techniques to representatives of the two major classes of parallel sampling-based motion planning algorithms, probabilistic roadmaps and rapidly-exploring random trees, results in a more scalable and load-balanced computation on more than 3,000 cores.


Robust Online Belief Space Planning in Changing Environments: Application to Physical Mobile Robots, Ali-akbar Agha-mohammadi, Saurav Agarwal, Aditya Mahadevan, Suman Chakravorty, Daniel Tomkins, Jory Denny, Nancy M. Amato, 2014 IEEE International Conference on Robotics and Automation (ICRA), pp. 149-156, Hong Kong, China, May 2014. DOI: 10.1109/ICRA.2014.6906602
Keywords: Belief Space, Mobile Robots, Sampling-Based Motion Planning
Links : [Published] [Manuscript]

BibTex

@INPROCEEDINGS{6906602, author={A. {Agha-mohammadi} and S. {Agarwal} and A. {Mahadevan} and S. {Chakravorty} and D. {Tomkins} and J. {Denny} and N. M. {Amato}}, booktitle={2014 IEEE International Conference on Robotics and Automation (ICRA)}, title={Robust online belief space planning in changing environments: Application to physical mobile robots}, year={2014}, volume={}, number={}, pages={149-156}, doi={10.1109/ICRA.2014.6906602}}


Abstract

Motion planning in belief space (under motion and sensing uncertainty) is a challenging problem due to the computational intractability of its exact solution. The Feedback-based Information RoadMap (FIRM) framework made an important theoretical step toward enabling roadmap-based planning in belief space and provided a computationally tractable version of belief space planning. However, there are still challenges in applying belief space planners to physical systems, such as the discrepancy between computational models and real physical models. In this paper, we propose a dynamic replanning scheme in belief space to address such challenges. Moreover, we present techniques to cope with changes in the environment (e.g., changes in the obstacle map), as well as unforeseen large deviations in the robot's location (e.g., the kidnapped robot problem). We then utilize these techniques to implement the first online replanning scheme in belief space on a physical mobile robot that is robust to changes in the environment and large disturbances. This method demonstrates that belief space planning is a practical tool for robot motion planning.


Adaptive Neighbor Connection using Node Characterization , Chinwe Ekenna , Shawna Thomas , Nancy M. Amato , N/A, N/A, Jan 2014.
Keywords: Machine Learning, Sampling-Based Motion Planning
Links : [Published]

BibTex

@MISC{Ekenna_adaptiveneighbor,
author = {Chinwe Ekenna and Shawna Thomas and Nancy M. Amato},
title = {Adaptive Neighbor Connection using Node Characterization},
year = {}
}


Abstract

Abstract. Sampling-based motion planning has been successful in plan-ning the motion for a wide variety of robot types. An important primitive of these methods involves connecting nodes by selecting candidate neigh-bors and checking the path between them. Recently, an approach called Adaptive Neighbor Connection (ANC) was proposed to automate neigh-bor selection using machine learning techniques. It adaptively selects a neighbor connection strategy from a candidate set of options and learns which strategy to use by examining their success rates and costs. In this work, we extend ANC’s reward function by characterizing the types of nodes added (including information about their connectivity) after each connection attempt. In doing so, we gain insight into the na-ture of these nodes and their ability to improve roadmap quality. We also refine ANC’s cost function by considering the computation time spent during each connection attempt so as to potentially learn faster and more efficient connectors. We show improved performance over selecting a sin-gle connection strategy and over ignoring node characterization during learning. We present results in a variety of 2D and 3D environments and are able to solve queries in half the time on average compared to the best single connection strategy and to the original ANC work. 1


Toggle PRM: A Coordinated Mapping of C-Free and C-Obstacle in Arbitrary Dimension, Jory Denny and Nancy M. Amato, Algorithmic Foundations of Robotics X. Springer Tracts in Advanced Robotics (STAR). Presented at the 2012 Workshop on the Algorithmic Foundations of Robotics (WAFR)., Vol: 86, pp. 297-312, Dec 2013. DOI: 10.1007/978-3-642-36279-8_18
Keywords: Robotics, Sampling-Based Motion Planning
Links : [Published]

BibTex

@article{denny-wafr-2012
, author = "Jory Denny and Nancy M. Amato"
, title = "Toggle PRM: A Coordinated Mapping of C-Free and C-Obstacle in Arbitrary Dimension"
, journal = "Algorithmic Foundations of Robotics X. Springer Tracts in Advanced Robotics (STAR)"
, volume = "86"
, publisher = "Springer, Berlin, Heidelberg."
, pages = "297-312"
, year = "2013"
, doi = "doi.org/10.1007/978-3-642-36279-8_18"
, note = "Presented at the 2012 Workshop on the Algorithmic Foundations of Robotics (WAFR)."
}


Abstract

Motion planning has received much attention over the past 40 years. More than 15 years have passed since the introduction of the successful sampling-based approach known as the Probabilistic RoadMap Method (PRM). PRM and its many variants have demonstrated great success for some high-dimensional problems, but they all have some level of difficulty in the presence of narrow passages. Recently, an approach called Toggle PRM has been introduced whose performance does not degrade for 2-dimensional problems with narrow passages. In Toggle PRM, a simultaneous, coordinated mapping of both C-free and C-obst is performed and every connection attempt augments one of the maps – either validating an edge in the current space or adding a configuration ’witnessing’ the connection failure to the other space. In this paper, we generalize Toggle PRM to d-dimensions and show that the benefits of mapping both C-free and C-obst continue to hold in higher dimensions. In particular, we introduce a new narrow passage characterization, alpha-epsilon-separable narrow passages, which describes the types of passages that can be successfully mapped by Toggle PRM. Intuitively,alpha-epsilson-separable narrow passages are arbitrarily narrow regions of C-free that separate regions of C-obst , at least locally, such as hallways in an office building. We experimentally compare Toggle PRM with other methods in a variety of scenarios with different types of narrow passages and robots with up to 16 dof.


FIRM: Sampling-based feedback motion-planning under motion uncertainty and imperfect measurements, Ali-akbar Agha-mohammadi, Suman Chakravorty, Nancy M. Amato, International Journal of Robotics Research (ijrr), Vol: 33, Issue: 2, pp. 268-304, Nov 2013. DOI: 10.1177/0278364913501564
Keywords: Belief Space, Sampling-Based Motion Planning
Links : [Published]

BibTex

@article{doi:10.1177/0278364913501564,
author = {Ali-akbar Agha-mohammadi and Suman Chakravorty and Nancy M Amato},
title ={FIRM: Sampling-based feedback motion-planning under motion uncertainty and imperfect measurements},
journal = {The International Journal of Robotics Research},
volume = {33},
number = {2},
pages = {268-304},
year = {2014},
doi = {10.1177/0278364913501564},

URL = {
https://doi.org/10.1177/0278364913501564

},
eprint = {
https://doi.org/10.1177/0278364913501564

}
,
abstract = { In this paper we present feedback-based information roadmap (FIRM), a multi-query approach for planning under uncertainty which is a belief-space variant of probabilistic roadmap methods. The crucial feature of FIRM is that the costs associated with the edges are independent of each other, and in this sense it is the first method that generates a graph in belief space that preserves the optimal substructure property. From a practical point of view, FIRM is a robust and reliable planning framework. It is robust since the solution is a feedback and there is no need for expensive replanning. It is reliable because accurate collision probabilities can be computed along the edges. In addition, FIRM is a scalable framework, where the complexity of planning with FIRM is a constant multiplier of the complexity of planning with PRM. In this paper, FIRM is introduced as an abstract framework. As a concrete instantiation of FIRM, we adopt stationary linear quadratic Gaussian (SLQG) controllers as belief stabilizers and introduce the so-called SLQG-FIRM. In SLQG-FIRM we focus on kinematic systems and then extend to dynamical systems by sampling in the equilibrium space. We investigate the performance of SLQG-FIRM in different scenarios. }
}


Abstract

In this paper we present feedback-based information roadmap (FIRM), a multi-query approach for planning under uncertainty which is a belief-space variant of probabilistic roadmap methods. The crucial feature of FIRM is that the costs associated with the edges are independent of each other, and in this sense it is the first method that generates a graph in belief space that preserves the optimal substructure property. From a practical point of view, FIRM is a robust and reliable planning framework. It is robust since the solution is a feedback and there is no need for expensive replanning. It is reliable because accurate collision probabilities can be computed along the edges. In addition, FIRM is a scalable framework, where the complexity of planning with FIRM is a constant multiplier of the complexity of planning with PRM. In this paper, FIRM is introduced as an abstract framework. As a concrete instantiation of FIRM, we adopt stationary linear quadratic Gaussian (SLQG) controllers as belief stabilizers and introduce the so-called SLQG-FIRM. In SLQG-FIRM we focus on kinematic systems and then extend to dynamical systems by sampling in the equilibrium space. We investigate the performance of SLQG-FIRM in different scenarios.


Adaptive Neighbor Connection for PRMs: A Natural Fit for Heterogeneous Environments and Parallelism, Chinwe Ekenna, Sam Ade Jacobs, Shawna Thomas, Nancy M. Amato, In. Proc. IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Tokyo, Japan, Nov 2013. DOI: 10.1109/IROS.2013.6696510
Keywords: Parallel Algorithms, Parallel Planning, Sampling-Based Motion Planning
Links : [Published]

BibTex

@INPROCEEDINGS{6696510, author={C. {Ekenna} and S. A. {Jacobs} and S. {Thomas} and N. M. {Amato}}, booktitle={2013 IEEE/RSJ International Conference on Intelligent Robots and Systems}, title={Adaptive neighbor connection for PRMs: A natural fit for heterogeneous environments and parallelism}, year={2013}, volume={}, number={}, pages={1249-1256}, doi={10.1109/IROS.2013.6696510}}


Abstract

Probabilistic Roadmap Methods (PRMs) are widely used motion planning methods that sample robot configurations (nodes) and connect them to form a graph (roadmap) containing feasible trajectories. Many PRM variants propose different strategies for each of the steps and choosing among them is problem dependent. Planning in heterogeneous environments and/or on parallel machines necessitates dividing the problem into regions where these choices have to be made for each one. Hand-selecting the best method for each region becomes infeasible. In particular, there are many ways to select connection candidates, and choosing the appropriate strategy is input dependent. In this paper, we present a general connection framework that adaptively selects a neighbor finding strategy from a candidate set of options. Our framework learns which strategy to use by examining their success rates and costs. It frees the user of the burden of selecting the best strategy and allows the selection to change over time. We perform experiments on rigid bodies of varying geometry and articulated linkages up to 37 degrees of freedom. Our results show that strategy performance is indeed problem/region dependent, and our adaptive method harnesses their strengths. Over all problems studied, our method differs the least from manual selection of the best method, and if one were to manually select a single method across all problems, the performance can be quite poor. Our method is able to adapt to changing sampling density and learns different strategies for each region when the problem is partitioned for parallelism.


Blind RRT: A Probabilistically Complete Distributed RRT, Cesar Rodriguez, Jory Denny, Sam Ade Jacobs, Shawna Thomas, Nancy M. Amato, In. Proc. IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Tokyo, Japan, Nov 2013. DOI: 10.1109/IROS.2013.6696587
Keywords: Parallel Planning, Rapidly-exploring Random Tree (RRT), Sampling-Based Motion Planning
Links : [Published]

BibTex

@INPROCEEDINGS{6696587, author={C. {Rodriguez} and J. {Denny} and S. A. {Jacobs} and S. {Thomas} and N. M. {Amato}}, booktitle={2013 IEEE/RSJ International Conference on Intelligent Robots and Systems}, title={Blind RRT: A probabilistically complete distributed RRT}, year={2013}, volume={}, number={}, pages={1758-1765}, doi={10.1109/IROS.2013.6696587}}


Abstract

Rapidly-Exploring Random Trees (RRTs) have been successful at finding feasible solutions for many types of problems. With motion planning becoming more computationally demanding, we turn to parallel motion planning for efficient solutions. Existing work on distributed RRTs has been limited by the overhead that global communication requires. A recent approach, Radial RRT, demonstrated a scalable algorithm that subdivides the space into regions to increase the computation locality. However, if an obstacle completely blocks RRT growth in a region, the planning space is not covered and is thus not probabilistically complete. We present a new algorithm, Blind RRT, which ignores obstacles during initial growth to efficiently explore the entire space. Because obstacles are ignored, free components of the tree become disconnected and fragmented. Blind RRT merges parts of the tree that have become disconnected from the root. We show how this algorithm can be applied to the Radial RRT framework allowing both scalability and effectiveness in motion planning. This method is a probabilistically complete approach to parallel RRTs. We show that our method not only scales but also overcomes the motion planning limitations that Radial RRT has in a series of difficult motion planning tasks.


Adapting RRT Growth for Heterogeneous Environments, Jory Denny, Marco Morales, Samuel Rodriguez, Nancy M. Amato, In. Proc. IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Tokyo, Japan, Nov 2013. DOI: 10.1109/IROS.2013.6696589
Keywords: Adaptive Algorithm, Rapidly-exploring Random Tree (RRT), Sampling-Based Motion Planning
Links : [Published]

BibTex

@INPROCEEDINGS{6696589, author={J. {Denny} and M. {Morales} and S. {Rodriguez} and N. M. {Amato}}, booktitle={2013 IEEE/RSJ International Conference on Intelligent Robots and Systems}, title={Adapting RRT growth for heterogeneous environments}, year={2013}, volume={}, number={}, pages={1772-1778}, doi={10.1109/IROS.2013.6696589}}


Abstract

Rapidly-exploring Random Trees (RRTs) are effective for a wide range of applications ranging from kinodynamic planning to motion planning under uncertainty. However, RRTs are not as efficient when exploring heterogeneous environments and do not adapt to the space. For example, in difficult areas an expensive RRT growth method might be appropriate, while in open areas inexpensive growth methods should be chosen. In this paper, we present a novel algorithm, Adaptive RRT, that adapts RRT growth to the current exploration area using a two level growth selection mechanism. At the first level, we select groups of expansion methods according to the visibility of the node being expanded. Second, we use a cost-sensitive learning approach to select a sampler from the group of expansion methods chosen. Also, we propose a novel definition of visibility for RRT nodes which can be computed in an online manner and used by Adaptive RRT to select an appropriate expansion method. We present the algorithm and experimental analysis on a broad range of problems showing not only its adaptability, but efficiency gains achieved by adapting exploration methods appropriately.


Rigidity Analysis for Protein Motion and Folding Core Identification, Shawna Thomas, Lydia Tapia, Chinwe Ekenna, Hsin-Yi (Cindy) Yeh, Nancy M. Amato, Association for the Advancement of Artificial Intelligence (AAAI) Workshop, Vol: WS-13-06, pp. 38-43, Bellevue, Washington, Jul 2013.
Keywords: Computational Biology, Protein Folding, Sampling-Based Motion Planning
Links : [Published]

BibTex

@inproceedings{thomas-aaaiW-2013
, author = "Shawna Thomas and Lydia Tapia and Chinwe Ekenna and Hsin-Yi (Cindy) Yeh and Nancy M. Amato "
, title = "Rigidity Analysis for Protein Motion and Folding Core Identification"
, booktitle = "AAAI Workshop on Artificial Intelligence and Robotics Methods in Computational Biology"
, volume = "WS-13-06"
, month = "July"
, address = "Bellevue, Washington, USA"
, pages = "38-43"
, year = "2013"
}


Abstract

Protein folding plays an essential role in protein function and stability. Despite the explosion in our knowledge of structural and functional data, our understanding of protein folding is still very limited. In addition, methods such as folding core identification are gaining importance with the increased desire to engineer proteins with particular functions and efficiencies. However, defining the folding core can be challenging for both experiment and simulation. In this work, we use rigidity analysis to effectively sample and model the protein's energy landscape and identify the folding core. Our results show that rigidity analysis improves the accuracy of our approximate landscape models and produces landscape models that capture the subtle folding differences between protein G and its mutants, NuG1 and NuG2. We then validate our folding core identification against known experimental data and compare to other simulation tools. In addition to correlating well with experiment, our method can suggest other components of structure that have not been identified as part of the core because they were not previously measured experimentally.


Graph-based Stochastic Control with Constraints: A Unified Approach with Perfect and Imperfect Measurements, Ali-akbar Agha-mohammadi, Suman Chakravorty, Nancy M. Amato, In. Proc. American Control Conference, Washington, DC, USA, Jun 2013. DOI: 10.1109/ACC.2013.6580545
Keywords: Belief Space, Sampling-Based Motion Planning
Links : [Published]

BibTex

@INPROCEEDINGS{6580545, author={A. {Agha-mohammadi} and S. {Chakravorty} and N. M. {Amato}}, booktitle={2013 American Control Conference}, title={Graph-based stochastic control with constraints: A unified approach with perfect and imperfect measurements}, year={2013}, volume={}, number={}, pages={4581-4586}, doi={10.1109/ACC.2013.6580545}}


Abstract

This paper is concerned with the problem of stochastic optimal control (possibly with imperfect measurements) in the presence of constraints. We propose a computationally tractable framework to address this problem. The method lends itself to sampling-based methods where we construct a graph in the state space of the problem, on which a Dynamic Programming (DP) is solved and a closed-loop feedback policy is computed. The constraints are seamlessly incorporated to the control policy selection by including their effect on the transition probabilities of the graph edges. We present a unified framework that is applicable both in the state space (with perfect measurements) and in the information space (with imperfect measurements).


A scalable distributed RRT for motion planning, Sam Ade Jacobs, Nicholas Stradford, Cesar Rodriguez, Shawna Thomas, Nancy M. Amato, 2013 IEEE International Conference on Robotics and Automation, pp. 5088-5095, Karlsruhe, Germany, May 2013. DOI: 10.1109/ICRA.2013.6631304
Keywords: Parallel Planning, Rapidly-exploring Random Tree (RRT), Sampling-Based Motion Planning
Links : [Published]

BibTex

@INPROCEEDINGS{6631304,
author={S. A. {Jacobs} and N. {Stradford} and C. {Rodriguez} and S. {Thomas} and N. M. {Amato}},
booktitle={2013 IEEE International Conference on Robotics and Automation},
title={A scalable distributed RRT for motion planning},
year={2013},
volume={},
number={},
pages={5088-5095},
doi={10.1109/ICRA.2013.6631304}}


Abstract

Rapidly-exploring Random Tree (RRT), like other sampling-based motion planning methods, has been very successful in solving motion planning problems. Even so, sampling-based planners cannot solve all problems of interest efficiently, so attention is increasingly turning to parallelizing them. However, one challenge in parallelizing RRT is the global computation and communication overhead of nearest neighbor search, a key operation in RRTs. This is a critical issue as it limits the scalability of previous algorithms. We present two parallel algorithms to address this problem. The first algorithm extends existing work by introducing a parameter that adjusts how much local computation is done before a global update. The second algorithm radially subdivides the configuration space into regions, constructs a portion of the tree in each region in parallel, and connects the subtrees,i removing cycles if they exist. By subdividing the space, we increase computation locality enabling a scalable result. We show that our approaches are scalable. We present results demonstrating almost linear scaling to hundreds of processors on a Linux cluster and a Cray XE6 machine.


Scaling Techniques for Massive Scale-Free Graphs in Distributed (External) Memory, Roger Pearce, Maya Gokhale, Nancy M. Amato, IEEE International Symposium on Parallel and Distributed Processing (IPDPS), pp. 825-836, Cambridge, Massachusetts, USA, May 2013. DOI: 10.1109/IPDPS.2013.72
Keywords: Parallel Graph Algorithms
Links : [Published]

BibTex

@inproceedings{DBLP:conf/ipps/PearceGA13,
author = {Roger A. Pearce and
Maya B. Gokhale and
Nancy M. Amato},
title = {Scaling Techniques for Massive Scale-Free Graphs in Distributed (External)
Memory},
booktitle = {27th {IEEE} International Symposium on Parallel and Distributed Processing,
{IPDPS} 2013, Cambridge, MA, USA, May 20-24, 2013},
pages = {825--836},
publisher = {{IEEE} Computer Society},
year = {2013},
url = {https://doi.org/10.1109/IPDPS.2013.72},
doi = {10.1109/IPDPS.2013.72},
timestamp = {Mon, 20 Apr 2020 17:14:44 +0200},
biburl = {https://dblp.org/rec/conf/ipps/PearceGA13.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}


Abstract

We present techniques to process large scale-free graphs in distributed memory. Our aim is to scale to trillions of edges, and our research is targeted at leadership class supercomputers and clusters with local non-volatile memory, e.g., NAND Flash. We apply an edge list partitioning technique, designed to accommodate high-degree vertices (hubs) that create scaling challenges when processing scale-free graphs. In addition to partitioning hubs, we use ghost vertices to represent the hubs to reduce communication hotspots. We present a scaling study with three important graph algorithms: Breadth-First Search (BFS), K-Core decomposition, and Triangle Counting. We also demonstrate scalability on BG/P Intrepid by comparing to best known Graph500 results [1]. We show results on two clusters with local NVRAM storage that are capable of traversing trillion-edge scale-free graphs. By leveraging node-local NAND Flash, our approach can process thirty-two times larger datasets with only a 39% performance degradation in Traversed Edges Per Second (TEPS).


Lazy Toggle PRM: A Single-Query Approach to Motion Planning, Jory Denny, Kensen Shi, and Nancy M. Amato, Proc. IEEE Int. Conf. on Robotics and Automation (ICRA), pp. 2407-2414, Karlsruhe, Germany, May 2013. DOI: 10.1109/ICRA.2013.6630904
Keywords: Robotics, Sampling-Based Motion Planning
Links : [Published]

BibTex

@inproceedings{denny-icra-2013
, author = "Jory Denny and Kensen Shi and Nancy M. Amato"
, title = "Lazy Toggle PRM: A single-query approach to motion planning"
, booktitle = "IEEE International Conference on Robotics and Automation"
, location = "Karlsruhe, Germany"
, month = "May"
, year - "2013"
, pages = "2407-2414"
, doi = "10.1109/ICRA.2013.6630904"
}


Abstract

Probabilistic RoadMaps (PRMs) are quite successful in solving complex and high-dimensional motion planning problems. While particularly suited for multiple-query scenarios and expansive spaces, they lack efficiency in both solving single-query scenarios and mapping narrow spaces. Two PRM variants separately tackle these gaps. Lazy PRM reduces the computational cost of roadmap construction for single-query scenarios by delaying roadmap validation until query time. Toggle PRM is well suited for mapping narrow spaces by mapping both C free and C obst , which gives certain theoretical benefits. However, fully validating the two resulting roadmaps can be costly. We present a strategy, Lazy Toggle PRM, for integrating these two approaches into a method which is both suited for narrow passages and efficient single-query calculations. This simultaneously addresses two challenges of PRMs. Like Lazy PRM, Lazy Toggle PRM delays validation of roadmaps until query time, but if no path is found, the algorithm augments the roadmap using the Toggle PRM methodology. We demonstrate the effectiveness of Lazy Toggle PRM in a wide range of scenarios, including those with narrow passages and high descriptive complexity (e.g., those described by many triangles), concluding that it is more effective than existing methods in solving difficult queries.


Improving Roadmap Quality through Connected Component Expansion, Juan Burgos, Jory Denny, Nancy M. Amato, Technical Report, TR13-003, Texas A&M University, Apr 2013.
Keywords: Sampling-Based Motion Planning
Links : [Manuscript]

BibTex

N/A


Abstract

Motion planning is the problem of computing valid paths through an environment. However, because computing exact solutions is intractable, sampling-based algorithms, such as Probabilistic RoadMaps (PRMs), have gained popularity. PRMs compute an approximate mapping of the planning space by sacrificing completeness in favor of efficiency. However, these algorithms have certain bottlenecks that hinder performance which causes difficulty mapping narrow or crowded regions, and the cost of nearest-neighbor queries, which is the asymptotic bottleneck of these algorithms. Thus, roadmaps may fail to efficiently capture the connectivity of the planning space. In this paper, we present a set of connected component (CC) expansion algorithms, each with different biases (random expand, expand to the nearest CC, expand away from the host CC, and expand along the medial-axis) and expansion node selection policies (random, farthest from CC’s centroid, and difficult nodes), that create a linked-chain of configurations designed to enable efficient connection of CCs. Given an a priori roadmap quality requirement in terms of roadmap connectivity, we show that when our expansion methods augment PRMs, we reach the required roadmap connectivity in less time.


Load Balancing Techniques for Scalable Parallelization of Sampling-Based Motion Planning Algorithms, Adam Fidel, Sam Ade Jacobs, Shishir Sharma, Lawrence Rauchwerger, Nancy M. Amato, Technical Report, TR13-002 , Parasol Laboratory, Department of Computer Science, Texas A&M University, College Station, TX, USA, Mar 2013.
Keywords: Parallel Planning, Sampling-Based Motion Planning, STAPL
Links : [Manuscript]

BibTex

@techreport{afidel-tr13-002-2013,
title = "Load Balancing Techniques for Scalable Parallelization of Sampling-Based Motion Planning Algorithms",
author = "Fidel, Adam and Jacobs, Sam Ade and Sharma, Shishir and Rauchwerger, Lawrence and Amato, Nancy M. ",
institution = "Texas A&M University",
address = "College Station, TX, USA",
number = "TR13-002",
year = 2013,
month = mar
}


Abstract

Motion planning, which is the problem of computing feasible paths through an environment for a movable object, has applications in many domains ranging from robotics, to intelligent CAD, to protein folding. The best methods for solving this PSPACE-hard problem are so-called sampling-based planners. Recent work introduced uniform spatial subdivision techniques for parallelizing sampling-based motion planning algorithms that scaled well. However, such methods are prone to load imbalance, particularly as the number of processors is increased, because planning time depends on region characteristics and, for most problems, the heterogeneity of the set of regions increases as the region size decreases. In this work, we introduce two techniques to address the problems of load imbalance in the parallelization of sampling-based motion planning algorithms: bulk-synchronous redistribution and an adaptive workstealing approach. We show that applying these techniques to representatives of the two major classes of parallel sampling-based motion planning algorithms, probabilistic roadmaps and rapidly-exploring random trees, results in a more scalable and load-balanced computation on 3,000+ cores.


Efficient Massively Parallel Transport Sweeps, W. Daryl Hawkins, Timmie Smith, Michael P. Adams, Lawrence Rauchwerger, Nancy Amato, Marvin L. Adams, Transactions of the American Nuclear Society, Vol: 107, Issue: 1, pp. 477-481, San Diego, CA, USA, Nov 2012.
Keywords: Parallel Algorithms, Scientific Computing, STAPL
Links : [Published]

BibTex

@article{hawkins-tans-2012
, author = "W. Daryl Hawkins and Timmie Smith and Michael P. Adams and Lawrence Rauchwerger and Nancy Amato and Marvin L. Adams"
, title = "Efficient Massively Parallel Transport Sweeps"
, journal = "Trans. American Nuclear Society"
, volume = "107"
, number = "1"
, month = "November"
, year = "2012"
, pages = "477-481"
, issn = "0003-018X"
, note = "2012 ANS Annual Winter Meeting ; Conference date: 11-11-2012 Through 15-11-2012",
}


Abstract

The full-domain “sweep,” in which all angular fluxes in a problem are calculated given previous-iterate values only for the volumetric source, forms the foundation for many iterative methods that have desirable properties. One important property is that iteration counts do not grow with mesh refinement. The sweep solution on parallel machines is complicated by the dependency of a given cell on its upstream neighbors. A sweep algorithm is defined by its partitioning (dividing the domain among processors), aggregation (grouping cells, directions, and energy groups into “tasks”), and scheduling (choosing which task to execute if more than one is available). The work presented here follows that of Bailey and Falgout, who theoretically and computationally evaluated the performance of three sweep algorithms. Their “data-driven” schedule appeared to be optimal—executing the sweep in the minimum possible number of stages—for tested partitionings and aggregations, but they were unable to prove this mathematically and they did not attempt to optimize across possible partitionings and aggregations. Here we consider 3D Cartesian grids of Nx × Ny × Nz spatial cells and simple Px × Py × Pz partitioning, and we permit general aggregation of cells, directions, and energy groups. We have found a provably optimal family of scheduling algorithms and we present results from one of these. We exploit our guaranteed minimum stage count to further optimize sweep-execution time by choosing the best possible partitioning and aggregation parameters. Our results show excellent scaling out to 32,768 cores, significantly better than results previously reported for sweeps and in line with the optimistic projections.


UOBPRM: A uniformly distributed obstacle-based PRM, Hsin-Yi Yeh, Shawna Thomas, David Eppstein, Nancy M. Amato, IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 2655-2662, Oct 2012. DOI: 10.1109/IROS.2012.6385875
Keywords: obstacle-based, Sampling-Based Motion Planning
Links : [Published]

BibTex

@INPROCEEDINGS{6385875,
author={Yeh, Hsin-Yi and Thomas, Shawna and Eppstein, David and Amato, Nancy M.},
booktitle={2012 IEEE/RSJ International Conference on Intelligent Robots and Systems},
title={UOBPRM: A uniformly distributed obstacle-based PRM},
year={2012},
volume={},
number={},
pages={2655-2662},
doi={10.1109/IROS.2012.6385875}}


Abstract

This paper presents a new sampling method for motion planning that can generate configurations more uniformly distributed on C-obstacle surfaces than prior approaches. Here, roadmap nodes are generated from the intersections between C-obstacles and a set of uniformly distributed fixed-length segments in C-space. The results show that this new sampling method yields samples that are more uniformly distributed than previous obstacle-based methods such as OBPRM, Gaussian sampling, and Bridge test sampling. UOBPRM is shown to have nodes more uniformly distributed near C-obstacle surfaces and also requires the fewest nodes and edges to solve challenging motion planning problems with varying narrow passages.


A Multi-Directional Rapidly Exploring Random Graph (mRRG) for Protein Folding, Shuvra Nath, Shawna Thomas, Chinwe Ekenna, Nancy M. Amato, Proc. ACM Conference on Bioinformatics, Computational Biology and Biomedicine (BCB), pp. 44-51, Orlando, FL, USA, Oct 2012. DOI: 10.1145/2382936.2382942
Keywords: Computational Biology, Protein Folding
Links : [Published]

BibTex

@inproceedings{nath-bcb-2012
, author = "Shuvra Nath and Shawna Thomas and Chinwe Ekenna and Nancy M. Amato"
, title = "A Multi-Directional Rapidly Exploring Random Graph (mRRG) for Protein Folding"
, booktitle = "ACM Conference on Bioinformatics, Computational Biology and Biomedicine (BCB)"
, pages = "44-51"
, location ="Orlando, FL, USA"
, month = "October"
, year = "2012"
, doi = "10.1145/2382936.2382942"
}


Abstract

Modeling large-scale protein motions, such as those involved in folding and binding interactions, is crucial to better understanding not only how proteins move and interact with other molecules but also how proteins misfold, thus causing many devastating diseases. Robotic motion planning algorithms, such as Rapidly Exploring Random Trees (RRTs), have been successful in simulating protein folding pathways. Here, we propose a new multi-directional Rapidly Exploring Random Graph (mRRG) specifically tailored for proteins. Unlike traditional RRGs which only expand a parent conformation in a single direction, our strategy expands the parent conformation in multiple directions to generate new samples. Resulting samples are connected to the parent conformation and its nearest neighbors. By leveraging multiple directions, mRRG can model the protein motion landscape with reduced computational time compared to several other robotics-based methods for small to moderate-sized proteins. Our results on several proteins agree with experimental hydrogen out-exchange, pulse-labeling, and Φ-value analysis. We also show that mRRG covers the conformation space better as compared to the other computation methods.


Sampling-based nonholonomic motion planning in belief space via Dynamic Feedback Linearization-based FIRM, Ali-akbar Agha-mohammadi, Suman Chakravorty, Nancy M. Amato, 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Oct 2012. DOI: 10.1109/IROS.2012.6385970
Keywords: Belief Space, Sampling-Based Motion Planning
Links : [Published]

BibTex

@INPROCEEDINGS{6385970,

author={A. {Agha-mohammadi} and S. {Chakravorty} and N. M. {Amato}},

booktitle={2012 IEEE/RSJ International Conference on Intelligent Robots and Systems},

title={Sampling-based nonholonomic motion planning in belief space via Dynamic Feedback Linearization-based FIRM},

year={2012},

volume={},

number={},

pages={4433-4440},

doi={10.1109/IROS.2012.6385970}}


Abstract

In roadmap-based methods, such as the Probabilistic Roadmap Method (PRM) in deterministic environments or the Feedback-based Information RoadMap (FIRM) in partially observable probabilistic environments, a stabilizing controller is needed to guarantee node reachability in state or belief space. In belief space, it has been shown that belief-node reachability can be achieved using stationary Linear Quadratic Gaussian (LQG) controllers, for linearly controllable systems. However, for nonholonomic systems such as a unicycle model, belief reachability is a challenge. In this paper, we construct a roadmap in information space, where the local planners in partially-observable space are constructed by utilizing a Kalman filter as an estimator along with a Dynamic Feedback Linearization-based (DFL-based) controller as the belief controller. As a consequence, the task of belief stabilization to pre-defined nodes in belief space is accomplished even for nonholonomic systems. Therefore, a query-independent roadmap is generated in belief space that preserves the “principle of optimality”, required in dynamic programming solvers. This method serves as an offline POMDP solver for motion planning in belief space, which can seamlessly take obstacles into account. Experimental results show the efficiency of both individual local planners and the overall planner over the information graph for a nonholonomic model.


The STAPL Parallel Graph Library, Harshvardhan, Adam Fidel, Nancy M. Amato, Lawrence Rauchwerger, Languages and Compilers for Parallel Computing (LCPC), pp. 46-60, Tokyo, Japan, Sep 2012. DOI: 10.1007/978-3-642-37658-0_4
Keywords: Parallel Graph Algorithms, STAPL
Links : [Published]

BibTex

@InProceedings{10.1007/978-3-642-37658-0_4,
author=\"Harshvardhan
and Fidel, Adam
and Amato, Nancy M.
and Rauchwerger, Lawrence\",
editor=\"Kasahara, Hironori
and Kimura, Keiji\",
title=\"The STAPL Parallel Graph Library\",
booktitle=\"Languages and Compilers for Parallel Computing\",
year=\"2013\",
publisher=\"Springer Berlin Heidelberg\",
address=\"Berlin, Heidelberg\",
pages=\"46--60\",
abstract=\"This paper describes the stapl Parallel Graph Library, a high-level framework that abstracts the user from data-distribution and parallelism details and allows them to concentrate on parallel graph algorithm development. It includes a customizable distributed graph container and a collection of commonly used parallel graph algorithms. The library introduces pGraphpViews that separate algorithm design from the container implementation. It supports three graph processing algorithmic paradigms, level-synchronous, asynchronous and coarse-grained, and provides common graph algorithms based on them. Experimental results demonstrate improved scalability in performance and data size over existing graph libraries on more than 16,000 cores and on internet-scale graphs containing over 16 billion vertices and 250 billion edges.\",
isbn=\"978-3-642-37658-0\"
}


Abstract

This paper describes the stapl Parallel Graph Library, a high-level framework that abstracts the user from data-distribution and parallelism details and allows them to concentrate on parallel graph algorithm development. It includes a customizable distributed graph container and a collection of commonly used parallel graph algorithms. The library introduces pGraph pViews that separate algorithm design from the container implementation. It supports three graph processing algorithmic paradigms, level-synchronous, asynchronous and coarse-grained, and provides common graph algorithms based on them. Experimental results demonstrate improved scalability in performance and data size over existing graph libraries on more than 16,000 cores and on internet-scale graphs containing over 16 billion vertices and 250 billion edges.


Quantifying the effectiveness of load balance algorithms, Olga Pearce, Todd Gamblin, Bronis R. de Supinski, Martin Schulz, Nancy M. Amato, International Conference on Supercomputing (ICS), pp. 185-194, Venice, Italy, Jun 2012. DOI: 10.1145/2304576.2304601
Keywords: Adaptive Algorithm
Links : [Published]

BibTex

@inproceedings{DBLP:conf/ics/PearceGSSA12,
author = {Olga Pearce and
Todd Gamblin and
Bronis R. de Supinski and
Martin Schulz and
Nancy M. Amato},
editor = {Utpal Banerjee and
Kyle A. Gallivan and
Gianfranco Bilardi and
Manolis Katevenis},
title = {Quantifying the effectiveness of load balance algorithms},
booktitle = {International Conference on Supercomputing, ICS'12, Venice, Italy,
June 25-29, 2012},
pages = {185--194},
publisher = {{ACM}},
year = {2012},
url = {https://doi.org/10.1145/2304576.2304601},
doi = {10.1145/2304576.2304601},
timestamp = {Tue, 06 Nov 2018 11:07:02 +0100},
biburl = {https://dblp.org/rec/conf/ics/PearceGSSA12.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}


Abstract

Load balance is critical for performance in large parallel applications. An imbalance on today's fastest supercomputers can force hundreds of thousands of cores to idle, and on future exascale machines this cost will increase by over a factor of a thousand. Improving load balance requires a detailed understanding of the amount of computational load per process and an application's simulated domain, but no existing metrics sufficiently account for both factors. Current load balance mechanisms are often integrated into applications and make implicit assumptions about the load. Some strategies place the burden of providing accurate load information, including the decision on when to balance, on the application. Existing application-independent mechanisms simply measure the application load without any knowledge of application elements, which limits them to identifying imbalance without correcting it. Our novel load model couples abstract application information with scalable measurements to derive accurate and actionable load metrics. Using these metrics, we develop a cost model for correcting load imbalance. Our model enables comparisons of the effectiveness of load balancing algorithms in any specific imbalance scenario. Our model correctly selects the algorithm that achieves the lowest runtime in up to 96% of the cases, and can achieve a 19% gain over selecting a single balancing algorithm for all cases.


A Scalable Method for Parallelizing Sampling-Based Motion Planning Algorithms, Sam Ade Jacobs, Kasra Manavi, Juan Burgos, Jory Denny, Shawna Thomas, Nancy M. Amato, 2012 IEEE International Conference on Robotics and Automation, pp. 2529-2536, Saint Paul, MN, USA, May 2012. DOI: 10.1109/ICRA.2012.6225334
Keywords: Parallel Algorithms, Parallel Planning, Sampling-Based Motion Planning
Links : [Published]

BibTex

@INPROCEEDINGS{6225334,
author={S. A. {Jacobs} and K. {Manavi} and J. {Burgos} and J. {Denny} and S. {Thomas} and N. M. {Amato}},
booktitle={2012 IEEE International Conference on Robotics and Automation},
title={A scalable method for parallelizing sampling-based motion planning algorithms},
year={2012},
volume={},
number={},
pages={2529-2536},
doi={10.1109/ICRA.2012.6225334}}


Abstract

This paper describes a scalable method for parallelizing sampling-based motion planning algorithms. It subdivides configuration space (C-space) into (possibly overlapping) regions and independently, in parallel, uses standard (sequential) sampling-based planners to construct roadmaps in each region. Next, in parallel, regional roadmaps in adjacent regions are connected to form a global roadmap. By subdividing the space and restricting the locality of connection attempts, we reduce the work and inter-processor communication associated with nearest neighbor calculation, a critical bottleneck for scalability in existing parallel motion planning methods. We show that our method is general enough to handle a variety of planning schemes, including the widely used Probabilistic Roadmap (PRM) and Rapidly-exploring Random Trees (RRT) algorithms. We compare our approach to two other existing parallel algorithms and demonstrate that our approach achieves better and more scalable performance. Our approach achieves almost linear scalability on a 2400 core LINUX cluster and on a 153,216 core Cray XE6 petascale machine.


On the Probabilistic Completeness of the Sampling-based Feedback Motion Planners in Belief Space, Ali-akbar Agha-mohammadi, Suman Chakravorty, Nancy M. Amato, IEEE International Conference on Robotics and Automation (ICRA), May 2012. DOI: 10.1109/ICRA.2012.6225062
Keywords: Belief Space, Sampling-Based Motion Planning
Links : [Published]

BibTex

@INPROCEEDINGS{6225062,

author={A. {Agha-mohammadi} and S. {Chakravorty} and N. M. {Amato}},

booktitle={2012 IEEE International Conference on Robotics and Automation},

title={On the probabilistic completeness of the sampling-based feedback motion planners in belief space},

year={2012},

volume={},

number={},

pages={3983-3990},

doi={10.1109/ICRA.2012.6225062}}


Abstract

This paper extends the concept of “probabilistic completeness” defined for motion planners in state space (or configuration space) to the concept of “probabilistic completeness under uncertainty” for motion planners in belief space. Accordingly, an approach is proposed to verify the probabilistic completeness of the sampling-based planners in belief space. Finally, through the proposed approach, it is shown that under mild conditions the sampling-based methods constructed based on the abstract framework of FIRM (Feedback-based Information Roadmap Method) are probabilistically complete under uncertainty.


The Toggle Local Planner for sampling-based motion planning, Jory Denny and Nancy M. Amato, Proc. IEEE Int. Conf. Robot. Autom. (ICRA), pp. 1779-1786, St. Paul, MN, USA, May 2012. DOI: 10.1109/ICRA.2012.6225212
Keywords: Robotics, Sampling-Based Motion Planning
Links : [Published]

BibTex

@inproceedings{denny-icra-2012
, author = "Jory Denny and Nancy M. Amato"
, title = "The Toggle Local Planner for sampling-based motion planning"
, booktitle = "IEEE International Conference on Robotics and Automation (ICRA)"
, location = "Saint Paul, MN, USA"
, month = "May"
, year - "2012"
, pages = "1779-1786"
, doi = "10.1109/ICRA.2012.6225212"
}


Abstract

Sampling-based solutions to the motion planning problem, such as the probabilistic roadmap method (PRM), have become commonplace in robotics applications. These solutions are the norm as the dimensionality of the planning space grows, i.e., d >; 5. An important primitive of these methods is the local planner, which is used for validation of simple paths between two configurations. The most common is the straight-line local planner which interpolates along the straight line between the two configurations. In this paper, we introduce a new local planner, Toggle Local Planner (Toggle LP), which extends local planning to a two-dimensional subspace of the overall planning space. If no path exists between the two configurations in the subspace, then Toggle LP is guaranteed to correctly return false. Intuitively, more connections could be found by Toggle LP than by the straight-line planner, resulting in better connected roadmaps. As shown in our results, this is the case, and additionally, the extra cost, in terms of time or storage, for Toggle LP is minimal. Additionally, our experimental analysis of the planner shows the benefit for a wide array of robots, with DOF as high as 70.


From Days to Seconds: Scalable Parallel Algorithms for Motion Planning, Sam Ade Jacobs, Nancy M. Amato, In ACM Student Research Compet, Conf. on High Performance Computing Networking, Storage and Analysis Companion Proceedings, Seattle, Washington, USA, Nov 2011.
Keywords: Parallel Planning, Sampling-Based Motion Planning, STAPL
Links : [Published]

BibTex

N/A


Abstract

This work describes a scalable method for parallelizing sampling-based motion planning algorithms. It subdivides configuration space (C-space) into (possibly overlapping) regions and independently, in parallel, uses standard (sequential) sampling-based planners to construct roadmaps in each region. Next, in parallel, regional roadmaps in adjacent regions are connected to form a global roadmap. By subdividing the space and restricting the locality of connection attempts, we reduce the work and inter-processor communication associated with nearest neighbor calculation, a critical bottleneck for scalability in existing parallel motion planning methods. We show that our method is general enough to handle a variety of planning schemes, including the widely used Probabilistic Roadmap (PRM) and Rapidly-exploring Random Trees (RRT) algorithms. We compare our approach to two other existing parallel algorithms and demonstrate that our approach achieves better and more scalable performance. Our approach achieves almost linear scalability on a 2400 core LINUX cluster and on a 153,216 core Cray XE6 petascale machine.


Toggle PRM: Simultaneous mapping of C-free and C-obstacle - A study in 2D, Jory Denny and Nancy M. Amato, Proc. IEEE/RSJ Int. Conf. Intel. Rob. Syst. (IROS), pp. 2632-2639, Sep 2011. DOI: 10.1109/IROS.2011.6095102
Keywords: Robotics, Sampling-Based Motion Planning
Links :

BibTex

@proceedings{denny-iros-2011
, author = "Jory Denny and Nancy Amato"
, title = "Toggle PRM: Simultaneous mapping of C-free and C-obstacle - A study in 2D"
, booktitle = "Proc. IEEE/RSJ Int. Conf. Intel. Rob. Syst. (IROS)"
, location = "San Francisco, CA, USA"
, month = "September"
, year = "2011"
, pages = "2632-2639"
, doi = "10.1109/IROS.2011.6095102"
}


Abstract

Motion planning is known to be difficult. Probabilistic planners have made great advances, but still have difficulty for problems that require planning in narrow passages or on surfaces in C space . This work proposes Toggle PRM, a new methodology for PRMs that simultaneously maps both free and obstacle space. In this paper, we focus on 2 DOF problems and show that mapping both spaces leads to increased sampling density in narrow passages and to improved overall efficiency as compared to previous sampling based approaches.


FIRM: Feedback controller-based information-state roadmap - A framework for motion planning under uncertainty, Ali-akbar Agha-mohammadi, Suman Chakravorty, Nancy M. Amato, IEEE/RSJ International Conference on Intelligent Robots and Systems, San Francisco, CA, USA, Sep 2011. DOI: 10.1109/IROS.2011.6095010
Keywords: Algorithms, Sampling-Based Motion Planning
Links : [Published]

BibTex

@inproceedings{agha2011firm,
title={FIRM: Feedback controller-based Information-state RoadMap-a framework for motion planning under uncertainty},
author={Agha-Mohammadi, Ali-Akbar and Chakravorty, Suman and Amato, Nancy M},
booktitle={2011 IEEE/RSJ International Conference on Intelligent Robots and Systems},
pages={4284--4291},
year={2011},
organization={IEEE}
}


Abstract

Direct transformation of sampling-based motion planning methods to the Information-state (belief) space is a challenge. The main bottleneck for roadmap-based techniques in belief space is that the incurred costs on different edges of the graph are not independent of each other. In this paper, we generalize the Probabilistic RoadMap (PRM) framework to obtain a Feedback controller-based Information-state RoadMap (FIRM) that takes into account motion and sensing uncertainty in planning. The FIRM nodes and edges lie in belief space and the crucial feature of FIRM is that the costs associated with different edges of FIRM are independent of each other. Therefore, this construct essentially breaks the “curse of history” in the original Partially Observable Markov Decision Process (POMDP), which models the planning problem. Further, we show how obstacles can be rigorously incorporated into planning on FIRM. All these properties stem from utilizing feedback controllers in the construction of FIRM.


Toward Realistic Pursuit-Evasion Using a Roadmap-Based Approach, Samuel Rodriguez, Jory Denny, Juan Burgos, Aditya Mahadevan, Kasra Manavi, Luke Murray, Anton Kodochygov, Takis Zourntos, and Nancy M. Amato, IEEE International Conference on Robotics and Automation, Shanghai, China, May 2011. DOI: 10.1109/ICRA.2011.5980467
Keywords: Group Behavior, Multi-Agent, Sampling-Based Motion Planning
Links : [Published]

BibTex

@inproceedings{rodriguez2011toward,
title={Toward realistic pursuit-evasion using a roadmap-based approach},
author={Rodriguez, Samuel and Denny, Jory and Burgos, Juan and Mahadevan, Aditya and Manavi, Kasra and Murray, Luke and Kodochygov, Anton and Zourntos, Takis and Amato, Nancy M},
booktitle={2011 IEEE International Conference on Robotics and Automation},
pages={1738--1745},
year={2011},
organization={IEEE}
}


Abstract

In this work, we describe an approach for modeling and simulating group behaviors for pursuit-evasion that uses a graph-based representation of the environment and integrates multi-agent simulation with roadmap-based path planning. Our approach can be applied to more realistic scenarios than are typically studied in most previous work, including agents moving in 3D environments such as terrains, multi-story buildings, and dynamic environments. We also support more realistic three-dimensional visibility computations that allow evading agents to hide in crowds or behind hills. We demonstrate the utility of this approach on mobile robots and in simulation for a variety of scenarios including pursuit-evasion and tag on terrains, in multi-level buildings, and in crowds.


The STAPL Parallel Container Framework, Gabriel Tanase, Antal Buss, Adam Fidel, Harshvardhan, Ioannis Papadopoulos, Olga Pearce, Timmie Smith, Nathan Thomas, Xiabing Xu, Nedhal Mourad, Jeremy Vu, Mauro Bianco, Nancy M. Amato, Lawrence Rauchwerger, Proceedings of the 16th ACM Symposium on Principles and Practice of Parallel Programming (PPoPP), pp. 235-246, San Antonio, TX, USA, Feb 2011. DOI: 10.1145/1941553.1941586
Keywords: Parallel Containers, Parallel Programming, STAPL
Links : [Published]

BibTex

@article{10.1145/2038037.1941586,
author = {Tanase, Gabriel and Buss, Antal and Fidel, Adam and Harshvardhan and Papadopoulos, Ioannis and Pearce, Olga and Smith, Timmie and Thomas, Nathan and Xu, Xiabing and Mourad, Nedal and Vu, Jeremy and Bianco, Mauro and Amato, Nancy M. and Rauchwerger, Lawrence},
title = {The STAPL Parallel Container Framework},
year = {2011},
issue_date = {August 2011},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
volume = {46},
number = {8},
issn = {0362-1340},
url = {https://doi.org/10.1145/2038037.1941586},
doi = {10.1145/2038037.1941586},
abstract = {The Standard Template Adaptive Parallel Library (STAPL) is a parallel programming infrastructure that extends C++ with support for parallelism. It includes a collection of distributed data structures called pContainers that are thread-safe, concurrent objects, i.e., shared objects that provide parallel methods that can be invoked concurrently. In this work, we present the STAPL Parallel Container Framework (PCF), that is designed to facilitate the development of generic parallel containers. We introduce a set of concepts and a methodology for assembling a pContainer from existing sequential or parallel containers, without requiring the programmer to deal with concurrency or data distribution issues. The PCF provides a large number of basic parallel data structures (e.g., pArray, pList, pVector, pMatrix, pGraph, pMap, pSet). The PCF provides a class hierarchy and a composition mechanism that allows users to extend and customize the current container base for improved application expressivity and performance. We evaluate STAPL pContainer performance on a CRAY XT4 massively parallel system and show that pContainer methods, generic pAlgorithms, and different applications provide good scalability on more than 16,000 processors.},
journal = {SIGPLAN Not.},
month = feb,
pages = {235–246},
numpages = {12},
keywords = {languages, libraries, data, structures, containers, parallel}
}

@inproceedings{10.1145/1941553.1941586,
author = {Tanase, Gabriel and Buss, Antal and Fidel, Adam and Harshvardhan and Papadopoulos, Ioannis and Pearce, Olga and Smith, Timmie and Thomas, Nathan and Xu, Xiabing and Mourad, Nedal and Vu, Jeremy and Bianco, Mauro and Amato, Nancy M. and Rauchwerger, Lawrence},
title = {The STAPL Parallel Container Framework},
year = {2011},
isbn = {9781450301190},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1941553.1941586},
doi = {10.1145/1941553.1941586},
abstract = {The Standard Template Adaptive Parallel Library (STAPL) is a parallel programming infrastructure that extends C++ with support for parallelism. It includes a collection of distributed data structures called pContainers that are thread-safe, concurrent objects, i.e., shared objects that provide parallel methods that can be invoked concurrently. In this work, we present the STAPL Parallel Container Framework (PCF), that is designed to facilitate the development of generic parallel containers. We introduce a set of concepts and a methodology for assembling a pContainer from existing sequential or parallel containers, without requiring the programmer to deal with concurrency or data distribution issues. The PCF provides a large number of basic parallel data structures (e.g., pArray, pList, pVector, pMatrix, pGraph, pMap, pSet). The PCF provides a class hierarchy and a composition mechanism that allows users to extend and customize the current container base for improved application expressivity and performance. We evaluate STAPL pContainer performance on a CRAY XT4 massively parallel system and show that pContainer methods, generic pAlgorithms, and different applications provide good scalability on more than 16,000 processors.},
booktitle = {Proceedings of the 16th ACM Symposium on Principles and Practice of Parallel Programming},
pages = {235–246},
numpages = {12},
keywords = {containers, libraries, structures, languages, parallel, data},
location = {San Antonio, TX, USA},
series = {PPoPP \'11}
}


Abstract

The Standard Template Adaptive Parallel Library (STAPL) is a parallel programming infrastructure that extends C++ with support for parallelism. It includes a collection of distributed data structures called pContainers that are thread-safe, concurrent objects, i.e., shared objects that provide parallel methods that can be invoked concurrently. In this work, we present the STAPL Parallel Container Framework (PCF), that is designed to facilitate the development of generic parallel containers. We introduce a set of concepts and a methodology for assembling a pContainer from existing sequential or parallel containers, without requiring the programmer to deal with concurrency or data distribution issues. The PCF provides a large number of basic parallel data structures (e.g., pArray, pList, pVector, pMatrix, pGraph, pMap, pSet). The PCF provides a class hierarchy and a composition mechanism that allows users to extend and customize the current container base for improved application expressivity and performance. We evaluate STAPL pContainer performance on a CRAY XT4 massively parallel system and show that pContainer methods, generic pAlgorithms, and different applications provide good scalability on more than 16,000 processors.


Utilizing Roadmaps in Evacuation Planning, Samuel Rodriguez, Nancy M. Amato, The International Journal of Virtual Reality, Vol: 10, Issue: 1, Jan 2011. DOI: https://doi.org/10.20870/IJVR.2011.10.1.2804
Keywords: Motion Planning
Links : [Published]

BibTex

@article{rodriguez2011utilizing,
title={Utilizing roadmaps in evacuation planning},
author={Rodriguez, Samuel and Amato, Nancy M},
journal={International Journal of Virtual Reality},
volume={10},
number={1},
pages={67--73},
year={2011}
}


Abstract

In this paper we describe utilization of roadmaps in a general evacuation planning system for complex 3D environments. The problem consists of heterogeneous groups of agents whose behaviors and properties affect usage of the environment when creating evacuation plans. This planning system includes behaviors for those agents evacuating and directors that may be guiding the agents to improve evacuation. One aspect we focus on is modeling different forms of direction and communication between agents.


Roadmap-Based Pursuit-Evasion in 3D Structures, Samuel Rodriguez, Jory Denny, Aditya Mahadevan, Jeremy Vu, Juan Burgos, Takis Zourntos, and Nancy M. Amato, International Conference on Computer Animation and Social Agents, Jan 2011.
Keywords: Motion Planning
Links : [Published]

BibTex

@inproceedings{rodriguez2011roadmap,
title={Roadmap-based pursuit-evasion in 3d structures},
author={Rodriguez, Samuel and Denny, Jory and Mahadevan, Aditya and Vu, Jeremy and Burgos, Juan and Zourntos, Takis and Amato, Nancy M},
booktitle={24th Intern. Conf. on Computer Animation and Social Agents (CASA)},
year={2011},
organization={Citeseer}
}


Abstract

We present an approach to the pursuit-evasion problem which is applicable to complex, multi-level environments. Studying each aspect of this problem in 3D structured environments is a distinct extension over many previous approaches. We also utilize our roadmap-based approach to multi-agent behavior when tracking agents of interest. Results are presented in three multi-level environments to highlight the search, pursuit and evasion components of the problem.


Toward Simulating Realistic Pursuit-Evasion Using a Roadmap-Based Approach, amuel Rodriguez, Jory Denny, Takis Zourntos, Nancy M. Amato, International Conference on Motion in Games, pp. 82-93, Utrecht, Netherlands, Nov 2010. DOI: https://doi.org/10.1007/978-3-642-16958-8_9
Keywords: Motion Planning
Links : [Published]

BibTex

@inproceedings{rodriguez2010toward,
title={Toward simulating realistic pursuit-evasion using a roadmap-based approach},
author={Rodriguez, Samuel and Denny, Jory and Zourntos, Takis and Amato, Nancy M},
booktitle={International Conference on Motion in Games},
pages={82--93},
year={2010},
organization={Springer}
}


Abstract

In this work, we describe an approach for modeling and simulating group behaviors for pursuit-evasion that uses a graph-based representation of the environment and integrates multi-agent simulation with roadmap-based path planning. We demonstrate the utility of this approach for a variety of scenarios including pursuit-evasion on terrains, in multi-level buildings, and in crowds.


Multithreaded Asynchronous Graph Traversal for In-Memory and Semi-External Memory, Roger Pearce, Maya Gokhale, Nancy M. Amato, Conference on High Performance Computing Networking, Storage and Analysis (SC), pp. 1-11, New Orleans, Louisiana, USA, Nov 2010. DOI: 10.1109/SC.2010.34
Keywords: Parallel Graph Algorithms
Links : [Published]

BibTex

@inproceedings{DBLP:conf/sc/PearceGA10,
author = {Roger A. Pearce and
Maya B. Gokhale and
Nancy M. Amato},
title = {Multithreaded Asynchronous Graph Traversal for In-Memory and Semi-External
Memory},
booktitle = {Conference on High Performance Computing Networking, Storage and Analysis,
{SC} 2010, New Orleans, LA, USA, November 13-19, 2010},
pages = {1--11},
publisher = {{IEEE}},
year = {2010},
url = {https://doi.org/10.1109/SC.2010.34},
doi = {10.1109/SC.2010.34},
timestamp = {Mon, 20 Apr 2020 17:14:43 +0200},
biburl = {https://dblp.org/rec/conf/sc/PearceGA10.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}


Abstract

Processing large graphs is becoming increasingly important for many domains such as social networks, bioinformatics, etc. Unfortunately, many algorithms and implementations do not scale with increasing graph sizes. As a result, researchers have attempted to meet the growing data demands using parallel and external memory techniques. We present a novel asynchronous approach to compute Breadth-First-Search (BFS), Single-Source-Shortest-Paths, and Connected Components for large graphs in shared memory. Our highly parallel asynchronous approach hides data latency due to both poor locality and delays in the underlying graph data storage. We present an experimental study applying our technique to both In-Memory and Semi-External Memory graphs utilizing multi-core processors and solid-state memory devices. Our experiments using synthetic and real-world datasets show that our asynchronous approach is able to overcome data latencies and provide significant speedup over alternative approaches. For example, on billion vertex graphs our asynchronous BFS scales up to 14 x on 16-cores.


The STAPL pView, Antal Buss, Adam Fidel, Harshvardhan, Timmie Smith, Gabriel Tanase, Nathan Thomas, Xiabing Xu, Mauro Bianco, Nancy M. Amato, Lawrence Rauchwerger, Languages and Compilers for Parallel Computing (LCPC), pp. 261-275, Houston, Texas, USA, Oct 2010. DOI: 10.1007/978-3-642-19595-2_18
Keywords: Parallel Containers, Parallel Programming, STAPL
Links : [Published]

BibTex

@InProceedings{10.1007/978-3-642-19595-2_18,
author=\"Buss, Antal
and Fidel, Adam
and Harshvardhan
and Smith, Timmie
and Tanase, Gabriel
and Thomas, Nathan
and Xu, Xiabing
and Bianco, Mauro
and Amato, Nancy M.
and Rauchwerger, Lawrence\",
editor=\"Cooper, Keith
and Mellor-Crummey, John
and Sarkar, Vivek\",
title=\"The STAPL pView\",
booktitle=\"Languages and Compilers for Parallel Computing\",
year=\"2011\",
publisher=\"Springer Berlin Heidelberg\",
address=\"Berlin, Heidelberg\",
pages=\"261--275\",
abstract=\"The Standard Template Adaptive Parallel Library (STAPL) is a C++ parallel programming library that provides a collection of distributed data structures (pContainers) and parallel algorithms (pAlgorithms) and a generic methodology for extending them to provide customized functionality. STAPL algorithms are written in terms of pViews, which provide a generic access interface to pContainer data by abstracting common data structure concepts. Briefly, pViews allow the same pContainer to present multiple interfaces, e.g., enabling the same pMatrix to be `viewed\' (or used) as a row-major or column-major matrix, or even as a vector. In this paper, we describe the staplpView concept and its properties. pViews generalize the iterator concept and enable parallelism by providing random access to, and an ADT for, collections of elements. We illustrate how pViews provide support for managing the tradeoff between expressivity and performance and examine the performance overhead incurred when using pViews.\",
isbn=\"978-3-642-19595-2\"
}


Abstract

The Standard Template Adaptive Parallel Library (STAPL) is a C++ parallel programming library that provides a collection of distributed data structures (pContainers) and parallel algorithms (pAlgorithms) and a generic methodology for extending them to provide customized functionality. STAPL algorithms are written in terms of pViews, which provide a generic access interface to pContainer data by abstracting common data structure concepts. Briefly, pViews allow the same pContainer to present multiple interfaces, e.g., enabling the same pMatrix to be ‘viewed’ (or used) as a row-major or column-major matrix, or even as a vector. In this paper, we describe the stapl pView concept and its properties. pViews generalize the iterator concept and enable parallelism by providing random access to, and an ADT for, collections of elements. We illustrate how pViews provide support for managing the tradeoff between expressivity and performance and examine the performance overhead incurred when using pViews.


Region Identification Methods for Efficient and Automated Motion Planning, Jory Denny, Anshul Agrawal, Evan Greco, Lydia Tapia, Nancy M. Amato, N/A, Sep 2010.
Keywords: Motion Planning
Links : [Published]

BibTex

@article{denny2010region,
title={Region Identification Methods for Efficient and Automated Motion Planning},
author={Denny, Jory and Agrawal, Anshul and Greco, Evan and Tapia, Lydia and Amato, Nancy M},
year={2010},
publisher={Citeseer}
}


Abstract

Realistic environments for motion planning problems typically consist of multiple areas, e.g., in a house there are rooms, hallways, and doorways. Decomposition of the environment into smaller areas, or regions, has been shown to impact the quality of planning for adaptive methods. Also other complex problems, such as planning for groups of agents with environmental preferences, can be aided with automated region identification. However, despite the advantages of regions, automatic region identification is not always easy, inexpensive, or consistent. In this paper we explore and compare three methods of automatic region identification and quantify the effects of the resulting regions on planning. The three strategies used for region identification are the statistical methods: k-means clustering, PG-means clustering, and Hierarchical clustering. First, we explore the differences in the regions produced by these three methods in a variety of rigid body and articulated linkage environments. Then, we quantitatively compare the usefulness of the region methods in automated motion planning through the application of the regions within the Unsupervised Adaptive Strategy for Motion Planning (UAS). Our results indicate both regions that seem intuitive and non-intuitive to the problems solve motion planning problems with increased efficiency and automation. That is, we found that planning generally benefitted from using regions, and the benefit was not tied to the naturalness of the region.


Reachable distance space: Efficient sampling-based planning for spatially constrained systems, Xinyu Tang, Shawna Thomas, Phillip Coleman, Nancy M. Amato, The International Journal of Robotics Research, Vol: 29, Issue: 7, pp. 916-934, Jun 2010. DOI: 10.1177/0278364909357643
Keywords: Motion Planning, Sampling-Based Motion Planning
Links : [Published]

BibTex

@article{tang2010reachable,
title={Reachable distance space: Efficient sampling-based planning for spatially constrained systems},
author={Tang, Xinyu and Thomas, Shawna and Coleman, Phillip and Amato, Nancy M},
journal={The international journal of robotics research},
volume={29},
number={7},
pages={916--934},
year={2010},
publisher={SAGE Publications Sage UK: London, England}
}


Abstract

Motion planning for spatially constrained robots is difficult due to additional constraints placed on the robot, such as closure constraints for closed chains or requirements on end-effector placement for articulated linkages. It is usually computationally too expensive to apply sampling-based planners to these problems since it is difficult to generate valid configurations. We overcome this challenge by redefining the robot’s degrees of freedom and constraints into a new set of parameters, called reachable distance space (RD-space), in which all configurations lie in the set of constraint-satisfying subspaces. This enables us to directly sample the constrained subspaces with complexity linear in the number of the robot’s degrees of freedom. In addition to supporting efficient sampling of configurations, we show that the RD-space formulation naturally supports planning and, in particular, we design a local planner suitable for use by sampling-based planners. We demonstrate the effectiveness and efficiency of our approach for several systems including closed chain planning with multiple loops, restricted end-effector sampling, and on-line planning for drawing/sculpting. We can sample single-loop closed chain systems with 1,000 links in time comparable to open chain sampling, and we can generate samples for 1,000-link multi-loop systems of varying topologies in less than a second.


STAPL: Standard Template Adaptive Parallel Library, Antal Buss, Harshvardhan, Ioannis Papadopoulos, Olga Tkachyshyn, Timmie Smith, Gabriel Tanase, Nathan Thomas, Xiabing Xu, Mauro Bianco, Nancy M. Amato, Lawrence Rauchwerger, Proceedings of the 3rd Annual Haifa Experimental Systems Conference, Haifa, Israel, May 2010. DOI: 10.1145/1815695.1815713
Keywords: High-Performance Computing, Parallel Programming, STAPL
Links : [Published]

BibTex

@inproceedings{10.1145/1815695.1815713,
author = {Buss, Antal and Harshvardhan and Papadopoulos, Ioannis and Pearce, Olga and Smith, Timmie and Tanase, Gabriel and Thomas, Nathan and Xu, Xiabing and Bianco, Mauro and Amato, Nancy M. and Rauchwerger, Lawrence},
title = {STAPL: Standard Template Adaptive Parallel Library},
year = {2010},
isbn = {9781605589084},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1815695.1815713},
doi = {10.1145/1815695.1815713},
abstract = {The Standard Template Adaptive Parallel Library (stapl) is a high-productivity parallel programming framework that extends C++ and stl with unified support for shared and distributed memory parallelism. stapl provides distributed data structures (pContainers) and parallel algorithms (pAlgorithms) and a generic methodology for extending them to provide customized functionality. The stapl runtime system provides the abstraction for communication and program execution. In this paper, we describe the major components of stapl and present performance results for both algorithms and data structures showing scalability up to tens of thousands of processors.},
booktitle = {Proceedings of the 3rd Annual Haifa Experimental Systems Conference},
articleno = {14},
numpages = {10},
keywords = {high productivity parallel programming, parallel data structures, library},
location = {Haifa, Israel},
series = {SYSTOR \'10}
}


Abstract

The Standard Template Adaptive Parallel Library (stapl) is a high-productivity parallel programming framework that extends C++ and stl with unified support for shared and distributed memory parallelism. stapl provides distributed data structures (pContainers) and parallel algorithms (pAlgorithms) and a generic methodology for extending them to provide customized functionality. The stapl runtime system provides the abstraction for communication and program execution. In this paper, we describe the major components of stapl and present performance results for both algorithms and data structures showing scalability up to tens of thousands of processors.


Behavior-Based Evacuation Planning, Samuel Rodriguez, Nancy M. Amato, IEEE International Conference on Robotics and Automation, May 2010. DOI: 10.1109/ROBOT.2010.5509502
Keywords: Group Behavior, Multi-Agent, Sampling-Based Motion Planning
Links : [Published]

BibTex

@inproceedings{rodriguez2010behavior,
title={Behavior-based evacuation planning},
author={Rodriguez, Samuel and Amato, Nancy M},
booktitle={2010 IEEE International Conference on Robotics and Automation},
pages={350--355},
year={2010},
organization={IEEE}
}


Abstract

In this work, we present a formulation of an evacuation planning problem that is inspired by motion planning and describe an integrated behavioral agent-based and roadmap-based motion planning approach to solve it. Our formulation allows users to test the effect on evacuation of a number of different environmental factors. One of our main focuses is to provide a mechanism to investigate how the interaction between agents influences the resulting evacuation plans. Specifically, we explore how various types of control provided by a set of directing agents effects the overall evacuation planning strategies of the evacuating agents.


A Motion Planning Approach to Studying Molecular Motions, Lydia Tapia, Shawna Thomas, Nancy M. Amato, Communications in Information and Systems, Vol: 10, Issue: 1, pp. 53-68, Jan 2010. DOI: 10.4310/cis.2010.v10.n1.a4
Keywords: Computational Biology, Motion Planning, Protein Folding
Links : [Published]

BibTex

@article{tapia-cis-2010-j
, author = "Lydia Tapia and Shawna Thomas and Nancy M. Amato"
, title = "A Motion Planning Approach to Studying Molecular Motions"
, journal = "Communications in Information and Systems"
, volume = "10"
, issue = "1"
, year = "2010"
, pages = "52-68"
, doi = "10.4310/CIS.2010.v10.n1.a4"
, note = "Special Issue in honor of Michael Waterman. Open Access. https://projecteuclid.org/euclid.cis/1268143373"
}


Abstract

While structurally very different, protein and RNA molecules share an important attribute. The motions they undergo are strongly related to the function they perform. For example, many diseases such as Mad Cow disease or Alzheimer’s disease are associated with protein misfolding and aggregation. Similarly, RNA folding velocity may regulate the plasmid copy number, and RNA folding kinetics can regulate gene expression at the translational level. Knowledge of the stability, folding, kinetics and detailed mechanics of the folding process may help provide insight into how proteins and RNAs fold. In this paper, we present an overview of our work with a computational method we have adapted from robotic motion planning to study molecular motions. We have validated against experimental data and have demonstrated that our method can capture biological results such as stochastic folding pathways, population kinetics of various conformations, and relative folding rates. Thus, our method provides both a detailed view (e.g., individual pathways) and a global view (e.g., population kinetics, relative folding rates, and reaction coordinates) of energy landscapes of both proteins and RNAs. We have validated these techniques by showing that we observe the same relative folding rates as shown in experiments for structurally similar protein molecules that exhibit different folding behaviors. Our analysis has also been able to predict the same relative gene expression rate for wild-type MS2 phage RNA and three of its mutants.


The STAPL pList, Gabriel Tanase, Xiabing Xu, Antal Buss, Harshvardhan, Ioannis Papadopoulos, Olga Tkachyshyn, Timmie Smith, Nathan Thomas, Mauro Bianco, Nancy M. Amato, Lawrence Rauchwerger, Proceedings of the 22nd International Conference on Languages and Compilers for Parallel Computing, pp. 16-30, Newark, DE, USA, Oct 2009. DOI: 10.1007/978-3-642-13374-9_2
Keywords: Parallel Containers, Parallel Programming, STAPL
Links : [Published]

BibTex

@inproceedings{10.1007/978-3-642-13374-9_2,
author = {Tanase, Gabriel and Xu, Xiabing and Buss, Antal and Harshvardhan and Papadopoulos, Ioannis and Pearce, Olga and Smith, Timmie and Thomas, Nathan and Bianco, Mauro and Amato, Nancy M. and Rauchwerger, Lawrence},
title = {The STAPL Plist},
year = {2009},
isbn = {3642133738},
publisher = {Springer-Verlag},
address = {Berlin, Heidelberg},
url = {https://doi.org/10.1007/978-3-642-13374-9_2},
doi = {10.1007/978-3-642-13374-9_2},
abstract = {We present the design and implementation of the staplpList, a parallel container that has the properties of a sequential list, but allows for scalable concurrent access when used in a parallel program. The Standard Template Adaptive Parallel Library (stapl) is a parallel programming library that extends C++ with support for parallelism. stapl provides a collection of distributed data structures (pContainers) and parallel algorithms (pAlgorithms) and a generic methodology for extending them to provide customized functionality. staplpContainers are thread-safe, concurrent objects, providing appropriate interfaces (e.g., views) that can be used by generic pAlgorithms. The pList provides stl equivalent methods, such as insert, erase, and splice, additional methods such as split, and efficient asynchronous (non-blocking) variants of some methods for improved parallel performance. We evaluate the performance of the staplpList on an IBM Power 5 cluster and on a CRAY XT4 massively parallel processing system. Although lists are generally not considered good data structures for parallel processing, we show that pList methods and pAlgorithms (p_generate and p_partial_sum) operating on pLists provide good scalability on more than 103 processors and that pList compares favorably with other dynamic data structures such as the pVector.},
booktitle = {Proceedings of the 22nd International Conference on Languages and Compilers for Parallel Computing},
pages = {16–30},
numpages = {15},
location = {Newark, DE},
series = {LCPC\'09}
}


Abstract

We present the design and implementation of the staplpList, a parallel container that has the properties of a sequential list, but allows for scalable concurrent access when used in a parallel program. The Standard Template Adaptive Parallel Library (stapl) is a parallel programming library that extends C++ with support for parallelism. stapl provides a collection of distributed data structures (pContainers) and parallel algorithms (pAlgorithms) and a generic methodology for extending them to provide customized functionality. stapl pContainers are thread-safe, concurrent objects, providing appropriate interfaces (e.g., views) that can be used by generic pAlgorithms. The pList provides stl equivalent methods, such as insert, erase, and splice, additional methods such as split, and efficient asynchronous (non-blocking) variants of some methods for improved parallel performance. We evaluate the performance of the stapl pList on an IBM Power 5 cluster and on a CRAY XT4 massively parallel processing system. Although lists are generally not considered good data structures for parallel processing, we show that pList methods and pAlgorithms (p_generate and p_partial_sum) operating on pLists provide good scalability on more than 1000 processors and that pList compares favorably with other dynamic data structures such as the pVector.


Design for Interoperability in STAPL: pMatrices and Linear Algebra Algorithms, Antal Buss, Timmie Smith, Gabriel Tanase, Nathan Thomas, Mauro Bianco, Nancy M. Amato, Lawrence Rauchwerger, Languages and Compilers for Parallel Computing (LCPC 2008). Lecture Notes in Computer Science, vol 5335. Springer, pp. 304-315, Edmonton, Alberta, Canada, Jul 2008. DOI: 10.1007/978-3-540-89740-8_21
Keywords: Parallel Containers, Parallel Programming, STAPL
Links : [Published]

BibTex

@InProceedings{10.1007/978-3-540-89740-8_21,
author=\"Buss, Antal A.
and Smith, Timmie G.
and Tanase, Gabriel
and Thomas, Nathan L.
and Bianco, Mauro
and Amato, Nancy M.
and Rauchwerger, Lawrence\",
editor=\"Amaral, Jos{\\\'e} Nelson\",
title=\"Design for Interoperability in stapl: pMatrices and Linear Algebra Algorithms\",
booktitle=\"Languages and Compilers for Parallel Computing\",
year=\"2008\",
publisher=\"Springer Berlin Heidelberg\",
address=\"Berlin, Heidelberg\",
pages=\"304--315\",
abstract=\"The Standard Template Adaptive Parallel Library (stapl) is a high-productivity parallel programming framework that extends C++ and stl with unified support for shared and distributed memory parallelism. stapl provides distributed data structures (pContainers) and parallel algorithms (pAlgorithms) and a generic methodology for extending them to provide customized functionality. To improve productivity and performance, it is essential for stapl to exploit third party libraries, including those developed in programming languages other than C++. In this paper we describe a methodology that enables third party libraries to be used with stapl. This methodology allows a developer to specify when these specialized libraries can correctly be used, and provides mechanisms to transparently invoke them when appropriate. It also provides support for using staplpAlgorithms and pContainers in external codes. As a concrete example, we illustrate how third party libraries, namely BLAS and PBLAS, can be transparently embedded into stapl to provide efficient linear algebra algorithms for the staplpMatrix, with negligible slowdown with respect to the optimized libraries themselves.\",
isbn=\"978-3-540-89740-8\"
}


Abstract

The Standard Template Adaptive Parallel Library (stapl) is a high-productivity parallel programming framework that extends C++ and stl with unified support for shared and distributed memory parallelism. stapl provides distributed data structures (pContainers) and parallel algorithms (pAlgorithms) and a generic methodology for extending them to provide customized functionality. To improve productivity and performance, it is essential for stapl to exploit third party libraries, including those developed in programming languages other than C++. In this paper we describe a methodology that enables third party libraries to be used with stapl. This methodology allows a developer to specify when these specialized libraries can correctly be used, and provides mechanisms to transparently invoke them when appropriate. It also provides support for using stapl pAlgorithms and pContainers in external codes. As a concrete example, we illustrate how third party libraries, namely BLAS and PBLAS, can be transparently embedded into stapl to provide efficient linear algebra algorithms for the stapl pMatrix, with negligible slowdown with respect to the optimized libraries themselves.


RESAMPL: A Region-Sensitive Adaptive Motion Planner, Samuel Rodriguez, Shawna Thomas, Roger Pearce, Nancy M. Amato, Algorithmic Foundation of Robotics VII , pp. 285-300, N/A, Jan 2008. DOI: 10.1007/978-3-540-68405-3_18
Keywords: Machine Learning, Sampling-Based Motion Planning
Links : [Published]

BibTex

@Inbook{Rodriguez2008,
author="Rodriguez, Samuel
and Thomas, Shawna
and Pearce, Roger
and Amato, Nancy M.",
editor="Akella, Srinivas
and Amato, Nancy M.
and Huang, Wesley H.
and Mishra, Bud",
title="RESAMPL: A Region-Sensitive Adaptive Motion Planner",
bookTitle="Algorithmic Foundation of Robotics VII: Selected Contributions of the Seventh International Workshop on the Algorithmic Foundations of Robotics",
year="2008",
publisher="Springer Berlin Heidelberg",
address="Berlin, Heidelberg",
pages="285--300",
isbn="978-3-540-68405-3",
doi="10.1007/978-3-540-68405-3_18",
url="https://doi.org/10.1007/978-3-540-68405-3_18"
}


Abstract

Automatic motion planning has applications ranging from traditional robotics to computer-aided design to computational biology and chemistry. While randomized planners, such as probabilistic roadmap methods (PRMs) or rapidly-exploring random trees (RRT), have been highly successful in solving many high degree of freedom problems, there are still many scenarios in which we need better methods, e.g., problems involving narrow passages or which contain multiple regions that are best suited to different planners. In this work, we present RESAMPL, a motion planning strategy that uses local region information to make intelligent decisions about how and where to sample, which samples to connect together, and to find paths through the environment. Briefly, RESAMPL classifies regions based on the entropy of the samples in it, and then uses these classifications to further refine the sampling. Regions are placed in a region graph that encodes relationships between regions, e.g., edges correspond to overlapping regions. The strategy for connecting samples is guided by the region graph, and can be exploited in both multi-query and single-query scenarios. Our experimental results comparing RESAMPL to previous multi-query and single-query methods show that RESAMPL is generally significantly faster and also usually requires fewer samples to solve the problem.


Incremental Map Generation (IMG), Dawen Xie, Marco Morales, Roger Pearce, Shawna Thomas, Jyh-Ming Lien, Nancy M. Amato, Algorithmic Foundation of Robotics VII, N/A, Jan 2008. DOI: 10.1007/978-3-540-68405-3_4
Keywords: Machine Learning, Sampling-Based Motion Planning
Links : [Published]

BibTex

@Inbook{Xie2008,
author="Xie, Dawen
and Morales, Marco
and Pearce, Roger
and Thomas, Shawna
and Lien, Jyh-Ming
and Amato, Nancy M.",
editor="Akella, Srinivas
and Amato, Nancy M.
and Huang, Wesley H.
and Mishra, Bud",
title="Incremental Map Generation (IMG)",
bookTitle="Algorithmic Foundation of Robotics VII: Selected Contributions of the Seventh International Workshop on the Algorithmic Foundations of Robotics",
year="2008",
publisher="Springer Berlin Heidelberg",
address="Berlin, Heidelberg",
pages="53--68",
isbn="978-3-540-68405-3",
doi="10.1007/978-3-540-68405-3_4",
url="https://doi.org/10.1007/978-3-540-68405-3_4"
}


Abstract

Probabilistic roadmap methods (prms) have been highly successful in solving many high degree of freedom motion planning problems arising in diverse application domains such as traditional robotics, computer-aided design, and computational biology and chemistry. One important practical issue with prms is that they do not provide an automated mechanism to determine how large a roadmap is needed for a given problem. Instead, users typically determine this by trial and error and as a consequence often construct larger roadmaps than are needed. In this paper, we propose a new prm-based framework called Incremental Map Generation (img) to address this problem. Our strategy is to break the map generation into several processes, each of which generates samples and connections, and to continue adding the next increment of samples and connections to the evolving roadmap until it stops improving. In particular, the process continues until a set of evaluation criteria determine that the planning strategy is no longer effective at improving the roadmap. We propose some general evaluation criteria and show how to apply them to construct different types of roadmaps, e.g., roadmaps that coarsely or more finely map the space. In addition, we show how img can be integrated with previously proposed adaptive strategies for selecting sampling methods. We provide results illustrating the power of img.


Associative Parallel Containers In STAPL, Gabriel Tanase, Chidambareswaran (Chids) Raman, Mauro Bianco, Nancy M. Amato, Lawrence Rauchwerger, Languages and Compilers for Parallel Computing (LCPC), pp. 156-171, Urbana, IL, USA, Oct 2007. DOI: 10.1007/978-3-540-85261-2_11
Keywords: Parallel Containers, Parallel Programming, STAPL
Links : [Published]

BibTex

@InProceedings{10.1007/978-3-540-85261-2_11,
author=\"Tanase, Gabriel
and Raman, Chidambareswaran
and Bianco, Mauro
and Amato, Nancy M.
and Rauchwerger, Lawrence\",
editor=\"Adve, Vikram
and Garzar{\\\'a}n, Mar{\\\'i}a Jes{\\\'u}s
and Petersen, Paul\",
title=\"Associative Parallel Containers in STAPL\",
booktitle=\"Languages and Compilers for Parallel Computing\",
year=\"2008\",
publisher=\"Springer Berlin Heidelberg\",
address=\"Berlin, Heidelberg\",
pages=\"156--171\",
abstract=\"The Standard Template Adaptive Parallel Library (stapl) is a parallel programming framework that extends C++ and stl with support for parallelism. stapl provides a collection of parallel data structures (pContainers) and algorithms (pAlgorithms) and a generic methodology for extending them to provide customized functionality. staplpContainers are thread-safe, concurrent objects, i.e., shared objects that provide parallel methods that can be invoked concurrently. They also provide appropriate interfaces that can be used by generic pAlgorithms. In this work, we present the design and implementation of the stapl associative pContainers: pMap, pSet, pMultiMap, pMultiSet, pHashMap, and pHashSet. These containers provide optimal insert, search, and delete operations for a distributed collection of elements based on keys. Their methods include counterparts of the methods provided by the stl associative containers, and also some asynchronous (non-blocking) variants that can provide improved performance in parallel. We evaluate the performance of the stapl associative pContainers on an IBM Power5 cluster, an IBM Power3 cluster, and on a linux-based Opteron cluster, and show that the new pContainer asynchronous methods, generic pAlgorithms (e.g., pfind) and a sort application based on associative pContainers, all provide good scalability on more than 1000 processors.\",
isbn=\"978-3-540-85261-2\"
}


Abstract

The Standard Template Adaptive Parallel Library (stapl) is a parallel programming framework that extends C++ and stl with support for parallelism. stapl provides a collection of parallel data structures (pContainers) and algorithms (pAlgorithms) and a generic methodology for extending them to provide customized functionality. stapl pContainers are thread-safe, concurrent objects, i.e., shared objects that provide parallel methods that can be invoked concurrently. They also provide appropriate interfaces that can be used by generic pAlgorithms. In this work, we present the design and implementation of the stapl associative pContainers: pMap, pSet, pMultiMap, pMultiSet, pHashMap, and pHashSet. These containers provide optimal insert, search, and delete operations for a distributed collection of elements based on keys. Their methods include counterparts of the methods provided by the stl associative containers, and also some asynchronous (non-blocking) variants that can provide improved performance in parallel. We evaluate the performance of the stapl associative pContainers on an IBM Power5 cluster, an IBM Power3 cluster, and on a linux-based Opteron cluster, and show that the new pContainer asynchronous methods, generic pAlgorithms (e.g., pfind) and a sort application based on associative pContainers, all provide good scalability on more than 1000 processors.


The STAPL pArray, Gabriel Tanase, Mauro Bianco, Nancy M. Amato, Lawrence Rauchwerger, Proceedings of the 2007 Workshop on MEmory Performance: DEaling with Applications, Systems and Architecture (MEDEA), pp. 73-80, Brasov, Romania, Sep 2007. DOI: 10.1145/1327171.1327180
Keywords: Parallel Containers, Parallel Programming, STAPL
Links : [Published]

BibTex

@inproceedings{10.1145/1327171.1327180,
author = {Tanase, Gabriel and Bianco, Mauro and Amato, Nancy M. and Rauchwerger, Lawrence},
title = {The STAPL PArray},
year = {2007},
isbn = {9781595938077},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1327171.1327180},
doi = {10.1145/1327171.1327180},
abstract = {The Standard Template Adaptive Parallel Library (STAPL) is a parallel programming framework that extends C++ and STL with support for parallelism. STAPL provides parallel data structures (pContainers) and generic parallel algorithms (pAlgorithms), and a methodology for extending them to provide customized functionality. STAPL pContainers are thread-safe, concurrent objects, i.e., shared objects that provide parallel methods that can be invoked concurrently. They provide views as a generic means to access data that can be passed as input to generic pAlgorithms.In this work, we present the STAPL pArray, the parallel equivalent of the sequential STL valarray, a fixed-size data structure optimized for storing and accessing data based on one-dimensional indices. We describe the pArray design and show how it can support a variety of underlying data distribution policies currently available in STAPL, such as blocked or blocked cyclic. We provide experimental results showing that pAlgorithms using the pArray scale well to more than 2,000 processors. We also provide results using different data distributions that illustrate that the performance of pAlgorithms and pArray methods is usually sensitive to the underlying data distribution, and moreover, that there is no one data distribution that performs best for all pAlgorithms, processor counts, or machines.},
booktitle = {Proceedings of the 2007 Workshop on MEmory Performance: DEaling with Applications, Systems and Architecture},
pages = {73–80},
numpages = {8},
location = {Brasov, Romania},
series = {MEDEA \'07}
}


Abstract

The Standard Template Adaptive Parallel Library (STAPL) is a parallel programming framework that extends C++ and STL with support for parallelism. STAPL provides parallel data structures (pContainers) and generic parallel algorithms (pAlgorithms), and a methodology for extending them to provide customized functionality. STAPL pContainers are thread-safe, concurrent objects, i.e., shared objects that provide parallel methods that can be invoked concurrently. They provide views as a generic means to access data that can be passed as input to generic pAlgorithms. In this work, we present the STAPL pArray, the parallel equivalent of the sequential STL valarray, a fixed-size data structure optimized for storing and accessing data based on one-dimensional indices. We describe the pArray design and show how it can support a variety of underlying data distribution policies currently available in STAPL, such as blocked or blocked cyclic. We provide experimental results showing that pAlgorithms using the pArray scale well to more than 2,000 processors. We also provide results using different data distributions that illustrate that the performance of pAlgorithms and pArray methods is usually sensitive to the underlying data distribution, and moreover, that there is no one data distribution that performs best for all pAlgorithms, processor counts, or machines.


Kinetics Analysis Methods for Approximate Folding Landscapes , Lydia Tapia, Xinyu Tang, Shawna Thomas, Nancy M. Amato, Bioinformatics, Vol: Volume 23, Issue: Issue 13, pp. i539- i548, Jul 2007. DOI: 10.1093/bioinformatics/btm199
Keywords: Protein Folding, Sampling-Based Motion Planning
Links : [Published]

BibTex

@article{tapia2007kinetics,
title={Kinetics analysis methods for approximate folding landscapes},
author={Tapia, Lydia and Tang, Xinyu and Thomas, Shawna and Amato, Nancy M},
journal={Bioinformatics},
volume={23},
number={13},
pages={i539--i548},
year={2007},
publisher={Oxford University Press}
}


Abstract

Motivation: Protein motions play an essential role in many biochemical processes. Lab studies often quantify these motions in terms of their kinetics such as the speed at which a protein folds or the population of certain interesting states like the native state. Kinetic metrics give quantifiable measurements of the folding process that can be compared across a group of proteins such as a wild-type protein and its mutants. Results: We present two new techniques, map-based master equation solution and map-based Monte Carlo simulation, to study protein kinetics through folding rates and population kinetics from approximate folding landscapes, models called maps. From these two new techniques, interesting metrics that describe the folding process, such as reaction coordinates, can also be studied. In this article we focus on two metrics, formation of helices and structure formation around tryptophan residues. These two metrics are often studied in the lab through circular dichroism (CD) spectra analysis and tryptophan fluorescence experiments, respectively. The approximated landscape models we use here are the maps of protein conformations and their associated transitions that we have presented and validated previously. In contrast to other methods such as the traditional master equation and Monte Carlo simulation, our techniques are both fast and can easily be computed for full-length detailed protein models. We validate our map-based kinetics techniques by comparing folding rates to known experimental results. We also look in depth at the population kinetics, helix formation and structure near tryptophan residues for a variety of proteins.


Approximate Convex Decomposition of Polyhedra, Jyh-Ming Lien, Nancy M. Amato, Proceedings of the 2007 ACM Symposium on Solid and Physical Modeling (SPM 07), pp. 121-131, Jun 2007. DOI: https://doi.org/10.1145/1236246.1236265
Keywords: Computational Geometry, Convex Decomposition
Links : [Published]

BibTex

@inproceedings{10.1145/1236246.1236265,
author = {Lien, Jyh-Ming and Amato, Nancy M.},
title = {Approximate Convex Decomposition of Polyhedra},
year = {2007},
isbn = {9781595936660},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1236246.1236265},
doi = {10.1145/1236246.1236265},
abstract = {Decomposition is a technique commonly used to partition complex models into simpler components. While decomposition into convex components results in pieces that are easy to process, such decompositions can be costly to construct and can result in representations with an unmanageable number of components. In this paper we explore an alternative partitioning strategy that decomposes a given model into "approximately convex" pieces that may provide similar benefits as convex components, while the resulting decomposition is both significantly smaller (typically by orders of magnitude) and can be computed more efficiently. Indeed, for many applications, an approximate convex decomposition (ACD) can more accurately represent the important structural features of the model by providing a mechanism for ignoring less significant features, such as surface texture. We describe a technique for computing ACDs of three-dimensional polyhedral solids and surfaces of arbitrary genus. We provide results illustrating that our approach results in high quality decompositions with very few components and applications showing that comparable or better results can be obtained using ACD decompositions in place of exact convex decompositions (ECD) that are several orders of magnitude larger.},
booktitle = {Proceedings of the 2007 ACM Symposium on Solid and Physical Modeling},
pages = {121–131},
numpages = {11},
keywords = {concavity measurement, convex decomposition},
location = {Beijing, China},
series = {SPM '07}
}


Abstract

Decomposition is a technique commonly used to partition complex models into simpler components. While decomposition into convex components results in pieces that are easy to process, such decompositions can be costly to construct and can result in representations with an unmanageable number of components. In this paper we explore an alternative partitioning strategy that decomposes a given model into "approximately convex" pieces that may provide similar benefits as convex components, while the resulting decomposition is both significantly smaller (typically by orders of magnitude) and can be computed more efficiently. Indeed, for many applications, an approximate convex decomposition (ACD) can more accurately represent the important structural features of the model by providing a mechanism for ignoring less significant features, such as surface texture. We describe a technique for computing ACDs of three-dimensional polyhedral solids and surfaces of arbitrary genus. We provide results illustrating that our approach results in high quality decompositions with very few components and applications showing that comparable or better results can be obtained using ACD decompositions in place of exact convex decompositions (ECD) that are several orders of magnitude larger.


Analysis of the Evolution of C-Space Models Built through Incremental Exploration, Marco Morales, Roger Pearce, Nancy M. Amato, in Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), pp. 1029-1034, Rome, Italy, Apr 2007. DOI: 10.1109/ROBOT.2007.363120
Keywords: Mobile Robots, Motion Planning, Sampling-Based Motion Planning
Links : [Published]

BibTex

@INPROCEEDINGS{4209224,
author={Morales, Marco and Pearce, Roger and Amato, Nancy M.},
booktitle={Proceedings 2007 IEEE International Conference on Robotics and Automation},
title={Analysis of the Evolution of C-Space Models built through Incremental Exploration},
year={2007},
volume={},
number={},
pages={1029-1034},
doi={10.1109/ROBOT.2007.363120}}


Abstract

Many sampling methods for motion planning explore the robot's configuration space (C-space) starting from a set of configuration(s) and incrementally explore surrounding areas to produce a growing model of the space. Although there is a common understanding of the strengths and weaknesses of these techniques, metrics for analyzing the incremental exploration process and for evaluating the performance of incremental samplers have been lacking. We propose the use of local metrics that provide insight into the complexity of the different regions in the model and global metrics that describe the process as a whole. These metrics only require local information and can be efficiently computed. We illustrate the use of our proposed metrics to analyze representative incremental strategies including the rapidly-exploring random trees, expansive space trees, and the original randomized path planner. We show how these metrics model the efficiency of C-space exploration and help to identify different modeling stages. In addition, these metrics are ideal for adapting space exploration to improve performance.


Biasing Samplers to Improve Motion Planning Performance, Shawna Thomas, Marco Morales, Xinyu Tang, Nancy M. Amato, In Proc. IEEE International Conference on Robotics and Automation (ICRA), Roma, Italy, Apr 2007. DOI: 10.1109/ROBOT.2007.363556
Keywords: Machine Learning, Sampling-Based Motion Planning
Links : [Published]

BibTex

@INPROCEEDINGS{4209320,

author={S. {Thomas} and M. {Morales} and X. {Tang} and N. M. {Amato}},

booktitle={Proceedings 2007 IEEE International Conference on Robotics and Automation},

title={Biasing Samplers to Improve Motion Planning Performance},

year={2007},

volume={},

number={},

pages={1625-1630},

doi={10.1109/ROBOT.2007.363556}}


Abstract

With the success of randomized sampling-based motion planners such as probabilistic roadmap methods, much work has been done to design new sampling techniques and distributions. To date, there is no sampling technique that outperforms all other techniques for all motion planning problems. Instead, each proposed technique has different strengths and weaknesses. However, little work has been done to combine these techniques to create new distributions. In this paper, we propose to bias one sampling distribution with another such that the resulting distribution out-performs either of its parent distributions. We present a general framework for biasing samplers that is easily extendable to new distributions and can handle an arbitrary number of parent distributions by chaining them together. Our experimental results show that by combining distributions, we can out-perform existing planners. Our results also indicate that not one single distribution combination performs the best in all problems, and we identify which perform better for the specific application domains studied.


Planning with Reachable Distances: Fast Enforcement of Closure Constraints, Xinyu Tang, Shawna Thomas, Nancy M. Amato, In Proc. IEEE International Conference on Robotics and Automation (ICRA), Roma, Italy, Apr 2007. DOI: 10.1109/ROBOT.2007.363872
Keywords: Manipulation Planning, Reachability Analysis, Sampling-Based Motion Planning
Links : [Published]

BibTex

@INPROCEEDINGS{4209490, author={X. {Tang} and S. {Thomas} and N. M. {Amato}}, booktitle={Proceedings 2007 IEEE International Conference on Robotics and Automation}, title={Planning with Reachable Distances: Fast Enforcement of Closure Constraints}, year={2007}, volume={}, number={}, pages={2694-2699}, doi={10.1109/ROBOT.2007.363872}}


Abstract

Motion planning for closed-chain systems is particularly difficult due to additional closure constraints placed on the system. In fact, the probability of randomly selecting a set of joint angles that satisfy the closure constraints is zero. We propose planning with reachable distance (PRD) to overcome this challenge by first precomputing the subspace satisfying the closure constraints, then directly sampling in it. To do so, we represent the chain as a hierarchy of sub-chains. Then we calculate the "closure" sub-space as appropriate reachable distance ranges of sub-chains satisfying the closure constraints. This provides two distinct advantages over traditional approaches: (1) configurations are quickly sampled and converted to joint angles using basic trigonometry functions instead of more expensive inverse kinematics solvers, and (2) configurations are guaranteed to be closed. In this paper, we describe this hierarchical chain representation and give a sampling algorithm with complexity linear in the number of links. We provide the necessary motion planning primitives for most sampling-based motion planners. Our experimental results show our method is fast, making sampling closed configurations comparable to sampling open chain configurations that ignore closure constraints. Our method is general, easy to implement, and also extends to other distance-related constraints besides the ones demonstrated here


Tools for Simulating and Analyzing RNA Folding Kinetics, Xinyu Tang, Shawna Thomas, Lydia Tapia, Nancy M. Amato, In. Proc. International Conference on Research in Computational Molecular Biology, Oaklanda, California, USA, Apr 2007. DOI: 10.1007/978-3-540-71681-5_19
Keywords: Computational Biology, RNA, Sampling-Based Motion Planning
Links : [Published]

BibTex

@InProceedings{10.1007/978-3-540-71681-5_19,
author="Tang, Xinyu
and Thomas, Shawna
and Tapia, Lydia
and Amato, Nancy M.",
editor="Speed, Terry
and Huang, Haiyan",
title="Tools for Simulating and Analyzing RNA Folding Kinetics",
booktitle="Research in Computational Molecular Biology",
year="2007",
publisher="Springer Berlin Heidelberg",
address="Berlin, Heidelberg",
pages="268--282",
isbn="978-3-540-71681-5"
}


Abstract

It has recently been found that some RNA functions are determined by the actual folding kinetics and not just the RNA’s nucleotide sequence or its native structure. We present new computational tools for simulating and analyzing RNA folding kinetic metrics such as population kinetics, folding rates, and the folding of particular subsequences. Our method first builds an approximate representation (called a map) of the RNA’s folding energy landscape, and then uses specialized analysis techniques to extract folding kinetics from the map. We provide a new sampling strategy called Probabilistic Boltzmann Sampling (PBS) that enables us to approximate the folding landscape with much smaller maps, typically by several orders of magnitude. We also describe a new analysis technique, Map-based Monte Carlo (MMC) simulation, to stochastically extract folding pathways from the map. We demonstrate that our technique can be applied to large RNA (e.g., 200+ nucleotides), where representing the full landscape is infeasible, and that our tools provide results comparable to other simulation methods that work on complete energy landscapes. We present results showing that our approach computes the same relative functional rates as seen in experiments for the relative plasmid replication rates of ColE1 RNAII and its mutants, and for the relative gene expression rates of MS2 phage RNA and its mutants.


Simultaneous Shape Decomposition and Skeletonization, Jyh-Ming Lien, John Keyser , Nancy M. Amato, In. Proc. of the 2006 ACM symposium on Solid and physical modeling, Cardiff, Wales, United Kingdom, Jun 2006. DOI: 10.1145/1128888.1128919
Keywords: Computational Geometry, Convex Decomposition
Links : [Published]

BibTex

@inproceedings{10.1145/1128888.1128919,
author = {Lien, Jyh-Ming and Keyser, John and Amato, Nancy M.},
title = {Simultaneous Shape Decomposition and Skeletonization},
year = {2006},
isbn = {1595933581},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1128888.1128919},
doi = {10.1145/1128888.1128919},
booktitle = {Proceedings of the 2006 ACM Symposium on Solid and Physical Modeling},
pages = {219–228},
numpages = {10},
keywords = {skeletonization, convex decomposition, multi-resolution skeleton},
location = {Cardiff, Wales, United Kingdom},
series = {SPM '06}
}


Abstract

Shape decomposition and skeletonization share many common properties and applications. However, they are generally treated as independent computations. In this paper, we propose an iterative approach that simultaneously generates a hierarchical shape decomposition and a corresponding set of multi-resolution skeletons. In our method, a skeleton of a model is extracted from the components of its decomposition --- that is, both processes and the qualities of their results are interdependent. In particular, if the quality of the extracted skeleton does not meet some user specified criteria, then the model is decomposed into finer components and a new skeleton is extracted from these components. The process of simultaneous shape decomposition and skeletonization iterates until the quality of the skeleton becomes satisfactory. We provide evidence that the proposed framework is efficient and robust under perturbation and. deformation. We also demonstrate that our results can readily be used in problems including skeletal deformations and virtual reality navigation.


Metrics for Analyzing the Evolution of C-Space Models, Marco A. Morales, Roger Pearce, Nancy M. Amato, in Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), pp. 1268-1273, Orlando, Florida, USA, May 2006. DOI: 10.1109/ROBOT.2006.1641883
Keywords: Algorithms, Motion Planning
Links : [Published]

BibTex

@INPROCEEDINGS{1641883,
author={Morales A, M.A. and Pearce, R. and Amato, N.M.},
booktitle={Proceedings 2006 IEEE International Conference on Robotics and Automation, 2006. ICRA 2006.},
title={Metrics for analyzing the evolution of C-space models},
year={2006},
volume={},
number={},
pages={1268-1273},
doi={10.1109/ROBOT.2006.1641883}}


Abstract

There are many sampling-based motion planning methods that model the connectivity of a robot's configuration space (C-space) with a graph whose nodes are valid configurations and whose edges represent valid transitions between nodes. One of the biggest challenges faced by users of these methods is selecting the right planner for their problem. While researchers have tried to compare different planners, most accepted metrics for comparing planners are based on efficiency, e.g., number of collision detection calls or samples needed to solve a particular set of queries, and there is still a lack of useful and efficient quantitative metrics that can be used to measure the suitability of a planner for solving a problem. That is, although there is great interest in determining which planners should be used in which situations, there are still many questions we cannot answer about the relative performance of different planning methods. In this paper we make some progress towards this goal. We propose a metric that can be applied to each new sample considered by a sampling-based planner to characterize how that sample improves, or not, the planner's current C-space model. This characterization requires only local information and can be computed quite efficiently, so that it can be applied to every sample. We show how this characterization can be used to analyze and compare how different planning strategies explore the configuration space. In particular, we show that it can be used to identify three phases that planners go through when building C-space models: quick learning (rapidly building a coarse model), model enhancement (refining the model), and learning decay (oversampling - most samples do not provide additional information). Hence, our work can also provide the basis for determining when a particular planning strategy has 'converged' on the best C-space model that it is capable of building.


VIZMO++: a visualization, authoring, and educational tool for motion planning, Aimée Vargas E., Jyh-Ming Lien, Nancy M. Amato, In Proc. IEEE Int. Conf. Robot. Autom. (ICRA), pp. 727-732, Orlando, Florida, USA, May 2006. DOI: 10.1109/ROBOT.2006.1641796
Keywords: Sampling-Based Motion Planning, Visualization
Links : [Published]

BibTex

@INPROCEEDINGS{1641796,
author={A. V. {Estrada} and {Jyh-Ming Lien} and N. M. {Amato}},
booktitle={Proceedings 2006 IEEE International Conference on Robotics and Automation, 2006. ICRA 2006.},
title={VIZMO++: a visualization, authoring, and educational tool for motion planning},
year={2006},
volume={},
number={},
pages={727-732},
doi={10.1109/ROBOT.2006.1641796}}


Abstract

Comprehension of concepts and algorithms involved in the robotics field can be improved through the use of an interactive visualization tool. In this paper we present an interactive tool for visualizing and editing motion planning environments, problem instances, and their solutions. Teachers can take advantage of visualization tools to help their students to better understand motion planning and its complexity as well as the different strategies that have been developed to solve the motion planning problem. While the tool we present allows the animation, manipulation, and evaluation of solution paths found by any motion planner, it is specialized for sampling-based randomized planners such as probabilistic roadmap (PRM) and rapidly-exploring random tree (RRT) methods.


Planning Motion in Completely Deformable Environments, Samuel Rodriguez, Jyh-Ming Lien, Nancy M. Amato, In Proc. IEEE International Conference on Robotics and Automation (ICRA), Orlando, Florida, USA, May 2006. DOI: 10.1109/ROBOT.2006.1642072
Keywords: Deformable Objects, Kinodynamic Planning, Sampling-Based Motion Planning
Links : [Published]

BibTex

@INPROCEEDINGS{1642072, author={S. {Rodriguez} and {Jyh-Ming Lien} and N. M. {Amato}}, booktitle={Proceedings 2006 IEEE International Conference on Robotics and Automation, 2006. ICRA 2006.}, title={Planning motion in completely deformable environments}, year={2006}, volume={}, number={}, pages={2466-2471}, doi={10.1109/ROBOT.2006.1642072}}


Abstract

Though motion planning has been studied extensively for rigid and articulated robots, motion planning for deformable objects is an area that has received far less attention. In this paper we present a framework for planning paths in completely deformable, elastic environments. We apply a deformable model to the robot and obstacles in the environment and present a kinodynamic planning algorithm suited for this type of deformable motion planning. The planning algorithm is based on the rapidly-exploring random tree (RRT) path planning algorithm. To the best of our knowledge, this is the first work that plans paths in totally deformable environments


An Obstacle-Based Rapidly-Exploring Random Tree, Samuel Rodriguez, Xinyu Tang, Jyh-Ming Lien, Nancy M. Amato, In Proc. IEEE International Conference on Robotics and Automation (ICRA), Orlando, Florida, USA, May 2006. DOI: 10.1109/ROBOT.2006.1641823
Keywords: obstacle-based, Rapidly-exploring Random Tree (RRT), Sampling-Based Motion Planning
Links : [Published]

BibTex

@INPROCEEDINGS{1641823, author={ {Rodriguez} and {Xinyu Tang} and {Jyh-Ming Lien} and N. M. {Amato}}, booktitle={Proceedings 2006 IEEE International Conference on Robotics and Automation, 2006. ICRA 2006.}, title={An obstacle-based rapidly-exploring random tree}, year={2006}, volume={}, number={}, pages={895-900}, doi={10.1109/ROBOT.2006.1641823}}


Abstract

Tree-based path planners have been shown to be well suited to solve various high dimensional motion planning problems. Here we present a variant of the Rapidly-Exploring Random Tree (RRT) path planning algorithm that is able to explore narrow passages or difficult areas more effectively. We show that both workspace obstacle information and C-space information can be used when deciding which direction to grow. The method includes many ways to grow the tree, some taking into account the obstacles in the environment. This planner works best in difficult areas when planning for free flying rigid or articulated robots. Indeed, whereas the standard RRT can face difficulties planning in a narrow passage, the tree based planner presented here works best in these areas


SmartApps: Middleware for Adaptive Applications on Reconfigurable Platforms, Lawrence Rauchwerger, Nancy Amato, ACM SIGOPS Operating Systems Review, Vol: 40, Issue: 2, pp. 73-82, Apr 2006. DOI: 10.1145/1131322.1131338
Keywords: High-Performance Computing, Memory Management, Runtime Systems
Links : [Published]

BibTex

@article{10.1145/1131322.1131338,
author = {Rauchwerger, Lawrence and Amato, Nancy M.},
title = {SmartApps: Middle-Ware for Adaptive Applications on Reconfigurable Platforms},
year = {2006},
issue_date = {April 2006},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
volume = {40},
number = {2},
issn = {0163-5980},
url = {https://doi.org/10.1145/1131322.1131338},
doi = {10.1145/1131322.1131338},
abstract = {One general avenue to obtain optimized performance on large and complex systems is to approach optimization from a global perspective of the complete system in a customized manner for each application, i.e., application-centric optimization. Lately, there have been encouraging developments in reconfigurable operating systems and hardware that will enable customized optimization. For example, machines built with PIM's and FPGA's can be quickly reconfigured to better fit a certain application and operating systems, such as IBM's K42, can have their services customized to fit the needs and characteristics of an application. While progress in operating system and hardware and hardware has made re-configuration possible, we still need strategies and techniques to exploit them for improved application performance.In this paper, we describe the approach we are using in our smart application (SMARTAPPS) project. In the SMARTAPP executable, the compiler embeds most run-time system services and a feedback loop to monitor performance and trigger run-time adaptations. At run-time, after incorporating the code's input and determining the system's state, the SMARTAPP performs an instance specific optimization. During execution, the application continually monitors its performance and the available resources to determine if restructuring should occur. The framework includes mechanisms for performing the actual restructuring at various levels including: algorithmic adaptation, tuning reconfigurable OS services (scheduling policy, page size, etc.), and system configuration (e.g., number of processors). This paper concentrates on the techniques for providing customized system services for communication, thread scheduling, memory management, and performance monitoring and modeling.},
journal = {SIGOPS Oper. Syst. Rev.},
month = apr,
pages = {73–82},
numpages = {10}
}


Abstract

One general avenue to obtain optimized performance on large and complex systems is to approach optimization from a global perspective of the complete system in a customized manner for each application, i.e., application-centric optimization. Lately, there have been encouraging developments in reconfigurable operating systems and hardware that will enable customized optimization. For example, machines built with PIM's and FPGA's can be quickly reconfigured to better fit a certain application and operating systems, such as IBM's K42, can have their services customized to fit the needs and characteristics of an application. While progress in operating system and hardware and hardware has made re-configuration possible, we still need strategies and techniques to exploit them for improved application performance.In this paper, we describe the approach we are using in our smart application (SMARTAPPS) project. In the SMARTAPP executable, the compiler embeds most run-time system services and a feedback loop to monitor performance and trigger run-time adaptations. At run-time, after incorporating the code's input and determining the system's state, the SMARTAPP performs an instance specific optimization. During execution, the application continually monitors its performance and the available resources to determine if restructuring should occur. The framework includes mechanisms for performing the actual restructuring at various levels including: algorithmic adaptation, tuning reconfigurable OS services (scheduling policy, page size, etc.), and system configuration (e.g., number of processors). This paper concentrates on the techniques for providing customized system services for communication, thread scheduling, memory management, and performance monitoring and modeling.


Parallel Protein Folding with STAPL, Shawna Thomas, Gabriel Tanase, Lucia K. Dale, Jose E. Moreira, Lawrence Rauchwerger, Nancy M. Amato, Concurrency and Computation: Practice and Experience, Vol: 17, Issue: 14, pp. 1643-1656, Dec 2005. DOI: https://doi.org/10.1002/cpe.950
Keywords: Parallel Planning, Protein Folding, STAPL
Links : [Published]

BibTex

@article{https://doi.org/10.1002/cpe.950,
author = {Thomas, Shawna and Tanase, Gabriel and Dale, Lucia K. and Moreira, Jose M. and Rauchwerger, Lawrence and Amato, Nancy M.},
title = {Parallel protein folding with STAPL},
journal = {Concurrency and Computation: Practice and Experience},
volume = {17},
number = {14},
pages = {1643-1656},
keywords = {protein folding, motion planning, parallel libraries, C++},
doi = {https://doi.org/10.1002/cpe.950},
url = {https://onlinelibrary.wiley.com/doi/abs/10.1002/cpe.950},
eprint = {https://onlinelibrary.wiley.com/doi/pdf/10.1002/cpe.950},
abstract = {Abstract The protein-folding problem is a study of how a protein dynamically folds to its so-called native state—an energetically stable, three-dimensional conformation. Understanding this process is of great practical importance since some devastating diseases such as Alzheimer's and bovine spongiform encephalopathy (Mad Cow) are associated with the misfolding of proteins. We have developed a new computational technique for studying protein folding that is based on probabilistic roadmap methods for motion planning. Our technique yields an approximate map of a protein's potential energy landscape that contains thousands of feasible folding pathways. We have validated our method against known experimental results. Other simulation techniques, such as molecular dynamics or Monte Carlo methods, require many orders of magnitude more time to produce a single, partial trajectory. In this paper we report on our experiences parallelizing our method using STAPL (Standard Template Adaptive Parallel Library) that is being developed in the Parasol Lab at Texas A\&M. An efficient parallel version will enable us to study larger proteins with increased accuracy. We demonstrate how STAPL enables portable efficiency across multiple platforms, ranging from small Linux clusters to massively parallel machines such as IBM's BlueGene/L, without user code modification. Copyright © 2005 John Wiley \& Sons, Ltd.},
year = {2005}
}


Abstract

The protein-folding problem is a study of how a protein dynamically folds to its so-called native state - an energetically stable, three-dimensional conformation. Understanding this process is of great practical importance since some devastating diseases such as Alzheimer's and bovine spongiform encephalopathy (Mad Cow) are associated with the misfolding of proteins. We have developed a new computational technique for studying protein folding that is based on probabilistic roadmap methods for motion planning. Our technique yields an approximate map of a protein's potential energy landscape that contains thousands of feasible folding pathways. We have validated our method against known experimental results. Other simulation techniques, such as molecular dynamics or Monte Carlo methods, require many orders of magnitude more time to produce a single, partial trajectory. In this paper we report on our experiences parallelizing our method using STAPL (Standard Template Adaptive Parallel Library) that is being developed in the Parasol Lab at Texas A&M. An efficient parallel version will enable us to study larger proteins with increased accuracy. We demonstrate how STAPL enables portable efficiency across multiple platforms, ranging from small Linux clusters to massively parallel machines such as IBM's BlueGene/L, without user code modification.


Protein Folding by Motion Planning, Shawna Thomas, Guang Song, Nancy M. Amato, Physical Biology, Vol: 2, Issue: 4, pp. S148-S155, Nov 2005. DOI: 10.1088/1478-3975/2/4/S09
Keywords: Protein Folding, Sampling-Based Motion Planning
Links : [Published]

BibTex

@article{Thomas_2005,
doi = {10.1088/1478-3975/2/4/s09},
url = {https://doi.org/10.1088%2F1478-3975%2F2%2F4%2Fs09},
year = 2005,
month = {nov},
publisher = {{IOP} Publishing},
volume = {2},
number = {4},
pages = {S148--S155},
author = {Shawna Thomas and Guang Song and Nancy M Amato},
title = {Protein folding by motion planning},
journal = {Physical Biology},
abstract = {We investigate a novel approach for studying protein folding that has evolved from robotics motion planning techniques called probabilistic roadmap methods (PRMs). Our focus is to study issues related to the folding process, such as the formation of secondary and tertiary structures, assuming we know the native fold. A feature of our PRM-based framework is that the large sets of folding pathways in the roadmaps it produces, in just a few hours on a desktop PC, provide global information about the protein\'s energy landscape. This is an advantage over other simulation methods such as molecular dynamics or Monte Carlo methods which require more computation and produce only a single trajectory in each run. In our initial studies, we obtained encouraging results for several small proteins. In this paper, we investigate more sophisticated techniques for analyzing the folding pathways in our roadmaps. In addition to more formally revalidating our previous results, we present a case study showing that our technique captures known folding differences between the structurally similar proteins G and L.}
}


Abstract

We investigate a novel approach for studying protein folding that has evolved from robotics motion planning techniques called probabilistic roadmap methods (PRMs). Our focus is to study issues related to the folding process, such as the formation of secondary and tertiary structures, assuming we know the native fold. A feature of our PRM-based framework is that the large sets of folding pathways in the roadmaps it produces, in just a few hours on a desktop PC, provide global information about the protein\'s energy landscape. This is an advantage over other simulation methods such as molecular dynamics or Monte Carlo methods which require more computation and produce only a single trajectory in each run. In our initial studies, we obtained encouraging results for several small proteins. In this paper, we investigate more sophisticated techniques for analyzing the folding pathways in our roadmaps. In addition to more formally revalidating our previous results, we present a case study showing that our technique captures known folding differences between the structurally similar proteins G and L.


An Experimental Evaluation of the HP V-Class and SGI Origin 2000 Multiprocessors using Microbenchmarks and Scientific Applications, Ravi Iyer, Jack Perdue, Lawrence Rauchwerger, Nancy M. Amato and Laxmi Bhuyan, International Journal of Parallel Programming, Vol: 33, pp. 307-350, Aug 2005. DOI: 10.1007/s10766-004-1187-0
Keywords: High-Performance Computing, Parallel Processing, Scientific Computing
Links : [Published]

BibTex

@article{iyer-ijpp-2005
, author = "Ravi Iyer and Jack Perdue and Lawrence Rauchwerger and Nancy M. Amato and Laxmi Bhuyan"
, title = "An Experimental Evaluation of the HP V-Class and SGI Origin 2000 Multiprocessors using Microbenchmarks and Scientific Applications"
, journal = "Internaational Journal of Parallel Programming"
, volume = "33"
, pages = "307--350"
, year = "2005"
, doi = "10.1007/s10766-004-1187-0"
}


Abstract

As processor technology continues to advance at a rapid pace, the principal performance bottleneck of shared memory systems has become the memory access latency. In order to understand the effects of cache and memory hierarchy on system latencies, performance analysts perform benchmark analysis on existing multiprocessors. In this study, we present a detailed comparison of two architectures, the HP V-Class and the SGI Origin 2000. Our goal is to compare and contrast design techniques used in these multiprocessors. We present the impact of processor design, cache/memory hierarchies and coherence protocol optimizations on the memory system performance of these multiprocessors. We also study the effect of parallelism overheads such as process creation and synchronization on the user-level performance of these multiprocessors. Our experimental methodology uses microbenchmarks as well as scientific applications to characterize the user-level performance. Our microbenchmark results show the impact of Ll/L2 cache size and TLB size on uniprocessor load/store latencies, the effect of coherence protocol design/optimizations and data sharing patterns on multiprocessor memory access latencies and finally the overhead of parallelism. Our application-based evaluation shows the impact of problem size, dominant sharing patterns and number of Processors used on speedup and raw execution time. Finally, we use hardware counter measurements to study the correlation of system-level performance metrics and the application’s execution time performance.


Iterative Relaxation of Constraints: A Framework for Improving Automated Motion Planning, O. Burchan Bayazit, Dawen Xie, Nancy M. Amato, In Proc. IEEE Int. Conf. Intel. Rob. Syst. (IROS), pp. 586-593, Edmonton, Alberta, Canada, Aug 2005. DOI: 10.1109/IROS.2005.1545045
Keywords: Computational Biology, Sampling-Based Motion Planning, Virtual Reality
Links : [Published]

BibTex

@INPROCEEDINGS{1545045,
author={O. B. {Bayazit} and {Dawen Xie} and N. M. {Amato}},
booktitle={2005 IEEE/RSJ International Conference on Intelligent Robots and Systems}, title={Iterative relaxation of constraints: a framework for improving automated motion planning},
year={2005},
volume={},
number={},
pages={3433-3440},
doi={10.1109/IROS.2005.1545045}}


Abstract

This paper presents a technique for improving the efficiency of automated motion planners. Motion planning has application in many areas such as robotics, virtual reality systems, computer-aided design, and even computational biology. Although there have been steady advances in motion planning algorithms, especially in randomized approaches such as probabilistic roadmap methods (PRMs) or rapidly-exploring random trees (RRTs), there are still some classes of problems that cannot be solved efficiently using these state-of-the-art motion planners. In this paper, we suggest an iterative strategy addressing this problem where we first simplify the problem by relaxing some feasibility constraints, solve the easier version of the problem, and then use that solution to help us find a solution for the harder problem. We show how this strategy can be applied to rigid bodies and to linkages with high degrees of freedom, including both open and closed chain systems. Experimental results are presented for linkages composed of 9-98 links. Although we use PRMs as the automated planner, the framework is general and can be applied with other motion planning techniques as well.


A Framework for Adaptive Algorithm Selection in STAPL, Nathan Thomas, Gabriel Tanase, Olga Tkachyshyn, Jack Perdue, Nancy M. Amato, Lawrence Rauchwerger, Proceedings of the Tenth ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming (PPoPP), pp. 277-288, Chicago, IL, USA, Jun 2005. DOI: 10.1145/1065944.1065981
Keywords: Parallel Algorithms, Parallel Programming, STAPL
Links : [Published]

BibTex

@inproceedings{10.1145/1065944.1065981,
author = {Thomas, Nathan and Tanase, Gabriel and Tkachyshyn, Olga and Perdue, Jack and Amato, Nancy M. and Rauchwerger, Lawrence},
title = {A Framework for Adaptive Algorithm Selection in STAPL},
year = {2005},
isbn = {1595930809},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1065944.1065981},
doi = {10.1145/1065944.1065981},
abstract = {Writing portable programs that perform well on multiple platforms or for varying input sizes and types can be very difficult because performance is often sensitive to the system architecture, the run-time environment, and input data characteristics. This is even more challenging on parallel and distributed systems due to the wide variety of system architectures. One way to address this problem is to adaptively select the best parallel algorithm for the current input data and system from a set of functionally equivalent algorithmic options. Toward this goal, we have developed a general framework for adaptive algorithm selection for use in the Standard Template Adaptive Parallel Library (STAPL). Our framework uses machine learning techniques to analyze data collected by STAPL installation benchmarks and to determine tests that will select among algorithmic options at run-time. We apply a prototype implementation of our framework to two important parallel operations, sorting and matrix multiplication, on multiple platforms and show that the framework determines run-time tests that correctly select the best performing algorithm from among several competing algorithmic options in 86-100% of the cases studied, depending on the operation and the system.},
booktitle = {Proceedings of the Tenth ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming},
pages = {277–288},
numpages = {12},
keywords = {machine learning, parallel algorithms, matrix multiplication, sorting, adaptive algorithms},
location = {Chicago, IL, USA},
series = {PPoPP \'05}
}


Abstract

Writing portable programs that perform well on multiple platforms or for varying input sizes and types can be very difficult because performance is often sensitive to the system architecture, the run-time environment, and input data characteristics. This is even more challenging on parallel and distributed systems due to the wide variety of system architectures. One way to address this problem is to adaptively select the best parallel algorithm for the current input data and system from a set of functionally equivalent algorithmic options. Toward this goal, we have developed a general framework for adaptive algorithm selection for use in the Standard Template Adaptive Parallel Library (STAPL). Our framework uses machine learning techniques to analyze data collected by STAPL installation benchmarks and to determine tests that will select among algorithmic options at run-time. We apply a prototype implementation of our framework to two important parallel operations, sorting and matrix multiplication, on multiple platforms and show that the framework determines run-time tests that correctly select the best performing algorithm from among several competing algorithmic options in 86-100% of the cases studied, depending on the operation and the system.


C-Space Subdivision and Integration in Feature-Sensitive Motion Planning, Marco A. Morales A., Lydia Tapia, Roger Pearce, Samuel Rodriguez, Nancy M. Amato, In Proc. IEEE International Conference on Robotics and Automation (ICRA), pp. 3114-3119, Barcelona, Spain, Apr 2005. DOI: 10.1109/ROBOT.2005.1570589
Keywords: Machine Learning, Motion Planning, Sampling-Based Motion Planning
Links : [Published] [Manuscript]

BibTex

@INPROCEEDINGS{1570589,
author={M. A. {Morales A.} and L. {Tapia} and R. {Pearce} and S. {Rodriguez} and N. M. {Amato}},
booktitle={Proceedings of the 2005 IEEE International Conference on Robotics and Automation},
title={C-space Subdivision and Integration in Feature-Sensitive Motion Planning},
year={2005},
volume={},
number={},
pages={3114-3119},
doi={10.1109/ROBOT.2005.1570589}}


Abstract

There are many randomized motion planning techniques, but it is often difficult to determine what planning method to apply to best solve a problem. Planners have their own strengths and weaknesses, and each one is best suited to a specific type of problem. In previous work, we proposed a meta-planner that, through analysis of the problem features, subdivides the instance into regions and determines which planner to apply in each region. The results obtained with our prototype system were very promising even though it utilized simplistic strategies for all components. Even so, we did determine that strategies for problem subdivision and for combination of partial regional solutions have a crucial impact on performance. In this paper, we propose new methods for these steps to improve the performance of the meta-planner. For problem subdivision, we propose two new methods: a method based on ‘ gaps’ and a method based on information theory. For combining partial solutions, we propose two new methods that concentrate on neighboring areas of the regional solutions. We present results that show the performance gain achieved by utilizing these new strategies.


Swarming Behavior Using Probabilistic Roadmap Techniques, O. Burçhan Bayazıt, Jyh-Ming Lien, Nancy M. Amato, Lecture Notes in Computer Science, Vol: 3342, pp. 112-125, Jan 2005. DOI: 10.1007/978-3-540-30552-1_10
Keywords: Group Behavior, Multi-Agent, Sampling-Based Motion Planning
Links : [Published]

BibTex

@article{10.1007/978-3-540-30552-1_10,
author ={O. Burçhan {Bayazıt}, Jyh-Ming {Lien}, Nancy M. {Amato}},
title ={Swarming Behavior Using Probabilistic Roadmap Techniques},
journal ={Lecture Notes in Computer Science},
volume ={3342},
year ={2003},
number ={},
pages ={112--125},
doi={10.1007/978-3-540-30552-1_10}
}


Abstract

While techniques exist for simulating swarming behaviors, these methods usually provide only simplistic navigation and planning capabilities. In this review, we explore the benefits of integrating roadmap-based path planning methods with flocking techniques to achieve different behaviors. We show how group behaviors such as exploring can be facilitated by using dynamic roadmaps (e.g., modifying edge weights) as an implicit means of communication between flock members. Extending ideas from cognitive modeling, we embed behavior rules in individual flock members and in the roadmap. These behavior rules enable the flock members to modify their actions based on their current location and state. We propose new techniques for several distinct group behaviors: homing, exploring (covering and goal searching), passing through narrow areas and shepherding. We present results that show that our methods provide significant improvement over methods that utilize purely local knowledge and moreover, that we achieve performance approaching that which could be obtained by an ideal method that has complete global knowledge. Animations of these behaviors can be viewed on our webpages.


A Machine Learning Approach for Feature-Sensitive Motion Planning, Marco Morales, Lydia Tapia, Roger Pearce, Samuel Rodriguez, Nancy M. Amato, In Proc. Int. Wkshp. on Alg. Found. of Rob. (WAFR), pp. 361-376, Utrecht/Zeist, The Netherlands, Jul 2004. DOI: 10.1007/10991541_25
Keywords: Machine Learning, Sampling-Based Motion Planning
Links : [Published]

BibTex

@INPROCEEDINGS{10.1007/10991541_25,
author={Marco {Morales}, Lydia {Tapia}, Roger {Pearce}, Samuel {Rodriguez}, Nancy M. {Amato}},
booktitle={In Proc. Int. Wkshp. on Alg. Found. of Rob. (WAFR)},
title={A Machine Learning Approach for Feature-Sensitive Motion Planning},
year={2004},
volume={}, number={}, pages={361--376},
doi={10.1007/10991541_25}
}


Abstract

Although there are many motion planning techniques, there is no method that outperforms all others for all problem instances. Rather, each technique has different strengths and weaknesses which makes it best-suited for certain types of problems. Moreover, since an environment can contain vastly different regions, there may not be a single planner that will perform well in all its regions. Ideally, one would use a suite of planners in concert and would solve the problem by applying the best-suited planner in each region. In this paper, we propose an automated framework for feature-sensitive motion planning. We use a machine learning approach to characterize and partition C-space into regions that are well suited to one of the methods in our library of roadmap-based motion planners. After the best-suited method is applied in each region, the resulting region roadmaps are combined to form a roadmap of the entire planning space. Over a range of problems, we demonstrate that our simple prototype system reliably outperforms any of the planners on their own.


Using Motion Planning to Map Protein Folding Landscapes and Analyze Folding Kinetics of Known Native Structures , Nancy M. Amato, Ken A. Dill, Guang Song, Journal of Computational Biology, Vol: 10, Issue: 3-4, pp. 239-255, Jul 2004. DOI: 10.1089/10665270360688002
Keywords: Computational Biology, Protein Folding, Sampling-Based Motion Planning
Links : [Published]

BibTex

@article{10.1089/10665270360688002,
author ={Nancy M. {Amato}, Ken {Dill}, Guang {Song}},
title ={Using Motion Planning to Map Protein Folding Landscapes and Analyze Folding Kinetics of Known Native Structures},
journal ={Journal of Computational Biology},
volume ={10},year ={2004},number ={3-4},pages ={239-255},
doi={10.1089/10665270360688002}
}


Abstract

We investigate a novel approach for studying the kinetics of protein folding. Our framework has evolved from robotics motion planning techniques called probabilistic roadmap methods (PRMs) that have been applied in many diverse fields with great success. In our previous work, we presented our PRM-based technique and obtained encouraging results studying protein folding pathways for several small proteins. In this paper, we describe how our motion planning framework can be used to study protein folding kinetics. In particular, we present a refined version of our PRM-based framework and describe how it can be used to produce potential energy landscapes, free energy landscapes, and many folding pathways all from a single roadmap which is computed in a few hours on a desktop PC. Results are presented for 14 proteins. Our ability to produce large sets of unrelated folding pathways may potentially provide crucial insight into some aspects of folding kinetics, such as proteins that exhibit both two-state and three-state kinetics that are not captured by other theoretical techniques.


Approximate Convex Decomposition, Jyh-Ming Lien, Nancy M. Amato, Proceedings of the Twentieth Annual Symposium on Computational Geometry (SOCG 04), pp. 457-458, Brooklyn, New York, Jun 2004. DOI: 10.1145/997817.997889
Keywords: Computational Geometry
Links : [Published]

BibTex

@inproceedings{ la-acd-04
, author = "J.-M. Lien and N. M. Amato"
, title = "Approximate Convex Decomposition"
, booktitle = "Proc.\ 20th Annual {ACM} Symp.\ Computat.\ Geom.\ (SoCG)"
, year = "2004"
, month = "June"
, note = "Video Abstract."
, pages = "457--458"
}


Abstract

Not applicable (it is a video abstract)


Approximate Decomposition of Polygons, Jyh-Ming Lien, Nancy M. Amato, Proceedings of the Twentieth Annual Symposium on Computational Geometry (SOCG 04), pp. 17-26, Brooklyn, New York, Jun 2004. DOI: 10.1145/997817.997823
Keywords: Computational Geometry, Convex Decomposition
Links : [Published]

BibTex

@inproceedings{ la-acdp-04
, author = "J.-M. Lien and N. M. Amato"
, title = "Approximate Convex Decomposition of Polygons"
, booktitle = "Proc.\ 20th Annual {ACM} Symp.\ Computat.\ Geom.\ (SoCG)"
, year = "2004"
, month="June"
, pages = "17--26"
}


Abstract

We propose a strategy to decompose a polygon, containing zero or more holes, into ``approximately convex'' pieces. For many applications, the approximately convex components of this decomposition provide similar benefits as convex components, while the resulting decomposition is significantly smaller and can be computed more efficiently. Moreover, our approximate convex decomposition (ACD) provides a mechanism to focus on key structural features and ignore less significant artifacts such as wrinkles and surface texture a user specified tolerance determines allowable concavity. We propose a simple algorithm that computes an ACD of a polygon by iteratively removing (resolving) the most significant non-convex feature (notch). As a by product, it produces an elegant hierarchical representation that provides a series of `increasingly convex' decompositions. Our algorithm computes an ACD of a simple polygon with n vertices and r notches in O(nr) time. In contrast, exact convex decomposition is NP-hard or,if the polygon has no holes, takes O(nr2) time.


Parallel Protein Folding with STAPL, Shawna Thomas, Nancy M. Amato, 18th International Parallel and Distributed Processing Symposium (IPDPS), pp. 189-, Santa Fe, NM, USA, Apr 2004. DOI: 10.1109/IPDPS.2004.1303204
Keywords: Parallel Planning, Protein Folding, STAPL
Links : [Published]

BibTex

@INPROCEEDINGS{1303204,
author={S. {Thomas} and N. M. {Amato}},
booktitle={18th International Parallel and Distributed Processing Symposium, 2004. Proceedings.},
title={Parallel protein folding with STAPL},
year={2004},
volume={},
number={},
pages={189-},
doi={10.1109/IPDPS.2004.1303204}}


Abstract

Summary form only given. The protein folding problem is to study how a protein dynamically folds to its so-called native state - an energetically stable, three-dimensional configuration. Understanding this process is of great practical importance since some devastating diseases such as Alzheimer\'s and bovine spongiform encephalopathy (Mad Cow) are associated with the misfolding of proteins. In our group, we have developed a new computational technique for studying protein folding that is based on probabilistic roadmap methods for motion planning. Our technique yields an approximate map of a protein\'s potential energy landscape that contains thousands of feasible folding pathways. We have validated our method against known experimental results. Other simulation techniques, such as molecular dynamics or Monte Carlo methods, require many orders of magnitude more time to produce a single, partial, trajectory. We report on our experiences parallelizing our method using STAPL (the standard template adaptive parallel library), that is being developed in the Parasol Lab at Texas A&M. An efficient parallel version enables us to study larger proteins with increased accuracy. We demonstrate how STAPL enables portable efficiency across multiple platforms without user code modification. We show performance gains on two systems: a dedicated Linux cluster and an extremely heterogeneous multiuser Linux cluster.


Enveloping multi-pocket obstacles with hexagonal metamorphic robots, Jennifer E. Walter, Mary E. Brooks, David F. Little, Nancy M. Amato, In Proc. IEEE Int. Conf. Robot. Autom. (ICRA), Vol: 3, pp. 2204-2209, New Orleans, Louisiana, USA, Apr 2004. DOI: 10.1109/ROBOT.2004.1307389
Keywords: Mobile Robots, Motion Planning, Multi-Agent
Links : [Published]

BibTex

@INPROCEEDINGS{1307389,
author={J. E. {Walter} and M. E. {Brooks} and D. F. {Little} and N. M. {Amato}},
booktitle={IEEE International Conference on Robotics and Automation, 2004. Proceedings. ICRA '04. 2004}, title={Enveloping multi-pocket obstacles with hexagonal metamorphic robots},
year={2004},
volume={3},
number={},
pages={2204-2209 Vol.3},
doi={10.1109/ROBOT.2004.1307389}}


Abstract

The problem addressed is reconfiguration planning for a metamorphic robotic system composed of any number of hexagonal robots when a single obstacle with multiple indentations or "pockets" is embedded in the goal environment. We extend our earlier work on filling a single pocket in an obstacle to the case where the obstacle surface may contain multiple pockets. The planning phase of our algorithm first determines whether the obstacle pockets provide sufficient clearance for module movement, i.e., whether the obstacle is "admissible". In this paper, we present algorithms that sequentially order individual pockets and order module placement inside each pocket. These algorithms ensure that every cell in each pocket is filled and that module deadlock and collision do not occur during reconfiguration. This paper also provides a complete overview of the planning stage that is executed prior to reconfiguration and presents a distributed reconfiguration schema for filling more than one obstacle pocket concurrently, followed by the envelopment of the entire obstacle. Lastly, we present examples of obstacles with multiple pockets that were successfully filled using our distributed reconfiguration simulator.


Shepherding Behaviors, Jyh-Ming Lien, O. Burchan Bayazit, Ross T. Sowell, Samuel Rodriguez, Nancy M. Amato, In Proc. IEEE Int. Conf. Robot. Autom. (ICRA), Vol: 4, pp. 4159-4164, New Orleans, Louisiana, USA, Apr 2004. DOI: 10.1109/ROBOT.2004.1308924
Keywords: Group Behavior, Multi-Agent, Sampling-Based Motion Planning
Links : [Published]

BibTex

@INPROCEEDINGS{1308924,
author={ {Jyh-Ming Lien} and O. B. {Bayazit} and R. T. {Sowell} and S. {Rodriguez} and N. M. {Amato}},
booktitle={IEEE International Conference on Robotics and Automation, 2004. Proceedings. ICRA '04. 2004}, title={Shepherding behaviors},
year={2004},
volume={4},
number={},
pages={4159-4164 Vol.4},
doi={10.1109/ROBOT.2004.1308924}}


Abstract

Shepherding behaviors are a type of flocking behavior in which outside agents guide or control members of a flock. Shepherding behaviors can be found in various forms in nature. For example, herding, covering, patrolling and collecting are common types of shepherding behaviors. In this work, we investigate ways to simulate these types of behaviors. A shepherd uses roadmaps to steer the flock and to re-group separated flock members. This paper focuses on improving the shepherd's movements to gain better control of the flock's motion and use this improved control to demonstrate a wider variety of shepherding behaviors.


Using Motion Planning to Study RNA Folding Kinetics, Xinyu Tang, Bonnie Kirkpatrick, Shawna Thomas, Guang Song, Nancy M. Amato, In Proc. Int. Conf. Comput. Molecular Biology (RECOMB), pp. 252-261, Mar 2004. DOI: 10.1145/974614.974648
Keywords: Computational Biology, RNA, Sampling-Based Motion Planning
Links : [Published]

BibTex

@inproceedings{10.1145/974614.974648,
author = {Tang, Xinyu and Kirkpatrick, Bonnie and Thomas, Shawna and Song, Guang and Amato, Nancy M.},
title = {Using Motion Planning to Study RNA Folding Kinetics},
year = {2004},
isbn = {1581137559},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/974614.974648},
doi = {10.1145/974614.974648},
abstract = {We propose a novel, motion planning based approach to approximately map the energy landscape of an RNA molecule. Our method is based on the successful probabilistic roadmap motion planners that we have previously successfully applied to protein folding. The key advantage of our method is that it provides a sparse map that captures the main features of the landscape and which can be analyzed to compute folding kinetics. In this paper, we provide evidence that this approach is also well suited to RNA. We compute population kinetics and transition rates on our roadmaps using the master equation for a few moderately sized RNA and show that our results compare favorably with results of other existing methods.},
booktitle = {Proceedings of the Eighth Annual International Conference on Resaerch in Computational Molecular Biology},
pages = {252–261},
numpages = {10},
keywords = {motion planning, RNA, folding kinetics},
location = {San Diego, California, USA},
series = {RECOMB \'04}
}


Abstract

We propose a novel, motion planning based approach to approximately map the energy landscape of an RNA molecule. Our method is based on the successful probabilistic roadmap motion planners that we have previously successfully applied to protein folding. The key advantage of our method is that it provides a sparse map that captures the main features of the landscape and which can be analyzed to compute folding kinetics. In this paper, we provide evidence that this approach is also well suited to RNA. We compute population kinetics and transition rates on our roadmaps using the master equation for a few moderately sized RNA and show that our results compare favorably with results of other existing methods.


A general framework for sampling on the medial axis of the free space, Jyh-Ming Lien, Shawna L. Thomas, Nancy M. Amato, In Proc. IEEE Int. Conf. Robot. Autom. (ICRA), Vol: 3, pp. 4439-4444, Taipei, Taiwan, Sep 2003. DOI: 10.1109/ROBOT.2003.1242288
Keywords: Medial Axis, Sampling-Based Motion Planning
Links : [Published]

BibTex

@INPROCEEDINGS{1242288,
author={ {Jyh-Ming Lien} and S. L. {Thomas} and N. M. {Amato}},
booktitle={2003 IEEE International Conference on Robotics and Automation (Cat. No.03CH37422)}, title={A general framework for sampling on the medial axis of the free space},
year={2003},
volume={3},
number={},
pages={4439-4444 vol.3},
doi={10.1109/ROBOT.2003.1242288}}


Abstract

We propose a general framework for sampling the configuration space in which randomly generated configurations, free or not, are retracted onto the medial axis of the free space. Generalizing our previous work, this framework provides a template encompassing all possible retraction approaches. It also removes the requirement of exactly computing distance metrics thereby enabling application to more realistic high dimensional problems. In particular, our framework supports methods that retract a given configuration exactly or approximately onto the medial axis. As in our previous work, exact methods provide fast and accurate retraction in low (2 or 3) dimensional space. We also propose new approximate methods that can be applied to high dimensional problems, such as many DOF articulated robots. Theoretical and experimental results show improved performance on problems requiring traversal of narrow passages. We also study tradeoffs between accuracy and efficiency for different levels of approximation, and how the level of approximation effects the quality of the resulting roadmap.


Extracting Optimal Paths from Roadmaps for Motion Planning, Jinsuck Kim, Roger A. Pearce, Nancy M. Amato, In Proc. IEEE Int. Conf. Robot. Autom. (ICRA), Vol: 2, pp. 2424-2429, Taipei, Taiwan, Sep 2003. DOI: 10.1109/ROBOT.2003.1241956
Keywords: Path Planning, Query Customization
Links : [Published]

BibTex

@INPROCEEDINGS{1241956,
author={ {Jinsuck Kim} and R. A. {Pearce} and N. M. {Amato}},
booktitle={2003 IEEE International Conference on Robotics and Automation (Cat. No.03CH37422)}, title={Extracting optimal paths from roadmaps for motion planning},
year={2003},
volume={2},
number={},
pages={2424-2429 vol.2},
doi={10.1109/ROBOT.2003.1241956}}


Abstract

We present methods for extracting optimal paths from motion planning roadmaps. Our system enables any combination of optimization criteria, such as collision detection, kinematic/dynamic constraints, or minimum clearance, and relaxed definitions of the goal state, to be used when selecting paths from roadmaps. Our algorithm is an augmented version of Dijkstra's shortest path algorithm which allows edge weights to be defined relative to the current path. We present simulation results maximizing minimum path clearance, minimizing localization effort, and enforcing kinematic/dynamic constraints.


Feature-Based Localization using Scannable Visibility Sectors, Jinsuck Kim, Roger A. Pearce, Nancy M. Amato, In Proc. IEEE Int. Conf. Robot. Autom. (ICRA), Vol: 2, pp. 2854-2859, Taipei, Taiwan, Sep 2003. DOI: 10.1109/ROBOT.2003.1242025
Keywords: Mobile Robots, Navigation, Sensor Systems
Links : [Published]

BibTex

@INPROCEEDINGS{1242025,
author={ {Jinsuck Kim} and R. A. {Pearce} and N. M. {Amato}},
booktitle={2003 IEEE International Conference on Robotics and Automation (Cat. No.03CH37422)}, title={Feature-based localization using scannable visibility sectors},
year={2003},
volume={2},
number={},
pages={2854-2859 vol.2},
doi={10.1109/ROBOT.2003.1242025}}


Abstract

This paper presents methods for navigating and localizing mobile robots in a known indoor environment. We introduce a restricted visibility concept called a scannable sector that can aid many existing navigation and localization algorithms. The scannable sectors are based on the physical characteristics of the environment and the limitations of the localization sensors used. We describe a complete navigation system that includes a scannable sector based localizer, sonar sensors, and a probabilistic roadmap path planner. Simulation and hardware results using a real robot with sonar sensors show the potential of our approach.


Improving the Connectivitiy of PRM Roadmaps, Marco Morales, Samuel Rodriguez, Nancy M. Amato, In Proc. IEEE Int. Conf. Robot. Autom. (ICRA), Vol: 3, pp. 4427-4432, Taipei, Taiwan, Sep 2003. DOI: 10.1109/ROBOT.2003.1242286
Keywords: Sampling-Based Motion Planning
Links : [Published]

BibTex

@INPROCEEDINGS{1242286,
author={M. {Morales} and S. {Rodriguez} and N. M. {Amato}},
booktitle={2003 IEEE International Conference on Robotics and Automation (Cat. No.03CH37422)}, title={Improving the connectivity of PRM roadmaps},
year={2003},
volume={3},
number={},
pages={4427-4432 vol.3},
doi={10.1109/ROBOT.2003.1242286}}


Abstract

In this paper we investigate how the coverage and connectedness of PRM roadmaps can be improved by adding a connected component (CC) connection step to the general PRM framework. We provide experimental results establishing that significant roadmap improvements can be obtained relatively efficiently by utilizing a suite of CC connection methods, which include variants of existing methods such as RRT and a new ray tracing based method. The coordinated application of these techniques is enabled by methods for selecting and scheduling pairs of nodes in different CCs for connection attempts. In addition to identifying important and/or promising regions of C-space for exploration, these methods also provide a mechanism for controlling the cost of the connection attempts. In our experiments, the time required by the improvement phase was on the same order as the time used to generate the initial roadmap.


A General Framework for PRM Motion Planning, Guang Song, Shawna Thomas, Nancy M. Amato, In Proc. IEEE Int. Conf. Robot. Autom. (ICRA), Vol: 3, pp. 4445-4450, Taipei, Taiwan, Sep 2003. DOI: 10.1109/ROBOT.2003.1242289
Keywords: Lazy Evaluation, Query Customization, Sampling-Based Motion Planning
Links : [Published]

BibTex

@INPROCEEDINGS{1242289,
author={ {Guang Song} and S. {Thomas} and N. M. {Amato}},
booktitle={2003 IEEE International Conference on Robotics and Automation (Cat. No.03CH37422)}, title={A general framework for PRM motion planning},
year={2003},
volume={3},
number={},
pages={4445-4450 vol.3},
doi={10.1109/ROBOT.2003.1242289}}


Abstract

An important property of PRM roadmaps is that they provide a good approximation of the connectivity of the free C-space. We present a general framework for building and querying probabilistic roadmaps that includes all previous PRM variants as special cases. In particular, it supports no, complete, or partial node and edge validation and various evaluation schedules for path validation, and it enables path customization for variable, adaptive query requirements. While each of the above features is present in some PRM variant, the general framework proposed here is the only one to include them all. Our framework enables users to choose the best approximation level for their problem. Our experimental evidence shows this can result in significant performance gains.


Neuron PRM: A Framework for Constructing Cortical Networks, Jyh-Ming Lien, Marco Morales, Nancy M. Amato, Neurocomputing, Vol: 54, pp. 191-197, Jun 2003. DOI: 10.1016/S0925-2312(02)00728-2
Keywords: Machine Learning, Sampling-Based Motion Planning
Links : [Published]

BibTex

@article{LIEN2003191,
title = "Neuron PRM: a framework for constructing cortical networks",
journal = "Neurocomputing",
volume = "52-54",
pages = "191 - 197",
year = "2003",
note = "Computational Neuroscience: Trends in Research 2003",
issn = "0925-2312",
doi = "https://doi.org/10.1016/S0925-2312(02)00728-2",
url = "http://www.sciencedirect.com/science/article/pii/S0925231202007282",
author = "Jyh-Ming Lien and Marco Morales and Nancy M. Amato",
keywords = "Cortical networks, PRM, BTS, L-system, Rectangle tree",
abstract = "The brain's extraordinary computational power to represent and interpret complex natural environments is essentially determined by the topology and geometry of the brain's architectures. We present a framework to construct cortical networks which borrows from probabilistic roadmap methods developed for robotic motion planning. We abstract the network as a large-scale directed graph, and use L-systems and statistical data to ‘grow’ neurons that are morphologically indistinguishable from real neurons. We detect connections (synapses) between neurons using geometric proximity tests."
}


Abstract

The brain's extraordinary computational power to represent and interpret complex natural environments is essentially determined by the topology and geometry of the brain's architectures. We present a framework to construct cortical networks which borrows from probabilistic roadmap methods developed for robotic motion planning. We abstract the network as a large-scale directed graph, and use L-systems and statistical data to ‘grow’ neurons that are morphologically indistinguishable from real neurons. We detect connections (synapses) between neurons using geometric proximity tests.


Enveloping Obstacles with Hexagonal Metamorphic Robots, Jennifer E. Walter, Mary E. Brooks, David F. Little, Nancy M. Amato, Proc. IEEE Int. Conf. Robot. Autom. (ICRA), Vol: 1, pp. 741-748, Taipei, Taiwan, Jan 2003. DOI: 10.1109/ROBOT.2003.1241682
Keywords: Mobile Robots, Motion Planning, Multi-Agent
Links : [Published]

BibTex

@INPROCEEDINGS{1241682,
author={J. E. {Walter} and E. M. {Tsai} and N. M. {Amato}},
booktitle={2003 IEEE International Conference on Robotics and Automation (Cat. No.03CH37422)}, title={Enveloping obstacles with hexagonal metamorphic robots},
year={2003},
volume={1},
number={},
pages={741-748 vol.1},
doi={10.1109/ROBOT.2003.1241682}}


Abstract

The problem addressed is the distributed reconfiguration of the metamorphic robot system composed of any number of two dimensional robots (modules). The initial configuration we consider is a straight chain of modules, while the goal configuration satisfies a simple admissibility condition. Our reconfiguration strategy depends on finding a contiguous path of cells, called a substrate path that spans the goal configuration. Modules fill in this substrate path and then move along the path to fill in the remainder of the goal without collision or deadlock. In this paper, we address the problem of reconfiguration when a single obstacle is embedded in the goal environment. We introduce a classification for traversable surfaces, which allows for coherence in defining admissibility characteristics for various objects in the hexagonal grid. We present algorithms to 1) determine if an obstacle embedded in the goal fulfills a simple admissibility requirement, 2) include an admissible obstacle in a substrate path, and 3) accomplish distributed reconfiguration.


A Path Planning-based Study of Protein Folding With a Case Study of Hairpin Formation in Protein G and L, Guang Song, Shawna Thomas, Ken A. Dill, J. Martin Scholtz, Nancy M. Amato, In Proc. Pac. Symp. of Biocomputing (PSB), Vol: 8, pp. 240-251, Lihue, Hawaii, Jan 2003.
Keywords: Computational Biology, Protein Folding, Sampling-Based Motion Planning
Links : [Published]

BibTex

@INPROCEEDINGS{Song_2003,
author={Guang {Song}, Shawna {Thomas}, Ken A. {Dill}, J. Martin {Scholtz}, Nancy M. {Amato}},
booktitle={In Proc. Pac. Symp. of Biocomputing (PSB)},
title={A Path Planning-based Study of Protein Folding With a Case Study of Hairpin Formation in Protein G and L},
year={2003}, volume={8}, number={}, pages={240-251},
doi={}
}


Abstract

We investigate a novel approach for studying protein folding that has evolvedfrom robotics motion planning techniques calledprobabilistic roadmapmethods(prms). Our focus is to study issues related to the folding process, such as theformation of secondary and tertiary structure,assumingweknowthenativefold.A feature of ourprm-based framework is that the large sets of folding pathwaysin the roadmaps it produces, in a few hours on a desktop PC, provide globalinformation about the protein’s energy landscape. This is an advantage over othersimulation methods such as molecular dynamics or Monte Carlo methods whichrequire more computation and produce only a single trajectory in each run. Inour initial studies, we obtained encouraging results for several small proteins. Inthis paper, we investigate more sophisticated techniques for analyzing the foldingpathways in our roadmaps. In addition to more formally revalidating our previousresults, we present a case study showing our technique captures known foldingdifferences between the structurally similar proteins G and L.


Better Group Behaviors Using Rule-Based Roadmaps, Osman Burçhan Bayazit, Jyh-Ming Lien, Nancy M. Amato, In Proc. Int. Wkshp. on Alg. Found. of Rob. (WAFR), Vol: 7, pp. 95-111, Nice, France, Dec 2002. DOI: 10.1007/978-3-540-45058-0_7
Keywords: Group Behavior, Multi-Agent, Sampling-Based Motion Planning
Links : [Published]

BibTex

@INPROCEEDINGS{10.1007/978-3-540-45058-0_7,
author={Osman Burçhan {Bayazit}, Jyh-Ming {Lien}, Nancy M. {Amato}},
booktitle={In Proc. Int. Wkshp. on Alg. Found. of Rob. (WAFR)},
title={Better Group Behaviors Using Rule-Based Roadmaps},
year={2002}, volume={7}, number={}, pages={95-111},
doi={10.1007/978-3-540-45058-0_7}}


Abstract

While techniques exist for simulating group behaviors, these methods usually only provide simplistic navigation and planning capabilities. In this work, we explore the benefits of integrating roadmap-based path planning methods with flocking techniques. We show how group behaviors such as exploring can be facilitated by using dynamic roadmaps (e.g., modifying edge weights) as an implicit means of communication between flock members. Extending ideas from cognitive modeling, we embed behavior rules in individual flock members and in the roadmap. These behavior rules enable the flock members to modify their actions based on their current location and state. We propose new techniques for three distinct group behaviors: homing, exploring (covering and goal searching) and passing through narrow areas. Animations of these behaviors can be viewed at http://parasol.tamu.edu/dsmft.


SmartApps, An Application Centric Approach to High Performance Computing: Compiler-Assisted Software and Hardware Support for Reduction Operations, Francis Dang, Maria Jesus Garzaran, Milos Prvulovic, Ye Zhang, Alin Jula, Hao Yu, Nancy Amato, Lawrence Rauchwerger, Josep Torrellas, Proceedings of the 16th International Parallel and Distributed Processing Symposium (IPDPS), pp. 172-181, Fort Lauderdale, Florida, USA, Apr 2002. DOI: 10.1109/IPDPS.2002.1016572
Keywords: Computer Architecture, High-Performance Computing, Optimizing Compilers
Links : [Published]

BibTex

@INPROCEEDINGS{1016572, author={F. {Dang} and M. {Jesus Garzaran} and M. {Prvulovic} and {Ye Zhang} and A. {Jula} and {Hao Yu} and N. {Amato} and L. {Rauchwerger} and J. {Torrellas}}, booktitle={Proceedings 16th International Parallel and Distributed Processing Symposium}, title={Smartapps, an application centric approach to high performance computing: compiler-assisted software and hardware support for reduction operations}, year={2002}, volume={}, number={}, pages={10 pp-}, doi={10.1109/IPDPS.2002.1016572}}


Abstract

State-of-the-art run-time systems are a poor match to diverse, dynamic distributed applications because they are designed to provide support to a wide variety of applications, without much customization to individual specific requirements. Little or no guiding information flows directly from the application to the run-time system to allow the latter to fully tailor its services to the application. As a result, the performance is disappointing. To address this problem, we propose application-centric computing, or SMART APPLICATIONS. In the executable of smart applications, the compiler embeds most run-time system services, and a performance-optimizing feedback loop that monitors the application's performance and adaptively reconfigures the application and the OS/hardware platform. At run-time, after incorporating the code's input and the system's resources and state, the SMARTAPP performs a global optimization. This optimization is instance specific and thus much more tractable than a global generic optimization between application, OS and hardware. The resulting code and resource customization should lead to major speedups. In this paper, we first describe the overall architecture of SMARTAPPS and then present some achievements to date, focusing on compiler-assisted software and hardware techniques for parallelizing reduction operations. These illustrate SMARTAPPS use of adaptive algorithm selection and moderately reconfigurable hardware.


STAPL: An Adaptive, Generic Parallel C++ Library, Ping An, Alin Jula, Silvius Rus, Steven Saunders, Tim Smith, Gabriel Tanase, Nathan Thomas, Nancy Amato, Lawrence Rauchwerger, Languages and Compilers for Parallel Computing (LCPC.) Lecture Notes in Computer Science, Vol: 2624, pp. 193-208, Cumberland Falls, KY, USA, Aug 2001. DOI: 10.1007/3-540-35767-X_13
Keywords: High-Performance Computing, Parallel Programming, STAPL
Links : [Published]

BibTex

@InProceedings{10.1007/3-540-35767-X_13,
author="An, Ping
and Jula, Alin
and Rus, Silvius
and Saunders, Steven
and Smith, Tim
and Tanase, Gabriel
and Thomas, Nathan
and Amato, Nancy
and Rauchwerger, Lawrence",
editor="Dietz, Henry G.",
title="STAPL: An Adaptive, Generic Parallel C++ Library",
booktitle="Languages and Compilers for Parallel Computing",
year="2003",
publisher="Springer Berlin Heidelberg",
address="Berlin, Heidelberg",
pages="193--208",
abstract="The Standard Template Adaptive Parallel Library (STAPL) is a parallel library designed as a superset of the ANSI C++ Standard Template Library (STL). It is sequentially consistent for functions with the same name, and executes on uni- or multi-processor systems that utilize shared or distributed memory. STAPL is implemented using simple parallel extensions of C++ that currently provide a SPMD model of parallelism, and supports nested parallelism. The library is intended to be general purpose, but emphasizes irregular programs to allow the exploitation of parallelism for applications which use dynamically linked data structures such as particle transport calculations, molecular dynamics, geometric modeling, and graph algorithms. STAPL provides several different algorithms for some library routines, and selects among them adaptively at runtime. STAPL can replace STL automatically by invoking a preprocessing translation phase. In the applications studied, the performance of translated code was within 5{\%} of the results obtained using STAPL directly. STAPL also provides functionality to allow the user to further optimize the code and achieve additional performance gains. We present results obtained using STAPL for a molecular dynamics code and a particle transport code.",
isbn="978-3-540-35767-4"
}


Abstract

The Standard Template Adaptive Parallel Library (STAPL) is a parallel library designed as a superset of the ANSI C++ Standard Template Library (STL). It is sequentially consistent for functions with the same name, and executes on uni- or multi-processor systems that utilize shared or distributed memory. STAPL is implemented using simple parallel extensions of C++ that currently provide a SPMD model of parallelism, and supports nested parallelism. The library is intended to be general purpose, but emphasizes irregular programs to allow the exploitation of parallelism for applications which use dynamically linked data structures such as particle transport calculations, molecular dynamics, geometric modeling, and graph algorithms. STAPL provides several different algorithms for some library routines, and selects among them adaptively at runtime. STAPL can replace STL automatically by invoking a preprocessing translation phase. In the applications studied, the performance of translated code was within 5% of the results obtained using STAPL directly. STAPL also provides functionality to allow the user to further optimize the code and achieve additional performance gains. We present results obtained using STAPL for a molecular dynamics code and a particle transport code.


STAPL: A standard template adaptive parallel C++ library, Ping An, Alin Jula, Silvius Rus, Steven Saunders, Tim Smith, Gabriel Tanase, Nathan Thomas, Nancy Amato, Lawrence Rauchwerger, Int. Wkshp on Adv. Compiler Technology for High Perf. and Embedded Processors, pp. 10-, Bucharest, Romania, Jul 2001.
Keywords: C++, Parallel Libraries, STAPL
Links : [Published]

BibTex

@INPROCEEDINGS{An01stapl:a,
author = {Ping An and Alin Jula and Silvius Rus and Steven Saunders and Tim Smith and Gabriel Tanase and Nathan Thomas Nancy M. Amato},
title = {STAPL: A standard template adaptive parallel C++ library},
booktitle = {In Int. Wkshp on Adv. Compiler Technology for High Perf. and Embedded Processors},
year = {2001},
pages = {10}
}


Abstract

The Standard Template Adaptive Parallel Library (STAPL) is a parallel library designed as a superset of the ANSI C++ Standard Template Library (STL). It is sequentially consistent for functions with the same name, and executes on uni-or multi-processor systems that utilize shared or distributed memory. STAPL is implemented using simple parallel extensions of C++ that currently provide a SPMD model of parallelism, and supports nested parallelism. The library is intended to be general purpose, but emphasizes irregular programs to allow the exploitation of parallelism in areas such as particle transport calculations, molecular dynamics, geometric modeling, and graph algorithms, which use dynamically linked data structures. STAPL provides several different algorithms for some library routines, and selects among them adaptively at run-time. STAPL can replace STL automatically by invoking a preprocessing translation phase. The performance of translated code is close to the results obtained using STAPL directly (less than 5% performance deterioration). However, STAPL also provides functionality to allow the user to further optimize the code and achieve additional performance gains. We present results obtained using STAPL for a molecular dynamics code and a particle transport code.


An adaptive framework for single shot motion planning: a self-tuning system for rigid and articulated robots, D. Vallejo, I. Remmler, N.M. Amato, Proceedings of IEEE International Conference on Robotics and Automation, pp. 21-26, Seoul, Korea (South), May 2001. DOI: 10.1109/ROBOT.2001.932524
Keywords: Sampling-Based Motion Planning
Links : [Published]

BibTex

@INPROCEEDINGS{932524,
author={Vallejo, D. and Remmler, I. and Amato, N.M.},
booktitle={Proceedings 2001 ICRA. IEEE International Conference on Robotics and Automation (Cat. No.01CH37164)},
title={An adaptive framework for 'single shot' motion planning: a self-tuning system for rigid and articulated robots},
year={2001},
volume={1},
number={},
pages={21-26 vol.1},
doi={10.1109/ROBOT.2001.932524}}


Abstract

Describes an enhanced version of an adaptive framework for single shot motion planning (Vallejo et al., 2000). This framework is versatile, and particularly suitable for crowded environments. Our iterative strategy analyzes the characteristics of the query and adaptively selects planners whose strengths match the current situation. Contributions in the paper include an automatic method for setting and adaptively tuning planner characterizations, and reducing the reliance on programmer expertise present in the original framework. The adaptive refinement enables the system to evolve parameters specifically suited for particular classes of applications. The system now supports articulated robots, which were not supported previously. Our experimental results in complex 3D CAD environments show that our strategy solves queries that none of the planners could solve on their own.


A motion planning approach to folding: from paper craft to protein folding, Guang Song, Nancy M. Amato, In Proc. IEEE Int. Conf. Robot. Autom. (ICRA), Vol: 1, pp. 948-953, Seoul, Korea, May 2001. DOI: 10.1109/ROBOT.2001.932672
Keywords: Path Planning, Protein Folding, Sampling-Based Motion Planning
Links : [Published]

BibTex

@INPROCEEDINGS{932672,
author={ {Guang Song} and N. M. {Amato}},
booktitle={Proceedings 2001 ICRA. IEEE International Conference on Robotics and Automation (Cat. No.01CH37164)}, title={A motion planning approach to folding: from paper craft to protein folding},
year={2001},
volume={1},
number={},
pages={948-953 vol.1},
doi={10.1109/ROBOT.2001.932672}}


Abstract

We present a framework for studying folding problems from a motion planning perspective. Modeling foldable objects as tree-like multi-link objects allows one to apply motion planning techniques to folding problems. An important feature of this approach is that it not only allows one to study foldability questions, such as, can an object be folded (or unfolded) into another object, but also provides one with another tool for investigating the dynamic folding process itself. The framework proposed here has application to traditional motion planning areas such as automation and animation, and presents a novel approach for studying protein folding pathways. Preliminary experimental results with traditional paper crafts (e.g., box folding) and small proteins (approximately 60 residues) are quite encouraging.


Customizing PRM Roadmaps at Query Time, Guang Song, S. Miller, N.M. Amato, In Proc. IEEE International Conference on Robotics and Automation (ICRA), Seoul, South Korea, May 2001. DOI: 10.1109/ROBOT.2001.932823
Keywords: Query Customization, Sampling-Based Motion Planning
Links : [Published]

BibTex

@inproceedings{song2001customizing,
title={Customizing PRM roadmaps at query time},
author={Song, Guang and Miller, Shawna and Amato, Nancy M},
booktitle={Proceedings 2001 ICRA. IEEE International Conference on Robotics and Automation (Cat. No. 01CH37164)},
volume={2},
pages={1500--1505},
year={2001},
organization={IEEE}
}


Abstract

We propose an approach for building and querying probabilistic roadmaps. In the roadmap construction stage, we build coarse roadmaps by performing only an approximate validation of the roadmap nodes and/or edges. In the query stage, the roadmap is validated and refined only in the area of interest for the query, and moreover is customized in accordance with any specified query preferences. This approach, which postpones some of the validation checks (e.g., collision checks) to the query phase, yields more efficient solutions to many problems. An important benefit of our approach is that it gives one the ability to customize the same roadmap in accordance with multiple, variable, query preferences. For example our approach enables one to find a path which maintains a particular clearance, or makes at most some specified number of sharp turns. Our preliminary results on problems drawn from diverse application domains show that this new approach dramatically improves performance, and shows remarkable flexibility when adapting to different query requirements.


An Integrated Mobile Robot Path (Re)Planner and Localizer for Personal Robots, Jinsuck Kim, Nancy Amato, Sooyong Lee, In Proc. IEEE International Conference on Robotics and Automation (ICRA), Seoul, South Korea, May 2001. DOI: 10.1109/ROBOT.2001.933208
Keywords: Mobile Robots, Motion Planning
Links : [Published]

BibTex

@INPROCEEDINGS{933208, author={ {Jinsuck Kim} and N. M. {Amato} and {Sooyong Lee}}, booktitle={Proceedings 2001 ICRA. IEEE International Conference on Robotics and Automation (Cat. No.01CH37164)}, title={An integrated mobile robot path (re)planner and localizer for personal robots}, year={2001}, volume={4}, number={}, pages={3789-3794 vol.4}, doi={10.1109/ROBOT.2001.933208}}


Abstract

We describe a method for navigation in a known indoor environment, such as a home or office, that requires only inexpensive range sensors. Our framework includes a high-level planner which integrates and coordinates path planning and localization modules with the aid of a module for computing regions which are expected, with high probability, to contain the robot at any given time. The localization method is based on simple geometric properties of the environment which are computed during a preprocessing stage. The roadmap-based path planner enables one to select routes, and subgoals along those routes, that will facilitate localization and other optimization criteria. In addition, our framework enables one to quickly plan new routes, dynamically, based on the current position as computed by intermediate localization operations. We present simulation and hardware experimental results that illustrate the practicality and potential of our approach.


Hybrid Dynamic Simulation of Rigid-Body Contact with Coulomb Friction, Wookho Son, J.C. Trinkle, Nancy Amato, In Proc. IEEE International Conference on Robotics and Automation (ICRA), Seoul, South Korea, May 2001. DOI: 10.1109/ROBOT.2001.932802
Keywords: Kinodynamic Planning, Robotics
Links : [Published]

BibTex

@inproceedings{son2001hybrid,
title={Hybrid dynamic simulation of rigid-body contact with Coulomb friction},
author={Son, Wookho and Trinkle, Jeffrey C and Amato, Nancy M},
booktitle={Proceedings 2001 ICRA. IEEE International Conference on Robotics and Automation (Cat. No. 01CH37164)},
volume={2},
pages={1376--1381},
year={2001},
organization={IEEE}
}


Disassembly Sequencing Using a Motion Planning Approach, Sujay Sundaram, Ian Remmler, Nancy Amato, In Proc. IEEE International Conference on Robotics and Automation (ICRA), Seoul, South Korea, May 2001. DOI: 10.1109/ROBOT.2001.932818
Keywords: Disassembly Planning, Motion Planning
Links : [Published]

BibTex

@INPROCEEDINGS{932818, author={S. {Sundaram} and I. {Remmler} and N. M. {Amato}}, booktitle={Proceedings 2001 ICRA. IEEE International Conference on Robotics and Automation (Cat. No.01CH37164)}, title={Disassembly sequencing using a motion planning approach}, year={2001}, volume={2}, number={}, pages={1475-1480 vol.2}, doi={10.1109/ROBOT.2001.932818}}


Abstract

Our motion planning based approach treats the parts in the assembly as robots and operates in the composite configuration space of the parts' individual configuration spaces. Randomized techniques inspired by recent motion planning methods are used to sample configurations in this space. Since typical assemblies consist of many parts, the corresponding composite C-spaces have high dimensionality. Also, since many important configurations for the disassembly sequence will involve closely packed parts, the disassembly problem suffers from the so-called narrow passage problem. We bias the sampling by computing potential movement directions based on the geometric characteristics of configurations known to be reachable from the assembled configuration. We construct a disassembly tree which is rooted at the starting assembled configuration. Our experimental results with several non-trivial puzzle-like assemblies show the potential of this approach.


Probabilistic Roadmaps-Putting It All Together, Lucia K. Dale, Nancy M. Amato, In Proc. IEEE International Conference on Robotics and Automation (ICRA), pp. 1940-1947, Seoul, South Korea, May 2001. DOI: 10.1109/ROBOT.2001.932892
Keywords: Sampling-Based Motion Planning
Links : [Published]

BibTex

@INPROCEEDINGS{932892, author={L. K. {Dale} and N. M. {Amato}}, booktitle={Proceedings 2001 ICRA. IEEE International Conference on Robotics and Automation (Cat. No.01CH37164)}, title={Probabilistic roadmaps-putting it all together}, year={2001}, volume={2}, number={}, pages={1940-1947 vol.2}, doi={10.1109/ROBOT.2001.932892}}


Abstract

Given a robot and a workspace, probabilistic roadmap planners (PRMs) build a roadmap of paths sampled from the workspace. A roadmap node is a single collision-free robot configuration, randomly generated. A roadmap edge is a sequence of collision-free robot configurations which interpolate the path from one roadmap node to another. Queries to the roadmap are (start, goal) pairs. If both the start and goal of a pair can be connected to the same connected component of the roadmap, the query is solved. Many promising variants of the PRM have been proposed, each with their own strengths and weaknesses. We propose a meta-planner for using many PRMs in such a way that the strengths are combined and the weaknesses offset. Our meta-planner will perform the combination in the following manner: i) provide a framework in which different motion planners are available and to which new ones are easily added; ii) characterize subregions (possibly overlapping) based on sample characteristics and connection results; iii) assign subregions to one or more planners which are judged promising; and iv) provide stopping criteria for roadmap construction. We present experimental results for four characterization measures. A general technique we call 'filtering' is presented for keeping roadmaps compact.


Enhancing Randomized Motion Planners: Exploring with Haptic Hints, O. Burçhan Bayazit, Guang Song, Nancy M. Amato , Autonomous Robots, Vol: 10, Issue: 2, pp. 163-174, Mar 2001. DOI: 10.1023/A:1008981903273
Keywords: Motion Planning, Sampling-Based Motion Planning
Links : [Published]

BibTex

@article{bayazit2001enhancing,
title={Enhancing randomized motion planners: Exploring with haptic hints},
author={Bayazit, O Burchan and Song, Guang and Amato, Nancy M},
journal={Autonomous Robots},
volume={10},
number={2},
pages={163--174},
year={2001},
publisher={Springer}
}


Abstract

In this paper, we investigate methods for enabling a human operator and an automatic motion planner to cooperatively solve a motion planning query. Our work is motivated by our experience that automatic motion planners sometimes fail due to the difficulty of discovering ‘critical’ configurations of the robot that are often naturally apparent to a human observer. Our goal is to develop techniques by which the automatic planner can utilize (easily generated) user-input, and determine ‘natural’ ways to inform the user of the progress made by the motion planner. We show that simple randomized techniques inspired by probabilistic roadmap methods are quite useful for transforming approximate, user-generated paths into collision-free paths, and describe an iterative transformation method which enables one to transform a solution for an easier version of the problem into a solution for the original problem. We also illustrate that simple visualization techniques can provide meaningful representations of the planner's progress in a 6-dimensional C-space. We illustrate the utility of our methods on difficult problems involving complex 3D CAD Models.


Customizing PRM Roadmaps at Query Time, Guang Song, Shawna Miller, Nancy M. Amato, Proceedings 2001 ICRA. IEEE International Conference on Robotics and Automation, Vol: 2, pp. 1500-1505, Seoul, Korea, Jan 2001. DOI: 10.1109/ROBOT.2001.932823
Keywords: Lazy Planning, Motion Planning, Sampling-Based Motion Planning
Links : [Published]

BibTex

@INPROCEEDINGS{932823,
author={Guang Song and Miller, S. and Amato, N.M.},
booktitle={Proceedings 2001 ICRA. IEEE International Conference on Robotics and Automation (Cat. No.01CH37164)},
title={Customizing PRM roadmaps at query time},
year={2001},
volume={2},
number={},
pages={1500-1505 vol.2},
doi={10.1109/ROBOT.2001.932823}}


Abstract

We propose an approach for building and querying probabilistic roadmaps. In the roadmap construction stage, we build coarse roadmaps by performing only an approximate validation of the roadmap nodes and/or edges. In the query stage, the roadmap is validated and refined only in the area of interest for the query, and moreover is customized in accordance with any specified query preferences. This approach, which postpones some of the validation checks (e.g., collision checks) to the query phase, yields more efficient solutions to many problems. An important benefit of our approach is that it gives one the ability to customize the same roadmap in accordance with multiple, variable, query preferences. For example our approach enables one to find a path which maintains a particular clearance, or makes at most some specified number of sharp turns. Our preliminary results on problems drawn from diverse application domains show that this new approach dramatically improves performance, and shows remarkable flexibility when adapting to different query requirements.


An Adaptive Framework for Single Shot Motion Planning, D.R. Vallejo, C. Jones, N.M. Amato, IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 1722-1727, Takamatsu, Japan, Oct 2000. DOI: 10.1109/IROS.2000.895220
Keywords: Sampling-Based Motion Planning
Links : [Published]

BibTex

@INPROCEEDINGS{895220,
author={Vallejo, D.R. and Jones, C. and Amato, N.M.},
booktitle={Proceedings. 2000 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2000) (Cat. No.00CH37113)},
title={An adaptive framework for 'single shot' motion planning},
year={2000},
volume={3},
number={},
pages={1722-1727 vol.3},
doi={10.1109/IROS.2000.895220}}


Abstract

This paper proposes an adaptive framework for single shot motion planning, i.e., planning without preprocessing. This framework can be used in any situation, and in particular, is suitable for crowded environments in which the robot's free C-space has narrow corridors such as maintainability studies in complex 3D CAD models. Our iterative strategy adaptively selects a planner whose strengths match the current situation, and then, online, switches to a different planner when circumstances change. This requires techniques to evaluate the characteristics of the current query, and a set of planners which are characterized so that we can match the query with the best planner for it. Our experimental results in complex 3D CAD environments show that our strategy solves queries that none of the planners could solve on their own.


Ligand Binding with OBPRM and Haptic User Input: Enhancing Automatic Motion Planning with Virtual Touch, O. Burchan Bayazit , Guang Song , Nancy M. Amato , ACM Digital Library, College Station, Texas, USA, Oct 2000.
Keywords: Ligand Binding, Sampling-Based Motion Planning
Links : [Published]

BibTex

@MISC{Bayazit00ligandbinding,
author = {O. Burchan Bayazit and Guang Song and Nancy M. Amato},
title = {Ligand Binding with OBPRM and Haptic User Input: Enhancing Automatic Motion Planning with Virtual Touch},
year = {2000}
}


Abstract

In this paper, we present a framework for studying ligand binding which is based on techniques recently developed in the robotics motion planning community. We are especially interested in locating binding sites on the protein for a ligand molecule. Our work investigates the performance of a fully automated motion planner, as well improvements obtained when supplementary user input is collected using a haptic device. Our results applying an obstacle-based probabilistic roadmap motion planning algorithm (obprm) to some known protein-ligand pairs are very encouraging. In particular, we were able to automatically generate congurations close to, and correctly identify, the true binding site in the three protein-ligand complexes we tested. We nd that user input helps the planner, and a haptic device helps the user to understand the protein structure by enabling them to feel the forces which are hard to visualize.


Choosing Good Distance Metrics and Local Planners for Probabilistic Roadmap Methods, N.M. Amato, O.B. Bayazit, L.K. Dale, C. Jones, D. Vallejo, IEEE Transactions on Robotics and Automation, Vol: 16, Issue: 4, pp. 442-447, Aug 2000. DOI: 10.1109/70.864240
Keywords: Sampling-Based Motion Planning
Links : [Published]

BibTex

@ARTICLE{864240,
author={Amato, N.M. and Bayazit, O.B. and Dale, L.K. and Jones, C. and Vallejo, D.},
journal={IEEE Transactions on Robotics and Automation},
title={Choosing good distance metrics and local planners for probabilistic roadmap methods},
year={2000},
volume={16},
number={4},
pages={442-447},
doi={10.1109/70.864240}}


Abstract

This paper presents a comparative evaluation of different distance metrics and local planners within the context of probabilistic roadmap methods for planning the motion of rigid objects in three-dimensional workspaces. The study concentrates on cluttered three-dimensional workspaces typical of, for example, virtual prototyping applications such as maintainability studies in mechanical CAD designs. Our results include recommendations for selecting appropriate combinations of distance metrics and local planners for such applications. Our study of distance metrics shows that the importance of the translational distance increases relative to the rotational distance as the environment becomes more crowded. We find that each local planner makes some connections that none of the others does-indicating that better connected roadmaps will be constructed using multiple local planners. We propose a new local planning method we call rotate-at-s that often outperforms the common straight-line in C-space method in crowded environments.


SmartApps: An Application Centric Approach to High Performance Computing, Lawrence Rauchwerger, Nancy Amato, Josep Torrellas, Languages and Compilers for Parallel Computing (LCPC.) Lecture Notes in Computer Science, Vol: 2017, pp. 82-96, Yorktown Heights, New York, USA, Aug 2000. DOI: 10.1007/3-540-45574-4_6
Keywords: Computer Architecture, High-Performance Computing, Optimizing Compilers
Links : [Published]

BibTex

@InProceedings{10.1007/3-540-45574-4_6,
author="Rauchwerger, Lawrence
and Amato, Nancy M.
and Torrellas, Josep",
editor="Midkiff, Samuel P.
and Moreira, Jos{\'e} E.
and Gupta, Manish
and Chatterjee, Siddhartha
and Ferrante, Jeanne
and Prins, Jan
and Pugh, William
and Tseng, Chau-Wen",
title="SmartApps: An Application Centric Approach to High Performance Computing",
booktitle="Languages and Compilers for Parallel Computing",
year="2001",
publisher="Springer Berlin Heidelberg",
address="Berlin, Heidelberg",
pages="82--96",
abstract="State-of-the-art run-time systems are a poor match to diverse, dynamic distributed applications because they are designed to provide support to a wide variety of applications, without much customization to individual specific requirements. Little or no guiding information flows directly from the application to the run-time system to allow the latter to fully tailor its services to the application. As a result, the performance is disappointing. To address this problem, we propose application-centric computing, or SMART APPLICATIONS. In the executable of smart applications, the compiler embeds most run-time system services, and a performance-optimizing feedback loop that monitors the application's performance and adaptively reconfigures the application and the OS/hardware platform. At run-time, after incorporating the code's input and the system's resources and state, the SmartApp performs a global optimization. This optimization is instance specific and thus much more tractable than a global generic optimization between application, OS and hardware. The resulting code and resource customization should lead to major speedups. In this paper, we first describe the overall architecture of Smartapps and then present the achievements to date: Run-time optimizations, performance modeling, and moderately reconfigurable hardware.",
isbn="978-3-540-45574-5"
}


Abstract

State-of-the-art run-time systems are a poor match to diverse, dynamic distributed applications because they are designed to provide support to a wide variety of applications, without much customization to individual specific requirements. Little or no guiding information flows directly from the application to the run-time system to allow the latter to fully tailor its services to the application. As a result, the performance is disappointing. To address this problem, we propose application-centric computing, or SMART APPLICATIONS. In the executable of smart applications, the compiler embeds most run-time system services, and a performance-optimizing feedback loop that monitors the application’s performance and adaptively reconfigures the application and the OS/hardware platform. At run-time, after incorporating the code’s input and the system’s resources and state, the SmartApp performs a global optimization. This optimization is instance specific and thus much more tractable than a global generic optimization between application, OS and hardware. The resulting code and resource customization should lead to major speedups. In this paper, we first describe the overall architecture of Smartapps and then present the achievements to date: Run-time optimizations, performance modeling, and moderately reconfigurable hardware.


Merging Physical Manipulatives and Digital Interface in Educational Software, Anna Zacchi, Nancy M. Amato, Proceedings of ED-MEDIA 2000 - World Conference on Educational Multimedia, Hypermedia & Telecommunications, pp. 1780-1781, Montreal, Canada, Jun 2000.
Keywords: Elementary Education
Links : [Published]

BibTex

@inproceedings{zacchi-edmedia-2000
, author = \"Anna Zacchi and Nancy M. Amato\"
, title = \"Merging Physical Manipulatives and Digital Interface in Educational Software\"
, booktitle = \"Proceedings of ED-MEDIA 2000 - World Conference on Educational Multimedia, Hypermedia \\& Telecommunications\"
, pages = \"1780-1781\"
, year = \"2000\"
, ISBN = \"978-1-880094-40-2\"
}


Abstract

In this paper we describe how elementary school students used physical manipulatives in conjunction with the digital interface of educational software for geometry. The blending of physical manipulatives and digital interface may help them to overcome the limits of the representation and interaction modalities of the digital interface.


Task Scheduling and Parallel Mesh-Sweeps in Transport Computations, Nancy M. Amato, Ping An, Technical Report, TR00-009, Department of Computer Science and Engineering, Texas A&M University, Jan 2000.
Keywords: Algorithmic Skeletons, Parallel Processing, Task Planning
Links : [Manuscript]

BibTex

@techreport{namato-tr00-009-2000,
title = "Task Scheduling and Parallel Mesh-Sweeps in Transport Computations",
author = "Nancy M. Amato, Ping An",
institution = "Texas A&M University",
address = "College Station, TX",
number = "TR00-009",
year = 2000,
month = jan
}


Abstract

In this report, we describe the relationship between task scheduling and deterministic mesh sweeps that arise in particle-transport computations. In particular, we argue that the efficient parallelization of such computations is most accurately viewed as a generalization of the traditional task scheduling problem and not as an application for domain decomposition, as has been generally assumed in the past. In fact, as we show, the transport problem represents an interesting composite task-scheduling problem: given one set of tasks, and multiple dependence graphs for these tasks (one for each distinguishable sweep direction), find an assignment of tasks to processors that minimizes the time required to process all such graphs. Within this context, our goal is to study and propose scheduling algorithms that are suitable for the particular generalization of the scheduling problem that arises in the context of transport sweeps. This report documents our progress to date and describes future plans. It consists of three parts. First, we define the traditional task scheduling problem and describe relevant related work. Second, we describe our progress in building a C++ task scheduling library that will provide a testbed in which to evaluate and compare the scheduling algorithms we design, including an experimental evaluation of the (traditional) scheduling algorithms implemented to date. Third, we briefly outline our plans for future work.


Motion planning for a rigid body using random networks on the medial axis of the free space, Steven A. Wilmarth, Nancy M. Amato, Peter F. Stiller, Proceedings of the Annual Symposium on Computational Geometry (SOCG), pp. 173-180, Jun 1999. DOI: 10.1145/304893.304967
Keywords: Computational Geometry, Medial Axis, Sampling-Based Motion Planning
Links : [Published]

BibTex

@INPROCEEDINGS{was-mprb-99,
author={Steven A. Wilmarth and and Nancy M. Amato and Peter F. Stiller},
booktitle={Proceedings of the Annual Symposium on Computational Geometry}
title={Motion Planning for a Rigid Body Using Random Networks on the Medial Axis of the Free Space},
year={1999},
pages={173-180},
doi={10.1145/304893.304967}}


Abstract

Several motion planning methods using networks of randomly generated nodes in the free space have been shown to perform well in a number of cases, however their performance degrades when paths are required to pass through narrow passages in the,jree space. In [16] we proposed MAPRM, a method of sampling the configuration space in which randomly generated configurations, free or not, are retracted onto the medial axis of the free space without having to first compute the medial axis; this was shown to increase sampling in narrow passages. In this paper we give details of the MAPRM algorithm for the case of a free-flying rigid body moving in three dimensions, and show that the retraction may be carried out without explicitly computing the C-obstacles or the medial axis. We give theoretical arguments to show that this improves sampling in narrow corridors, and present preliminary experimental results comparing the performance to uniform random sampling from the free space.


MAPRM: A Probabilistic Roadmap Planner with Sampling on the Medial Axis of the Free Space, S. A. Wilmarth, N. M. Amato, P. F. Stiller, Proceedings of IEEE International Conference on Robotics and Automation, pp. 1024-1031, Detroit, MI, May 1999. DOI: 10.1109/ROBOT.1999.772448
Keywords: Medial Axis, Sampling-Based Motion Planning
Links : [Published]

BibTex

@INPROCEEDINGS{772448,
author={Wilmarth, S.A. and Amato, N.M. and Stiller, P.F.},
booktitle={Proceedings 1999 IEEE International Conference on Robotics and Automation (Cat. No.99CH36288C)},
title={MAPRM: a probabilistic roadmap planner with sampling on the medial axis of the free space},
year={1999},
volume={2},
number={},
pages={1024-1031 vol.2},
doi={10.1109/ROBOT.1999.772448}}


Abstract

Probabilistic roadmap planning methods have been shown to perform well in a number of practical situations, but their performance degrades when paths are required to pass through narrow passages in the free space. We propose a new method of sampling the configuration space in which randomly generated configurations, free or not, are retracted onto the medial axis of the free space. We give algorithms that perform this retraction while avoiding explicit computation of the medial axis, and we show that sampling and retracting in this manner increases the number of nodes found in small volume corridors in a way that is independent of the volume of the corridor and depends only on the characteristics of the obstacles bounding it. Theoretical and experimental results are given to show that this improves performance on problems requiring traversal of narrow passages.


Probabilistic Roadmap Methods are Embarrassingly Parallel, N.M. Amato, L.K. Dale, Proceedings of IEEE International Conference on Robotics and Automation, pp. 688-694, Detroit, MI, USA, May 1999. DOI: 10.1109/ROBOT.1999.770055
Keywords: Parallel Algorithms, Parallel Planning, Sampling-Based Motion Planning
Links : [Published]

BibTex

@INPROCEEDINGS{770055,
author={Amato, N.M. and Dale, L.K.},
booktitle={Proceedings 1999 IEEE International Conference on Robotics and Automation (Cat. No.99CH36288C)},
title={Probabilistic roadmap methods are embarrassingly parallel},
year={1999},
volume={1},
number={},
pages={688-694 vol.1},
doi={10.1109/ROBOT.1999.770055}}


Abstract

In this paper we report on our experience in parallelizing probabilistic roadmap motion planning methods (PRMs). We show that significant, scalable speed-ups can be obtained with relatively little effort on the part of the developer. Our experience is not limited to PRMs. In particular, we outline general techniques for parallelizing types of computations commonly performed in motion planning algorithms, and identify potential difficulties that might be faced in other efforts to parallelize sequential motion planning methods.


Choosing Good Distance Metrics and Local Planners for Probabilistic Roadmap Methods, Nancy M. Amato, O. Burchan Bayazit, Lucia K. Dale, Christopher Jones, Daniel Vallejo, Proceedings of IEEE International Conference on Robotics and Automation, pp. 630-637, Leuven, Belgium, May 1998. DOI: 10.1109/ROBOT.1998.677043
Keywords: Sampling-Based Motion Planning
Links : [Published]

BibTex

@INPROCEEDINGS{677043,
author={Amato, N.M. and Bayazit, O.B. and Dale, L.K. and Jones, C. and Vallejo, D.},
booktitle={Proceedings. 1998 IEEE International Conference on Robotics and Automation (Cat. No.98CH36146)},
title={Choosing good distance metrics and local planners for probabilistic roadmap methods},
year={1998},
volume={1},
number={},
pages={630-637 vol.1},
doi={10.1109/ROBOT.1998.677043}}


Abstract

This paper presents a comparative evaluation of different distance metrics and local planners within the content of probabilistic roadmap methods for motion planning. Both C-space and workspace distance metrics and local planners are considered. The study concentrates on cluttered 3D workspaces, typical of mechanical designs. Our results include recommendations for selecting appropriate combinations of distance metrics and local planners for use in motion planning methods, particularly probabilistic roadmap methods. We find that each local planner makes some connections than none of the others do indicating that better connected roadmaps will be constructed using multiple local planners. We propose a new local planning method, we call rotate-at-s, that outperforms the common straight-line in C-space method in crowded environments.


OBPRM: An Obstacle-Based PRM for 3DWorkspaces, Nancy M. Amato, O. Burchan Bayazit, Lucia K. Dale, Christopher Jones, Daniel Vallejo, Robotics: The Algorithmic Perspective (Third Workshop on Algorithmic Foundations of Robotics, WAFR 1998), pp. 155-168, Houston, TX, Mar 1998.
Keywords: obstacle-based, Sampling-Based Motion Planning
Links : [Published]

BibTex

@inproceedings{ABDJV-wafr98-c
, author = "N. M. Amato and O. B. Bayazit and L. K. Dale and C. V. Jones and D. Vallejo"
, title = "{OBPRM:} An Obstacle-Based {PRM} for {3D} Workspaces"
, booktitle = "Proc. of Workshop on Algorithmic Foundations of Robotics {(WAFR'98)}"
, month = "March"
, pages = "155-168"
, year = "1998"
}


Abstract

Recently, a new class of randomized path planning methods, known as Probabilistic Roadmap Methods (prms) have shown great potential for solving compli­ cated high-dimensional problems, pr m s use randomiza­ tion (usually during preprocessing) to construct a graph of representative paths in C-space (a roadmap) whose vertices correspond to collision-free configurations of the robot and in which two vertices are connected by an edge if a path between the two corresponding config­ urations can be found by a local planning method.


A Randomized Roadmap Method for Path and Manipulation Planning, Nancy M. Amato, Yan Wu, Proceedings of IEEE International Conference on Robotics and Automation, Vol: 1, pp. 113-120, Minneapolis, MN, Apr 1996. DOI: 10.1109/ROBOT.1996.503582
Keywords: Sampling-Based Motion Planning
Links : [Published]

BibTex

@INPROCEEDINGS{503582,
author={Amato, N.M. and Wu, Y.},
booktitle={Proceedings of IEEE International Conference on Robotics and Automation},
title={A randomized roadmap method for path and manipulation planning},
year={1996},
volume={1},
number={},
pages={113-120 vol.1},
doi={10.1109/ROBOT.1996.503582}}


Abstract

This paper presents a new randomized roadmap method for motion planning for many DOF robots that can be used to obtain high quality roadmaps even when C-space is crowded. The main novelty in the authors' approach is that roadmap candidate points are chosen on C-obstacle surfaces. As a consequence, the roadmap is likely to contain difficult paths, such as those traversing long, narrow passages in C-space. The approach can be used for both collision-free path planning and for manipulation planning of contact tasks. Experimental results with a planar articulated 6 DOF robot show that, after preprocessing, difficult path planning operations can often be carried out in less than a second.


A Scalable Method for Run-Time Loop Parallelization, Lawrence Rauchwerger, Nancy M. Amato, David A. Padua, International Journal of Parallel Programming, Vol: 23, Issue: 6, pp. 537-576, Dec 1995. DOI: 10.1007/BF02577866
Keywords: Parallelising Compilers
Links : [Published]

BibTex

@article{cite-key,
Abstract = {Current parallelizing compilers do a reasonable job of extracting parallelism from programs with regular, well behaved, statically analyzable access patterns. However, they cannot extract a significant fraction of the avaialable, parallelism if the program has a complex and/or statically insufficiently defined access pattern, e.g., simulation programs with irregular domains and/or dynamically changing interactions. Since such programs represent a large fraction of all applications, techniques are needed for extracting their inherent parallelism at run-time. In this paper we give a new run-time technique for finding an optimal parallel execution schedule for a partially parallel loop, i.e., a loop whose parallelization requires synchronization to ensure that the iterations are executed in the correct order. Given the original loop, the compiler generatesinspector code that performas run-time preprocessing of the loop's access pattern, andscheduler code that schedules (and executes) the loop interations. The inspector is fully parallel, uses no sychronization, and can be applied to any loop (from which an inspector can be extracted). In addition, it can implement at run-time the two most effective transformations for increasing the amount of parallelism in a loop:array privatization andreduction parallelization (elementwise). The ability to identify privatizable and reduction variables is very powerful since it eliminates the data dependences involving these variables and},
Author = {Rauchwerger, Lawrence and Amato, Nancy M. and Padua, David A.},
Da = {1995/12/01},
Date-Added = {2020-11-02 22:20:42 -0600},
Date-Modified = {2020-11-02 22:20:42 -0600},
Doi = {10.1007/BF02577866},
Id = {Rauchwerger1995},
Isbn = {1573-7640},
Journal = {International Journal of Parallel Programming},
Number = {6},
Pages = {537--576},
Title = {A scalable method for run-time loop parallelization},
Ty = {JOUR},
Url = {https://doi.org/10.1007/BF02577866},
Volume = {23},
Year = {1995},
Bdsk-Url-1 = {https://doi.org/10.1007/BF02577866}}


Abstract

Current parallelizing compilers do a reasonable job of extracting parallelism from programs with regular, well behaved, statically analyzable access patterns. However, they cannot extract a significant fraction of the available, parallelism if the program has a complex and/or statically insufficiently defined access pattern, e.g., simulation programs with irregular domains and/or dynamically changing interactions. Since such programs represent a large fraction of all applications, techniques are needed for extracting their inherent parallelism at run-time. In this paper we give a new run-time technique for finding an optimal parallel execution schedule for a partially parallel loop, i.e., a loop whose parallelization requires synchronization to ensure that the iterations are executed in the correct order. Given the original loop, the compiler generates inspector code that perform as run-time preprocessing of the loop's access pattern, and scheduler code that schedules (and executes) the loop interations. The inspector is fully parallel, uses no sychronization, and can be applied to any loop (from which an inspector can be extracted). In addition, it can implement at run-time the two most effective transformations for increasing the amount of parallelism in a loop:array privatization and reduction parallelization (elementwise). The ability to identify privatizable and reduction variables is very powerful since it eliminates the data dependences involving these variables and


Run-Time Methods for Parallelizing Partially Parallel Loops, Lawrence Rauchwerger, Nancy M. Amato, David A. Padua, Proceedings of the 9th International Conference on Supercomputing, pp. 137-146, Barcelona, Spain, Jul 1995. DOI: 10.1145/224538.224553
Keywords: Data Dependence, Parallel Programming, Parallelising Compilers
Links : [Published]

BibTex

@inproceedings{10.1145/224538.224553,
author = {Rauchwerger, Lawrence and Amato, Nancy M. and Padua, David A.},
title = {Run-Time Methods for Parallelizing Partially Parallel Loops},
year = {1995},
isbn = {0897917286},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/224538.224553},
doi = {10.1145/224538.224553},
booktitle = {Proceedings of the 9th International Conference on Supercomputing},
pages = {137–146},
numpages = {10},
location = {Barcelona, Spain},
series = {ICS '95}
}


Abstract

In this paper we give a new run-time technique for finding an optimal parallel execution schedule for a partially parallel loop, i.e., a loop whose parallelization requires synchronization to ensure that the iterations are executed in the correct order. Given the original loop, the compiler generates inspector code that performs run-time preprocessing of the loop’s access pattern, and scheduler code that schedules (and executes) the loop iterations. The inspector is fully parallel, uses no synchronization, and can be applied to any loop. In addition, it can implement at run-time the two most effective transformations for increasing the amount of parallelism in a loop: array privatization and reduction parallelizatiort (element–wise). We also describe a new scheme for constructing an optimal parallel execution schedule for the iterations of the loop.


Parallel Algorithms for Convex Hulls and Proximity Problems, Nancy M. Amato, University of Illinois Urbana-Champaign, Jan 1995.
Keywords: Computational Geometry, High-Performance Computing, Parallel Algorithms
Links : [Published]

BibTex

@phdthesis{amato-phd-1995
, author = "Nancy M. Amato"
, title = "Parallel Algorithms for Convex Hulls and Proximity Problems"
, school = "Department of Computer Science, University of Illinois at Urbana-Champaign"
, year = "1995"
, month = "January"
, note = "https://search.proquest.com/openview/a46d67f1f648ab5badf409ac3ed51894/1?pq-origsite=gscholar&cbl=18750&diss=y"
}


Abstract

Computational geometry is concerned with the algorithmic aspects of solving geometric problems. The problems are motivated from and have application to such diverse areas as computer graphics, robotics, computer vision, and operations research. Problems arising from these areas of application are good candidates for parallelization since they often have both intense computational needs and stringent response time requirements. Motivated by these concerns, this thesis investigates parallel algorithms for some basic geometric problems. The model of parallel computation used in our studies is the Parallel Random Access Machine (CREW PRAM).


Checking Linked Data Structures, Nancy M. Amato and Michael C. Loui, Proceedings of IEEE 24th International Symposium on Fault-Tolerant Computing, pp. 164-173, Austin, TX, USA, Jun 1994. DOI: 10.1109/FTCS.1994.315644
Keywords: Algorithms, Data Structures
Links : [Published]

BibTex

@inproceedings{amato-ftcs94-c
, author = "Nancy M. Amato and Michael C. Loui"
, title = "Checking Linked Data Structures"
, booktitle = "Proceedings of the 24th Annual International Symposium on Fault-Tolerant Computing (FTCS)"
, year = 1994
, pages = "164--173"
, doi="10.1109/FTCS.1994.315644"
}


Abstract

In the program checking paradigm, the original program is run on the desired input, and its output is checked by another program called a checker. Recently, the notion of program checking has been extended from its original formulation of checking functions to checking a sequence of operations which query and alter the state of an object external to the program, e.g., checking the interactions between a client and the manager (server) of a data structure. In this expanded paradigm, the checker acts as an intermediary between the client, which generates the requests, and the server, which processes them. The checker is allowed a small amount of reliable memory and may provide a probabilistic guarantee of correctness for the client. We present off-line and on-line checkers for data structures such as linked lists, trees, and graphs. Previously, the only data structures for which such checkers existed were random access memories, stacks, and queues.


An NC1 Parallel 3D Convex Hull Algorithm, Nancy M. Amato and Franco P. Preparata, Proceedings of the Ninth Annual Symposium on Computational Geometry, pp. 289-297, Jul 1993. DOI: 10.1145/160985.161152
Keywords: Algorithms, Computational Geometry, Parallel Algorithms
Links : [Published]

BibTex

@inproceedings{amato-socg-93
, author = "N. M. Amato and F. P. Preparata"
, title = "An {NC}$^1$ Parallel {3D} Convex Hull Algorithm"
, booktitle = "Proceedings of the 9th Annual {ACM} Symposium on Computational Geometry"
, year = 1993
, pages = "289--297"
}


Abstract

In this paper we present an O(log n) time parallel algorithm for computing the convex hull of n points in R3. This algorithm uses O(n^(1+α)) processors on a CREW PRAM, for any constant 0 < α ≤ 1. So far, all adequately documented parallel algorithms proposed for this problem use time at least O (log^2 n). In addition, the algorithm presented here is the first parallel algorithm for the three-dimensional convex hull problem that is not based on the serial divide-and-conquer algorithm of Preparata and Hong, whose crucial operation is the merging of the convex hulls of two linearly separated point sets. The contributions of this paper are therefore (i) an O(logn) time parallel algorithm for the three-dimensional convex hull problem, and (ii) a parallel algorithm for this problem that does not follow the traditional divide-and-conquer paradigm.


Next Generation Geographic Modeling Framework Research at USACERL, Kurt Buehler, Jeffrey Wallace, Michael Shapiro, Nancy M. Amato, Unni Narayanan, GRASSClippings: The Journal of Open Geographic Information Systems, Vol: 6, Issue: 3, pp. 35-39, Dec 1992.
Keywords: Algorithms
Links : [Published]

BibTex

@article{
, author = "Kurt Buehler and Jeffrey Wallace and Michael Shapiro and Nancy M. Amato and Unni Narayanan"
, title = "Next Generation Geographic Modeling Framework Research at USACERL"
, journal = "GRASSClippings: The Journal of Open Geographic Information Systems"
, volume = "6"
, number = "3"
, year = "1992"
, pages = "35-39"
}


Abstract

N/A


The Parallel 3D Convex Hull Problem Revisited, Nancy M. Amato and Franco P. Preparata, International Journal of Computational Geometry & Applications, Vol: 2, Issue: 2, pp. 163-173, Jun 1992. DOI: 10.1142/S021819599200010X
Keywords: Computational Geometry, Parallel Algorithms
Links : [Published]

BibTex

@article{amato-ijcga-92
, author = "Nancy M. Amato and Franco P. Preparata"
, title = "The Parallel 3D Convex-Hull Problem Revisited"
, journal = "International Journal of Computational Geometry \& Applications"
, volume = 2
, number = 2
, month = "June"
, year = "1992"
, pages = "163--174"
, doi = "10.1142/S021819599200010X"
}


Abstract

In this paper we prove the correctness of a "local" criterion for computing the convex hull of the union ( "merging") of two disjoint convex polyhedra. This criterion is structural. Therefore it can be algorithmically tested in several ways, not necessarily involving the determination of support (tangent) planes; indeed, it can be implemented by just testing for the intersection of certain planes and lines with convex polytopes. This criterion is amenable to parallel implementation and leads to a provably correct algorithm that computes the convex hull of any n points in three-dimensional space in O(log2 n) time using O(n) processors on a CREW PRAM.


Reversing Trains: A Turn of the Century Sorting Problem, Nancy Amato, Manuel Blum, Sandra Irani, Ronitt Rubinfeld, Journal of Algorithms, Vol: 10, Issue: 3, pp. 413-428, Sep 1989. DOI: 10.1016/0196-6774(89)90037-0
Keywords: Algorithms
Links : [Published]

BibTex

@article{amato-joa89
, author = "Nancy Amato and Manuel Blum and Sandra Irani and Ronitt Rubinfeld"
, title = "Reversing Trains: A Turn of the Century Sorting Problem"
, journal = "Journal of Algorithms"
, volume = "10"
, number = "3"
, month = "September"
, year = "1989"
, pages = "413--428"
, note ="DOI: 10.1016/0196-6774(89)90037-0"
}


Abstract

In this paper we study a reversing train puzzle proposed by Sam Loyd near the turn of the century. We concern ourselves with a version of this puzzle described most recently by A. K. Dewdney in Scientific American. There is a train, locomotive and n cars, that must be entirely reversed using only a short spur line attached to the main track. The efficiency of a solution is determined by summing, for all cars, the total distance moved by each car, where distance is measured in car lengths. We present an O(n2 log2 n) algorithm for accomplishing this task.