@misc{solis2023adaptive,
title={Adaptive Robot Coordination: A Subproblem-based Approach for Hybrid Multi-Robot Motion Planning},
author={Irving Solis and James Motes and Mike Qin and Marco Morales and Nancy M. Amato},
year={2023},
eprint={2312.08554},
archivePrefix={arXiv},
primaryClass={cs.RO}
}

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 inexpensive hybrid exploration of the multi-robot planning space. ARC leverages the hybrid exploration by dynamically adjusting the coupling and decoupling of the multi-robot planning space. 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 efficient cost 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.

@misc{mcbeth2023hypergraphbased,
title={Hypergraph-based Multi-robot Motion Planning with Topological Guidance},
author={Courtney McBeth and James Motes and Marco Morales and Nancy M. Amato},
year={2023},
eprint={2311.10176},
archivePrefix={arXiv},
primaryClass={cs.RO}
}

We present a multi-robot motion planning algorithm that efficiently finds paths for robot teams up to ten times larger than existing methods in congested settings with narrow passages in the environment. Narrow passages represent a source of difficulty for sampling-based motion planning algorithms. This problem is exacerbated for multi-robot systems where the planner must also avoid inter-robot collisions within these congested spaces, requiring coordination. Topological guidance, which leverages information about the robot's environment, has been shown to improve performance for mobile robot motion planning in single robot scenarios with narrow passages. Additionally, our prior work has explored using topological guidance in multi-robot settings where a high degree of coordination is required of the full robot group. This high level of coordination, however, is not always necessary and results in excessive computational overhead for large groups. Here, we propose a novel multi-robot motion planning method that leverages topological guidance to inform the planner when coordination between robots is necessary, leading to a significant improvement in scalability.

@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}
}

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.

@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}}

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.

@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}
}

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.

@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}
}

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.

@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}}

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.

@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}
}

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.

@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}
}

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.

@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}
}

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.

@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}
}

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.

@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}
}

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.

@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}}

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.

@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"
}

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.

@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}}

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.

@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}}

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.

@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}
}

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.

@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}}

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.

@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."
}

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.