Home Page for Marco Morales Aguirre | Parasol Laboratory


Picture Marco Morales Aguirre
Teaching Associate Professor

Parasol Laboratory url: http://parasollab.web.illinois.edu/~moralesa/
Department of Computer Science email:
University of Illinois at Urbana-Champaign office: 3304 Siebel Center
Urbana, IL 61801, USA


Google Scholar

My research focuses on algorithms for motion planning and control with applications to autonomous robots, machine learning, computational geometry, bioinformatics, and computational neuroscience.


Publications

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.


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.


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.


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.


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.


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.


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.


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.


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.