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Home Page for Sam Rodriguez | Parasol Laboratory


Picture Sam Rodriguez
Postdoctoral Research Associate
Algorithms & Applications Group

Parasol Laboratory url: http://parasol.tamu.edu/~sor8786/
Department of Computer Science and Engineering email:
Texas A&M University office: 427d HRBB
College Station, TX 77843-3112 tel: (979) 847-8835
USA fax: (979) 458-0718


I am an IAMCS-KAUST Postdoctoral Research Associate in the Parasol Lab, Dept. of Computer Science and Engineering at Texas A&M University working with Dr. Nancy Amato. My work is focused on improving group behaviors in multi-agent systems by incorporating motion planning techniques with an agent-based approach. In our system, agents can have a range of behaviors, capabilities, levels of communication, knowledge of the environment and coordination . We investigate how agents can work cooperatively to perform tasks, plan paths in dynamic environments, and influence other groups of agents in an environment. The specific applications I have worked on are pursuit-evasion scenarios, evacuation behaviors and shepherding techniques. I have also worked on adapting various motion planning algorithms to produce more efficient and effective algorithms for path planning in high dimensional space.


CV (ps, pdf)


Research:

Evacuation and Direction: Behavior-Based Evacuation Planning
One important application of our work is evacuation planning. By being able to simulate agents that are evacuating an area, we can study the effects of things such as the number and placement of exits, how losing exits affects evacuation routes and times, or how evacuation times vary depending on the number, type and placement of barriers and directing agents available to control the evacuating agents.

In our initial work in this area, we study a scenario where some agents are attempting to evacuate the first floor of a building. The agents have to find paths to the safe areas. They use their knowledge of the environment (a roadmap) and information they learn about the situation by discovering barriers blocking routes or from directing agents (e.g., emergency response personal or posted signs) indicating which exists to use/avoid and/or which safe areas they should evacuate to.

Pursuit-Evasion Techniques
Pursuit and evasion are commonly studied behaviors. One group of agents, the pursuers, attempts to find and capture another group of agents, the evaders. The evaders attempt to remain undetected and once detected, attempt to escape and hide from the pursuers. We look at aspects of pursuit-evasion which involve studying searching techniques, pursuit strategies and evasion heuristics. We are particularly interested in studying the problem in more interesting scnearios that may include terrains, 3D, multi-level environments and in crowds.
A Framework for Planning Motion in Environments with Moving Obstacles
We present a heuristic approach to planning in an environment with moving obstacles. Our approach assumes that the robot has no knowledge of the future trajectory of the moving objects. Our framework also distinguishes between two types of moving objects:hard and soft objects in the environment. We distinguish between the two types of objects in the environment as varying application domains could allow for some collision between moving objects.
Planning Motion in Completely Deformable Environments
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 environments. In particular we apply a deformable model to the robot and obstacles in the environment and we 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.
Specialized Techniques for Shepherding Behaviors
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. In this work, we investigate ways to simulate these types of behaviors. When the size of the flock gets large or if the flock's behavior makes it difficult to influence, a single shepherd cannot adequately control the flock. In this work, we study how a group of shepherds can work cooperatively without communication to efficiently control the flock.
RESAMPL: A Region-Sensitive Adaptive Motion Planner
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. 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.
Obstacle-Based Rapidily-Exploring Random Tree (OBRRT)
Here we present a new variant of Rapidly-Exploring Random Tree (RRT) path planning algorithm that explores narrow passages or difficult areas more effectively. We show that both workspace obstacle information and C-space information can help decide which direction to grow. The method includes many ways to grow the tree, some taking into account the obstacles in the environment. Using obstacle hints for directions to grow a tree for path planning can be beneficial, especially when exploring difficult areas. Indeed, whereas the standard RRT can face difficulties planning in a narrow passage, the tree based planner presented here works best in these areas.
Feature-Sensitive Motion Planning
In this work, we propose a feature-sensitive meta-planner that cooperatively applies more specialized planners to map an specific instance. It analyzes a region to characterize it so to find the best-suited planning strategy for the region and to decide whether it needs to partition it and repeat the characterization until assigning a planning strategy to each distinct region in the planning space. Then, the meta-planner maps each region with the strategy assigned. Finally, regional maps are combined into a map for the overall problem.



Publications:

Motion Planning using Hierarchical Aggregation of Workspace Obstacles, Mukulika Ghosh, Shawna L. Thomas, Marco Morales Aguirre, Samuel Rodriguez, Nancy M. Amato, In Proc. IEEE Int. Conf. Intel. Rob. Syst. (IROS), Oct 2016.
Proceedings(pdf, abstract)

Planning Motions for Shape-Memory Alloy Sheets, Mukulika Ghosh, Daniel Tomkins, Jory Denny, Sam Rodriguez, Marco Morales Aguirre, Nancy M. Amato, Origami 6, 6(6):501-511, Dec 2015.
Journal(pdf)

Optimizing Aspects of Pedestrian Traffic in Building Designs, Samuel Rodriguez, Yinghua Zhang, Nicholas Gans, Nancy M. Amato, In Proc. IEEE Int. Conf. Intel. Rob. Syst. (IROS), Nov 2013.
Proceedings(pdf, abstract)

Adapting RRT Growth for Heterogeneous Environments, Jory Denny, Marco A. Morales A., Samuel Rodriguez, Nancy M. Amato, In Proc. IEEE Int. Conf. Intel. Rob. Syst. (IROS), pp. 1772 - 1778, Tokyo, Japan, Nov 2013.
Proceedings(ps, pdf, abstract)

Blind RRT: A Probabilistically Complete Distributed RRT, Cesar Rodriguez, Jory Denny, Sam Jacobs, Shawna L. Thomas, Nancy M. Amato, In Proc. IEEE Int. Conf. Intel. Rob. Syst. (IROS), pp. 1758 - 1765, Tokyo, Japan, Nov 2013.
Proceedings(ps, pdf, abstract)

Multi-Robot Caravanning, Jory Denny, Andrew Giese, Aditya Mahadevan, Arnaud Marfaing, Rachel Glockenmeier, Colton Revia, Samuel Rodriguez, Nancy M. Amato, In Proc. IEEE Int. Conf. Intel. Rob. Syst. (IROS), pp. 5722 - 5729, Tokyo, Japan, Nov 2013.
Proceedings(pdf, abstract)

Improving Aggregate Behavior in Parking Lots with Appropriate Local Maneuvers, Samuel Rodriguez, Andrew Giese, Nancy M. Amato, In Proc. IEEE Int. Conf. Intel. Rob. Syst. (IROS), Nov 2013.
Proceedings(pdf, abstract)

A Scalable Distributed RRT for Motion Planning, Sam Ade Jacobs, Nicholas Stradford, Cesar Rodriguez, Shawna Thomas, Nancy M. Amato, In Proc. IEEE Int. Conf. Robot. Autom. (ICRA), pp. 5088-5095, Karlsruhe, Germany, May 2013.
Proceedings(ps, pdf, abstract)

Environmental Effect on Egress Simulation, Samuel Rodriguez, Andrew Giese, Nancy M. Amato, Saeid Zarrinmehr, Firas Al-Douri, Mark Clayton, In Proc. of the 5th Intern. Conf. on Motion in Games (MIG), 2012, in Lecture Notes in Computer Science (LNCS), pp. to appear, Rennes, Brittany, France, Nov 2012.
Proceedings(ps, pdf, abstract)

Roadmap-Based Techniques for Modeling Group Behaviors in Multi-Agent Systems, Samuel Rodriguez, Ph.D. Thesis, Department of Computer Science and Engineering, Texas A&M University, Jan 2012.
Ph.D. Thesis(ps, pdf, abstract)

Roadmap-Based Level Clearing of Buildings, Samuel Rodriguez, Nancy M. Amato, In Proc. of the 4th Intern. Conf. on Motion in Games (MIG), 2011, in Lecture Notes in Computer Science (LNCS), pp. 340-352, Edinburgh, UK, Oct 2011.
Proceedings(ps, pdf, abstract)

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, Nancy M. Amato, In Proc. IEEE Int. Conf. Robot. Autom. (ICRA), pp. 1738-1745, May 2011.
Proceedings(ps, pdf, abstract)

Roadmap-Based Pursuit-Evasion in 3D Structures, Samuel Rodriguez, Jory Denny, Aditya Mahadevan, Jeremy (Cong-Trung) Vu, Juan Burgos, Takis Zourntos, Nancy M. Amato, In Proc. of 24th Intern. Conf. on Computer Animation and Social Agents (CASA), 2011, in Transactions on Edutainment, pp. to appear, May 2011.
Proceedings(ps, pdf, abstract)

Utilizing Roadmaps in Evacuation Planning, Samuel Rodriguez, Nancy M. Amato, In Proc. of 24th Intern. Conf. on Computer Animation and Social Agents (CASA), 2011, in Intern. J. of Virtual Reality (IJVR), pp. 67-73, May 2011.
Proceedings(ps, pdf, abstract)

Toward Simulating Realistic Pursuit-Evasion Using a Roadmap-Based Approach, Samuel Rodriguez, Jory Denny, Takis Zourntos, Nancy M. Amato, In Proc. of the 3rd Intern. Conf. on Motion in Games (MIG), 2010, in Lecture Notes in Computer Science (LNCS), pp. 82-93, Nov 2010.
Proceedings(ps, pdf, abstract)

Behavior-Based Evacuation Planning, Sam Rodriguez, Nancy M. Amato, In Proc. IEEE Int. Conf. Robot. Autom. (ICRA), pp. 350-355, Anchorage, AK, May 2010.
Proceedings(ps, pdf, abstract)

A Framework for Planning Motion in Environments with Moving Obstacles, Sam Rodriguez, Jyh-Ming Lien, Nancy M. Amato, In Proc. IEEE Int. Conf. Intel. Rob. Syst. (IROS), pp. 3309-3314, Oct 2007.
Proceedings(ps, pdf, abstract)

RESAMPL: A Region-Sensitive Adaptive Motion Planner, Samuel Rodriguez, Shawna Thomas, Roger Pearce, Nancy M. Amato, In Proc. Int. Wkshp. on Alg. Found. of Rob. (WAFR), pp. 285-300, New York City, NY, Jul 2006.
Proceedings(ps, pdf, abstract)

Planning Motion in Completely Deformable Environments, Samuel Rodriguez, Jyh-Ming Lien, N. M. Amato, In Proc. IEEE Int. Conf. Robot. Autom. (ICRA), pp. 2466-2471, Orlando, FL, May 2006.
Proceedings(ps, pdf, abstract)

An Obstacle-Based Rapidly-Exploring Random Tree, Samuel Rodriguez, Xinyu Tang, Jyh-Ming Lien, Nancy M. Amato, In Proc. IEEE Int. Conf. Robot. Autom. (ICRA), pp. 895-900, Orlando, FL, May 2006. Also, Technical Report, TR05-009, Parasol Laboratory, Department of Computer Science, Texas A&M University, Sep 2005.
Proceedings(ps, pdf, abstract) Technical Report(ps, pdf, abstract)

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 Int. Conf. Robot. Autom. (ICRA), pp. 3114-3119, Barcelona, Spain, May 2005. Also, Technical Report, TR04-004, Parasol Laboratory, Department of Computer Science, Texas A&M University, College Station, Texas, U.S.A., Sep 2004.
Proceedings(ps, pdf, abstract) Technical Report(ps, pdf, abstract)

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. Also, Technical Report, TR04-001, Parasol Laboratory, Department of Computer Science, Texas A&M University, College Station, Texas, U.S.A., Feb 2004.
Proceedings(ps, pdf, abstract) Technical Report(ps, pdf, abstract)

Shepherding Behaviors, Jyh-Ming Lien, O. Burchan Bayazit, Ross T. Sowell, Samuel Rodriguez, Nancy M. Amato, In Proc. IEEE Int. Conf. Robot. Autom. (ICRA), pp. 4159-4164, New Orleans, Apr 2004. Also, Technical Report, TR03-006, Parasol Laboratory, Department of Computer Science, Texas A&M University, Nov 2003.
Proceedings(ps, pdf, abstract) Technical Report(ps, pdf, abstract)

Improving the Connectivitiy of PRM Roadmaps, Marco Morales, Samuel Rodriguez, Nancy M. Amato, In Proc. IEEE Int. Conf. Robot. Autom. (ICRA), pp. 4427-4432, Taipei, Taiwan, Sep 2003.
Proceedings(ps, abstract)