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Roger Pearce is a Ph.D. student in the Department of Computer Science at Texas A&M University. He is a member
of the Parasol Lab, under the guidance of Dr. Nancy Amato.
He received a B.S. in Computer Engineering from the Department of Electrical Engineering at Texas A&M University
in 2004.
Roger's current research includes: Robotic Motion Planning, Computational GeoPhysics and a project aimed to
use motion planning techniques for personal navigation -- The Campus Navigator. In addition, his general
research interests are in computational science (biology, geophysics), robotics, artificial intelligence, and global optimization
problems.
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Metrics for Probabilistic Motion Planning
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. |
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Incremental Map Generation
Probabilistic roadmap methods (PRMs) have been highly successful in solving many high degree of freedom problems. One important practical issue with PRMs is they do not provide an automated mechanism to determine what size roadmap to construct. In this work, 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 independent processes, each of which generates samples and connections independently. IMG proceeds by adding these collections of samples and connections to an existing roadmap until it satisfies some specified evaluation criteria. We propose some general evaluation criteria and show how they can be used to construct different types of roadmaps, e.g., roadmaps that coarsely or more finely map the space. In addition to addressing the roadmap size question, the fact that each roadmap increment is independently and deterministically seeded has several other benefits such as supporting roadmap reproducibility, the adaptive selection of sampling methods in different roadmap increments, and parallelization. We provide results illustrating the power of IMG. |
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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. |
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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. |
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Seismic Ray Tracing
This project involves both Computer Science and Geo Sciences. Seismic Ray Tracing is often used to get information about the Earth's interior. It has its applications in a number of areas in GeoSciences including oil exploration and quake fault analysis. An efficient sequential algorithm for ray tracing is being developed by the research group of Dr. Gibson of the Department Geology and GeoPhysics at Texas A&M University. Our lab has worked on the parallel computing and performance modeling aspects of the project. We will be working on developing an efficient parallel algorithm for Seismic Ray Tracing. The second phase of the project deals with implementing the algorithm in a parallel machine using the Standard Template Adaptive Parallel Library (STAPL) which is being developed at Texas A&M. |
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TAMU Campus Navigator
In this project, we incorporate roadmap-based path planning techniques to a web-based route planner that covers the Texas A&M campus. The goal is to allow users to quickly find directions to all TAMU buildings, departments, and major services. Transportation information (e.g. bus routes and parking lots) is incorporated to provide meaningful answers to users questions such as "How do I get from the Bright building to Reed Arena, taking an on-campus buss?" We use a layered roadmap approach to compose multiple transportation methods into a single queryable roadmap. The user interface is implemented using Google Maps API. |
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Feature-Based Mobile Robot Localization and Navigation
Personal robotics applications require autonomous mobile robot navigation methods that are robust and inexpensive. We are researching on a method for navigation in a known indoor environment, such as a home or office, that requires only inexpensive range sensors such as sonar 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 sub-goals 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. |
Roger Pearce, Marco Morales, Nancy M. Amato, "Structural Improvement Filtering Strategy for PRM," In Proc. Int. Conf. on Robotics: Science and Systems, pp. 167-174, Zurich, Switzerland, Jun 2008.
Proceedings(pdf, abstract)
Marco Morales, Roger Pearce, Nancy M. Amato, "Analysis of the Evolution of C-Space Models built through Incremental Exploration," In Proc. IEEE Int. Conf. Robot.
Autom. (ICRA), pp. 1029-1034, Rome, Italy, Apr 2007. Also, Technical Report, TR06-013, Parasol Laboratory, Department of Computer Science, Texas A&M University, College Station, Texas, U.S.A., Sep 2006.
Proceedings(ps, pdf, abstract) Technical Report(ps, pdf, abstract)
Roger Pearce, Bryan Boyd, Xinyu Tang, Darla Haigler, Akhil Patel, Nancy M. Amato, "Supporting Path Planning Queries Incorporating Multiple Modes of Transportation using Layered Roadmaps," Technical Report, TR06-014, Parasol Laboratory, Department of Computer Science, Texas A&M University, Oct 2006.
Samuel Rodriguez, Shawna Thomas, Roger Pearce, Nancy M. Amato, "RESAMPL: A Region-Sensitive Adaptive Motion Planner," In Proc. Int. Wkshp. on Alg.
Found. of Rob. (WAFR), New York City, NY, Jul 2006. Also, Technical Report, TR06-004, Parasol Laboratory, Department of Computer Science, Texas A&M University, Mar 2006.
Proceedings(ps, pdf, abstract) Technical Report(ps, pdf, abstract)
Dawen Xie, Marco Morales, Roger Pearce, Shawna Thomas, Jyh-Ming Lien, Nancy M. Amato, "Incremental Map Generation (IMG)," In Proc. Int. Wkshp. on Alg.
Found. of Rob. (WAFR), New York City, NY, Jul 2006. Also, Technical Report, TR06-005, Department of Computer Science and Engineering, Texas A&M University, Mar 2006.
Proceedings(ps, pdf, abstract) Technical Report(ps, pdf, abstract)
Marco A. Morales A., Roger Pearce, Nancy M. Amato, "Metrics for Analyzing the Evolution of C-Space Models," In Proc. IEEE Int. Conf. Robot.
Autom. (ICRA), pp. 1268-1273, Orlando, Florida, U.S.A., May 2006.
Proceedings(ps, pdf, abstract)
Marco A. Morales A., Roger Pearce, Aimée Vargas E., Nancy M. Amato, "Metrics for Comparing C-space Roadmaps," Technical Report, TR05-012, Parasol Laboratory, Department of Computer Science, Texas A&M University, College Station, Texas, U.S.A., Sep 2005.
Technical Report(ps, pdf, abstract)
Marco A. Morales A., Lydia Tapia, Roger Pearce, Samuel Rodriguez, Nancy M. Amato, "C-Space Subdivision and Integration in Feature-Sensitive Motion Planning," 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)
Marco Morales, Lydia Tapia, Roger Pearce, Samuel Rodriguez, Nancy M. Amato, "A Machine Learning Approach for Feature-Sensitive Motion Planning," 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)
Jinsuck Kim, Roger A. Pearce, Nancy M. Amato, "Extracting Optimal Paths from Roadmaps for Motion Planning," In Proc. IEEE Int. Conf. Robot.
Autom. (ICRA), pp. 2424-2429, vol 2, Sep 2003.
Proceedings(ps, pdf)
Jinsuck Kim, Roger A. Pearce, Nancy M. Amato, "Feature-Based Localization using Scannable Visibility Sectors," In Proc. IEEE Int. Conf. Robot.
Autom. (ICRA), pp. 2854-2859, vol 2, Sep 2003.
Proceedings(ps, pdf)
Jinsuck Kim, Roger A. Pearce, Nancy M. Amato, "Robust Geometric-Based Localization in Indoor Environments Using Sonar Sensors," In Proc. IEEE Int. Conf. Intel.
Rob. Syst. (IROS), pp. 421-426, Oct 2002.
Proceedings(ps, pdf)
Jinsuck Kim, Roger A. Pearce, Nancy M. Amato, "Multiple Robot Navigation and Localization Using Sonar Sensors in an Indoor Environment," Technical Report, TR01-004, Parasol Laboratory, Department of Computer Science, Texas A&M University, Oct 2001.
Technical Report(ps, pdf)
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