Project Personnel:Nancy Amato
Probabilistic RoadMap (PRM) motion planners use randomization to construct a graph (roadmap) in C-space in which nodes correspond to collision-free robot configurations and two nodes are connected by an edge if a 'local planner' can connect them. Queries are then processed by connecting the start and goal to the roadmap. In this project, we are developing an obstacle-based PRM (OBPRM). The main novelty in our approach is that roadmap nodes are sampled on or near C-obstacle surfaces (corresponding to contact configurations). We have shown that this strategy improves the quality of roadmaps for crowded environments.
Choosing Good Distance Metrics and Local Planners for Probabilistic Roadmap Methods, Nancy M. Amato, O. Burchan Bayazit, Lucia K. Dale, Christopher Jones, Daniel Vallejo, IEEE Transactions on Robotics and Automation, 16(4):442-447, Aug 2000. Also, Technical Report, TR98-010, Department of Computer Science and Engineering, Texas A&M University, May 1998.
OBPRM: An Obstacle-Based PRM for 3D Workspaces, Nancy M. Amato, O. Burchan Bayazit, Lucia K. Dale, Christopher Jones, Daniel Vallejo, In Proc. Int. Wkshp. on Alg. Found. of Rob. (WAFR), pp. 155-168, Houston, TX, Mar 1998.
A Randomized Roadmap Method for Path and Manipulation Planning, Nancy M. Amato, Yan Wu, In Proc. IEEE Int. Conf. Robot. Autom. (ICRA), pp. 113-120, Minneapolis, MN, Apr 1996.
Supported by NSF, Dept. of Education, Texas Higher Education Coordinating Board
Project Alumni:O. Burchan Bayazit,Lucia K. Dale,Christopher Jones,Guang Song,Daniel Vallejo,Yan Wu
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