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Probabilistic Roadmaps for Path Planning in High-Dimensional Configuration Spaces (2002)

Abstract
A new' motion planning method for robots in static workspaces is presented. This method proceeds in two phases: a learning phase and a query phase. In the learning phase, a probabilistic roadmap is constructed and stored as a graph whose nodes correspond to collision-free configurations and whose edges correspond to feasible paths between these configurations. These paths are computed using a simple and fast local planner. In the query phase, any given start and goal configurations of the robot are connected to two nodes of the roadmap; the roadmap is then searched for a path joining these two nodes. The method is general and easy to implement.

Publication details
Download http://citeseer.ist.psu.edu/539368.html
Source http://www.cs.rice.edu/CS/Robotics/papers/./kavraki1996prm-high-dim-conf.pdf
Publisher unknown
Contributors The Pennsylvania State University CiteSeer Archives
Repository CiteSeer (United States)
Keywords Lydia E. Kavraki Petr Vestka,Jean-claude Latombe,Mark H. Overmars Probabilistic Roadmaps for Path Planning in High-Dimensional Configuration Spaces
Language Englisch