A probabilistic learning approach to motion planning
WAFR Proceedings of the workshop on Algorithmic foundations of robotics
Motion planning for carlike robots using a probabilistic learning approach
International Journal of Robotics Research
The complexity of the two dimensional curvature-constrained shortest-path problem
WAFR '98 Proceedings of the third workshop on the algorithmic foundations of robotics on Robotics : the algorithmic perspective: the algorithmic perspective
Robot Motion Planning and Control
Robot Motion Planning and Control
An Adaptive Framework for `Single Shot'' Motion Planning
An Adaptive Framework for `Single Shot'' Motion Planning
A reactive lazy PRM approach for nonholonomic motion planning
IBERAMIA-SBIA'06 Proceedings of the 2nd international joint conference, and Proceedings of the 10th Ibero-American Conference on AI 18th Brazilian conference on Advances in Artificial Intelligence
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In this paper we describe an approach to probabilistic roadmap method. Our algorithm builds initially a roadmap in the configuration space considering that all nodes and edges are collision-free, and searches the roadmap for the shortest path between start and goal nodes. If a collision with the obstacles occurs, the corresponding nodes and edges are removed from the roadmap or the planner updates the roadmap with new nodes and edges, and then searches for a shortest path. The procedure is repeated until a collision-free path is found. The goal of our approach is to minimize the number of collision checks and calls to the local method. Experimental results presented in this paper show that our approach is very efficient in practice.