Motion Planning for Car-Like Robots Using Lazy Probabilistic Roadmap Method
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.
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