Finding Collision Free Path with Probabilistic Roadmaps: The Bounding Volume Expansion
نویسنده
چکیده
The efficiency of Probabilistic Roadmaps (PRM) depends on the number of samples in free configuration space and computation time to connect such samples. Key idea of this approach is to generate samples with bounding volume expansion and connect free samples based on geometric characteristics so that straight–line local planner does not do collision check while it connects a sample to another. This paper presents a geometric analysis in configuration space with bounding volume expansion and a sampling scheme to generate sample densely enough. Experiments show that 5–links robot finds a collision–free path in the environment where some obstacles are placed.
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