ABSTRACT

Probabilistic roadmap methods have recently received considerable attention as a practical approach for mo­ tion planning in complex environments. These algo­ rithms sample a number of configurations in the free space and build a roadmap. Their performance varies as a function of the sampling strategies and relative configurations of the obstacles. To improve the perfor­ mance of the planner through narrow passages in con­ figuration space, some researchers have proposed algo­ rithms for sampling along or near the medial axis of the free space. However, their usage has been limited be­ cause of the practical complexity of computing the me­ dial axis or the cost of computing such samples.