Using workspace information as a guide to non-uniform sampling in probabilistic roadmap planners

Abstract

The probabilistic roadmap (PRM) planner is a popular method for robot motion planning problems with many degrees of freedom. However, it has been shown that the method performs less well in situations where the robot has to pass through a narrow passage in the scene. This is mainly due to the uniformity of the sampling used in the planner; it places many samples in large open regions and too few in tight passages. A technique based on a robot independent cell decomposition of the free workspace is proposed to guide the probabilistic sampling, such that the distribution of samples tends more toward the interesting regions in the scene. It is shown that this leads to improved performance on difficult planning problems in 2D and 3D workspaces.