Choosing good distance metrics and local planners for probabilistic roadmap motion planning methods

Abstract

This thesis presents a comparative evaluation of different distance metrics and local planners within the context of probabilistic roadmap methods for motion planning. Both C-space and Workspace distance metrics and local planners are considered. The study concentrates on cluttered three-dimensional Workspaces typical, e.g., of mechanical designs. Our results include recommendations for selecting appropriate combinations of distance metrics and local planners for use in motion planning methods, particularly probabilistic roadmap methods. Our study of distance metrics showed that the importance of the translational distance increased relative to the rotational distance as the environment become more crowded. We find that each local planner makes some connections than none of the others do-indicating that better connected roadmaps will be constructed using multiple local planners. We propose a new local planning method we call rotate-at-s that outperforms the common straight-line in C-space method in crowded environments.