Choosing Good Distance Metrics and Local Planners for Probabilistic Roadmap Methods

Nancy M. Amato
Texas A&M University
amato@cs.tamu.edu
O. Burchan Bayazit
Texas A&M University
burchanb@cs.tamu.edu
Lucia K. Dale
Texas A&M University
dalel@cs.tamu.edu

Christopher Jones
Texas A&M University
cvj3341@cs.tamu.edu
Daniel Vallejo
Texas A&M University
dvallejo@cs.tamu.edu

Abstract

This paper 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. 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.

In Proceedings of the 1998 IEEE International Conference on Robotics and Automation (ICRA'98), pp. 630-637, 1998. Full Paper (postscript)