Choosing good distance metrics and local planners for probabilistic roadmap methods

• Published in: IEEE transactions on robotics and automation Vol. 16; no. 4; pp. 442 - 447
• Main Authors: Amato, N.M., Bayazit, O.B., Dale, L.K., Jones, C., Vallejo, D.
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.08.2000; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Application software; Automation; Computer aided design; Computer science; Concentrates; Construction; Design automation; Joining processes; Joints; Motion planning; Probabilistic methods; Probability; Probability theory; Robot kinematics; Robotics and automation; Rotational; Scholarships; Virtual prototyping; Virtual reality
• ISSN: 1042-296X; 2374-958X
• DOI: 10.1109/70.864240

This paper presents a comparative evaluation of different distance metrics and local planners within the context of probabilistic roadmap methods for planning the motion of rigid objects in three-dimensional workspaces. The study concentrates on cluttered three-dimensional workspaces typical of, for example, virtual prototyping applications such as maintainability studies in mechanical CAD designs. Our results include recommendations for selecting appropriate combinations of distance metrics and local planners for such applications. Our study of distance metrics shows that the importance of the translational distance increases relative to the rotational distance as the environment becomes more crowded. We find that each local planner makes some connections that none of the others does-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 often outperforms the common straight-line in C-space method in crowded environments.