Title :
Choosing good distance metrics and local planners for probabilistic roadmap methods
Author :
Amato, Nancy M. ; Bayazit, O. Burchan ; Dale, Lucia K. ; Jones, Christopher ; Vallejo, Daniel
Author_Institution :
Dept. of Comput. Sci., Texas A&M Univ., College Station, TX, USA
fDate :
8/1/2000 12:00:00 AM
Abstract :
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
Keywords :
CAD; maintenance engineering; mechanical engineering computing; path planning; probability; cluttered 3D workspaces; distance metrics; local planners; maintainability studies; mechanical CAD designs; probabilistic roadmap methods; rigid object motion planning; rotate-at-s planning; rotational distance; translational distance; virtual prototyping applications; Application software; Computer science; Design automation; Joining processes; Motion planning; Robot kinematics; Robotics and automation; Scholarships; Virtual prototyping; Virtual reality;
Journal_Title :
Robotics and Automation, IEEE Transactions on