Choosing good distance metrics and local planners for probabilistic roadmap methods

This paper presents a comparative evaluation of different distance metrics and local planners within the content of probabilistic roadmap methods for motion planning. Both C-space and workspace distance metrics and local planners are considered. The study concentrates on cluttered 3D workspaces, typ...

Full description

Saved in:
Bibliographic Details
Published inProceedings. 1998 IEEE International Conference on Robotics and Automation (Cat. No.98CH36146) Vol. 1; pp. 630 - 637 vol.1
Main Authors Amato, N.M., Bayazit, O.B., Dale, L.K., Jones, C., Vallejo, D.
Format Conference Proceeding
LanguageEnglish
Published IEEE 1998
Subjects
Computer science
Design automation
Engineering profession
Motion planning
Orbital robotics
Path planning
Robot kinematics
Robotics and automation
Scholarships
Virtual reality
Online AccessGet full text

Cover

More Information
Summary:This paper presents a comparative evaluation of different distance metrics and local planners within the content of probabilistic roadmap methods for motion planning. Both C-space and workspace distance metrics and local planners are considered. The study concentrates on cluttered 3D workspaces, typical 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 ndicating 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.
ISBN:078034300X
9780780343009
ISSN:1050-4729
2577-087X
DOI:10.1109/ROBOT.1998.677043