Choosing good distance metrics and local planners for probabilistic roadmap methods

N. M. Amato, O. B. Bayazit, L. K. Dale, C. Jones, D. Vallejo

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

This paperpresents a cumpurative evuluatiun uf 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 localplanners. We propose a new local planning method we cull rotate-at-s that outperforms the common straight-line in C-space method in crowded environments.

Original languageEnglish (US)
Title of host publicationProceedings - 1998 IEEE International Conference on Robotics and Automation, ICRA 1998
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages630-637
Number of pages8
ISBN (Print)078034300X
DOIs
StatePublished - 1998
Externally publishedYes
Event15th IEEE International Conference on Robotics and Automation, ICRA 1998 - Leuven, Belgium
Duration: May 16 1998May 20 1998

Publication series

NameProceedings - IEEE International Conference on Robotics and Automation
Volume1
ISSN (Print)1050-4729

Other

Other15th IEEE International Conference on Robotics and Automation, ICRA 1998
CountryBelgium
CityLeuven
Period5/16/985/20/98

ASJC Scopus subject areas

  • Software
  • Control and Systems Engineering
  • Artificial Intelligence
  • Electrical and Electronic Engineering

Fingerprint Dive into the research topics of 'Choosing good distance metrics and local planners for probabilistic roadmap methods'. Together they form a unique fingerprint.

Cite this