A general framework for PRM motion planning

Guang Song, Shawna Thomas, Nancy M. Amato

Research output: Contribution to journalConference articlepeer-review


An important property of PRM roadmaps is that they provide a good approximation of the connectivity of the free C-space. We present a general framework for building and querying probabilistic roadmaps that includes all previous PRM variants as special cases. In particular, it supports no, complete, or partial node and edge validation and various evaluation schedules for path validation, and it enables path customization for variable, adaptive query requirements. While each of the above features is present in some PRM variant, the general framework proposed here is the only one to include them all. Our framework enables users to choose the best approximation level for their problem. Our experimental evidence shows this can result in significant performance gains.

Original languageEnglish (US)
Pages (from-to)4445-4450
Number of pages6
JournalProceedings - IEEE International Conference on Robotics and Automation
StatePublished - Dec 9 2003
Externally publishedYes
Event2003 IEEE International Conference on Robotics and Automation - Taipei, Taiwan, Province of China
Duration: Sep 14 2003Sep 19 2003

ASJC Scopus subject areas

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


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