Probabilistic roadmap methods are embarrassingly parallel

Nancy M. Amato, Lucia K. Dale

Research output: Contribution to journalConference articlepeer-review


In this paper we report on our experience parallelizing probabilistic roadmap motion planning methods (PRMs). We show that significant, scalable speedups can be obtained with relatively little effort on the part of the developer. Our experience is not limited to PRMs, however. In particular, we outline general techniques for parallelizing types of computations commonly performed in motion planning algorithms, and identify potential difficulties that might be faced in other efforts to parallelize sequential motion planning methods.

Original languageEnglish (US)
Pages (from-to)688-694
Number of pages7
JournalProceedings - IEEE International Conference on Robotics and Automation
StatePublished - 1999
Externally publishedYes
EventProceedings of the 1999 IEEE International Conference on Robotics and Automation, ICRA99 - Detroit, MI, USA
Duration: May 10 1999May 15 1999

ASJC Scopus subject areas

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


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