Structural improvement filtering strategy for PRM

Roger Pearce, Marco Morales, Nancy M. Amato

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

Abstract

Sampling based motion planning methods have been highly successful in solving many high degree of freedom motion planning problems arising in diverse application domains such as traditional robotics, computer-aided design, and computational biology and chemistry. Recent work in metrics for sampling based planners provide tools to analyze the model building process at three levels of detail: sample level, region level, and global level. These tools are useful for comparing the evolution of sampling methods, and have shown promise to improve the process altogether [15], [17], [24]. Here, we introduce a filtering strategy for the Probabilistic Roadmap Methods (PRM) with the aim to improve roadmap construction performance by selecting only the samples that are likely to produce roadmap structure improvement. By measuring a new sample's maximum potential structural improvement with respect to the current roadmap, we can choose to only accept samples that have an adequate potential for improvement. We show how this approach can improve the standard PRM framework in a variety of motion planning situations using popular sampling techniques.

Original languageEnglish (US)
Title of host publicationRobotics
Subtitle of host publicationScience and Systems IV
EditorsOliver Brock, Jeff Trinkle, Fabio Ramos
PublisherMIT Press Journals
Pages167-174
Number of pages8
ISBN (Print)9780262513098
StatePublished - Jan 1 2009
Externally publishedYes
EventInternational Conference on Robotics Science and Systems, RSS 2008 - Zurich, Switzerland
Duration: Jun 25 2008Jun 28 2008

Publication series

NameRobotics: Science and Systems
Volume4
ISSN (Print)2330-7668
ISSN (Electronic)2330-765X

Other

OtherInternational Conference on Robotics Science and Systems, RSS 2008
CountrySwitzerland
CityZurich
Period6/25/086/28/08

ASJC Scopus subject areas

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

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  • Cite this

    Pearce, R., Morales, M., & Amato, N. M. (2009). Structural improvement filtering strategy for PRM. In O. Brock, J. Trinkle, & F. Ramos (Eds.), Robotics: Science and Systems IV (pp. 167-174). (Robotics: Science and Systems; Vol. 4). MIT Press Journals.