Adaptively combining multiple sampling strategies for probabilistic roadmap planning

David Hsu, Zheng Sun

Research output: Chapter in book/report/conference proceedingConference proceedingpeer-review

14 Citations (Scopus)

Abstract

Several sophisticated sampling strategies have been proposed recently to address the narrow passage problem for probabilistic roadmap (PRM) planning. They all have unique strengths and weaknesses in different environments, but in general, none seems sufficient on its own. In this paper, we present a new approach that adaptively combines multiple sampling strategies for PRM planning. Using this approach, we describe an adaptive hybrid sampling (AHS) strategy using two component samplers: the bridge test, a specialized sampler for narrow passages, and the uniform sampler. We tested the AHS strategy on robots with two to eight degrees of freedom. These preliminary tests show that the AHS strategy achieves consistently good performance, compared with fixed-weight hybrid sampling strategies.
Original languageEnglish
Title of host publicationIEEE Conference on Robotics, Automation and Mechatronics, 2004
PublisherIEEE
Pages774-779
Number of pages6
Volume2
ISBN (Print)0780386450
DOIs
Publication statusPublished - 3 Dec 2004
Externally publishedYes
EventIEEE Conference on Robotics, Automation and Mechatronics 2004 - , Singapore
Duration: 1 Dec 20043 Dec 2004
https://ieeexplore.ieee.org/xpl/conhome/9824/proceeding (Conference proceedings)

Conference

ConferenceIEEE Conference on Robotics, Automation and Mechatronics 2004
Country/TerritorySingapore
Period1/12/043/12/04
Internet address

User-Defined Keywords

  • Sampling methods
  • Strategic planning
  • Testing
  • Bridges
  • Automated highways
  • Computer science
  • Orbital robotics
  • Robots
  • Motion planning
  • Computational geometry

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