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Capturing the Connectivity of High-Dimensional Geometric Spaces by Parallelizable Random Sampling Techniques

  • David Hsu
  • Jean-Claude Latombe
  • Rajeev Motwani
  • Lydia E. Kavraki
Part of the Combinatorial Optimization book series (COOP, volume 5)

Abstract

Applications such as robot programming, design for manufacturing, animation of digital actors, rationale drug design, and surgical planning, require computing paths in high-dimensional geometric spaces, a provably hard problem. Recently, a general path-planning approach based on a parallelizable random sampling scheme has emerged as an effective approach to solve this problem. In this approach, the path planner captures the connectivity of a space F by building a probabilistic roadmap, a network of simple paths connecting points picked at random in F. This paper combines results previously presented in separate papers. It describes a basic probabilistic roadmap planner that is easily parallelizable, and it analyzes the performance of this planner as a function of how well F satisfies geometric properties called -goodness, expansiveness, and path clearance. While -goodness allows us to study how well a probabilistic roadmap covers F, expansiveness and path clearance allow us to compare the connectivity of the roadmap to that of F.

Keywords

Free Space Path Planning Adequate Coverage Path Planner Narrow Passage 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Kluwer Academic Publishers 1999

Authors and Affiliations

  • David Hsu
    • 1
  • Jean-Claude Latombe
    • 1
  • Rajeev Motwani
    • 1
  • Lydia E. Kavraki
    • 2
  1. 1.Computer Science DepartmentStanford UniversityUSA
  2. 2.Computer Science DepartmentRice UniversityUSA

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