Capturing the connectivity of high-dimensional geometric spaces by parallelizable random sampling techniques

  • David Hsu
  • Lydia E. Kavraki
  • Jean-Claude Latombel
  • Rajeev Motwani
Workshop on Randomized Parallel Computing Panos Pardalos, University of Florisa, Gainesvill Sanguthevar Rajasekaran, University of Florida, Gainesville
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1388)


Finding paths in high-dimensional gemetric spaces is a provably hard problem. Recently, a general randomized planning scheme has emerged as an effective approach to solve this problem. In this scheme, the planner samples the space at random and build a network of simple paths, called a probabilistic roadmap. This paper describes a basic probabilistic roadmap planner, which is easily parallelizable, and provides a formal analysis that explains its empirical success when the space satisfies two geometric properties called e-goodness and expansiveness.


Free Space Motion Planning Path Planning Configuration Space Adequate Coverage 
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

© Springer-Verlag Berlin Heidelberg 1998

Authors and Affiliations

  • David Hsu
    • 1
  • Lydia E. Kavraki
    • 2
  • Jean-Claude Latombel
    • 1
  • Rajeev Motwani
    • 1
  1. 1.Computer Science DepartmentStanford UniversityStanfordUSA
  2. 2.Computer Science DepartmentRice UniversityHoustonUSA

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