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

  • Workshop on Randomized Parallel Computing Panos Pardalos, University of Florisa, Gainesvill Sanguthevar Rajasekaran, University of Florida, Gainesville
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Parallel and Distributed Processing (IPPS 1998)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 1388))

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Abstract

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.

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Author information

Authors and Affiliations

  1. Computer Science Department, Stanford University, 94305, Stanford, CA, USA

    David Hsu, Jean-Claude Latombel & Rajeev Motwani

  2. Computer Science Department, Rice University, 77005, Houston, TX, USA

    Lydia E. Kavraki

Authors
  1. David Hsu
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  2. Lydia E. Kavraki
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  3. Jean-Claude Latombel
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  4. Rajeev Motwani
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Editor information

José Rolim

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© 1998 Springer-Verlag Berlin Heidelberg

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Hsu, D., Kavraki, L.E., Latombel, JC., Motwani, R. (1998). Capturing the connectivity of high-dimensional geometric spaces by parallelizable random sampling techniques. In: Rolim, J. (eds) Parallel and Distributed Processing. IPPS 1998. Lecture Notes in Computer Science, vol 1388. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-64359-1_704

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  • DOI: https://doi.org/10.1007/3-540-64359-1_704

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-64359-3

  • Online ISBN: 978-3-540-69756-5

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