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GPU-Based Parallel Collision Detection for Real-Time Motion Planning

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Algorithmic Foundations of Robotics IX

Part of the book series: Springer Tracts in Advanced Robotics ((STAR,volume 68))

Abstract

We present parallel algorithms to accelerate collision queries for sample-based motion planning. Our approach is designed for current many-core GPUs and exploits the data-parallelism and multi-threaded capabilities. In order to take advantage of high number of cores, we present a clustering scheme and collision-packet traversal to perform efficient collision queries on multiple configurations simultaneously. Furthermore, we present a hierarchical traversal scheme that performs workload balancing for high parallel efficiency. We have implemented our algorithms on commodity NVIDIA GPUs using CUDA and can perform 500,000 collision queries/second on our benchmarks, which is 10X faster than prior GPU-based techniques. Moreover, we can compute collision-free paths for rigid and articulated models in less than 100 milliseconds for many benchmarks, almost 50-100X faster than current CPU-based planners.

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Author information

Authors and Affiliations

  1. University of North Carolina, Chapel Hill, NC, USA

    Jia Pan & Dinesh Manocha

Authors
  1. Jia Pan
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  2. Dinesh Manocha
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Editor information

Editors and Affiliations

  1. School of Computing, National University of Singapore, 13 Computing Drive, 117417, Singapore

    David Hsu

  2. Department of Computer Science, University of Minnesota, 200 Union Street SE, 55455, Minneapolis, MN, USA

    Volkan Isler

  3. Computer Science Department, Stanford University, 94305-9010, Stanford, CA, USA

    Jean-Claude Latombe

  4. Department of Computer Science, University of North Carolina, Chapel Hill, 254 Brooks Building, 27599, Chapel Hill, NC, USA

    Ming C. Lin

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Pan, J., Manocha, D. (2010). GPU-Based Parallel Collision Detection for Real-Time Motion Planning. In: Hsu, D., Isler, V., Latombe, JC., Lin, M.C. (eds) Algorithmic Foundations of Robotics IX. Springer Tracts in Advanced Robotics, vol 68. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-17452-0_13

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  • DOI: https://doi.org/10.1007/978-3-642-17452-0_13

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-17451-3

  • Online ISBN: 978-3-642-17452-0

  • eBook Packages: EngineeringEngineering (R0)

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