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Sparse Methods for Efficient Asymptotically Optimal Kinodynamic Planning

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Algorithmic Foundations of Robotics XI

Part of the book series: Springer Tracts in Advanced Robotics ((STAR,volume 107))

Abstract

This work describes STABLE SPARSE RRT (SST), an algorithm that (a) provably provides asymptotic (near-)optimality for kinodynamic planning without access to a steering function, (b) maintains only a sparse set of samples, (c) converges fast to high-quality paths and (d) achieves competitive running time to RRT, which provides only probabilistic completeness. SST addresses the limitation of RRT \(^*\), which requires a steering function for asymptotic optimality . This issue has motivated recent variations of RRT \(^*\), which either work for a limiting set of systems or exhibit increased computational cost. This paper provides formal arguments for the properties of the proposed algorithm. To the best of the authors’ knowledge, this is the first sparse data structure that provides such desirable guarantees for a wide set of systems under a reasonable set of assumptions. Simulations for a variety of benchmarks, including physically simulated ones, confirm the argued properties of the approach.

Yanbo Li is associated with Cerner Corporation.

Zakary Littlefield is supported by a NASA Space Technology Research Fellowship.

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Authors and Affiliations

  1. Rutgers University, New Jersey, NJ, USA

    Yanbo Li, Zakary Littlefield & Kostas E. Bekris

Authors
  1. Yanbo Li
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  2. Zakary Littlefield
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  3. Kostas E. Bekris
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Corresponding author

Correspondence to Kostas E. Bekris .

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Editors and Affiliations

  1. Department of Computer Engineering, Bogazici University, Istanbul, Turkey

    H. Levent Akin

  2. Department of Computer Science and Engineering, Texas A&M University, College Station, Texas, USA

    Nancy M. Amato

  3. Department of Computer Science and Engineering, University of Minnesota, Minneapolis, Minnesota, USA

    Volkan Isler

  4. Department of Information and Computing Sciences, Utrecht University, Utrecht, Utrecht, The Netherlands

    A. Frank van der Stappen

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Li, Y., Littlefield, Z., Bekris, K.E. (2015). Sparse Methods for Efficient Asymptotically Optimal Kinodynamic Planning. In: Akin, H., Amato, N., Isler, V., van der Stappen, A. (eds) Algorithmic Foundations of Robotics XI. Springer Tracts in Advanced Robotics, vol 107. Springer, Cham. https://doi.org/10.1007/978-3-319-16595-0_16

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  • DOI: https://doi.org/10.1007/978-3-319-16595-0_16

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  • Print ISBN: 978-3-319-16594-3

  • Online ISBN: 978-3-319-16595-0

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