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Motion Planning by Sampling in Subspaces of Progressively Increasing Dimension

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Abstract

This paper introduces an enhancement to traditional sampling-based planners, resulting in efficiency increases for high-dimensional holonomic systems such as hyper-redundant manipulators, snake-like robots, and humanoids. Despite the performance advantages of modern sampling-based motion planners, solving high dimensional planning problems in near real-time remains a considerable challenge. The proposed enhancement to popular sampling-based planning algorithms is aimed at circumventing the exponential dependence on dimensionality, by progressively exploring lower dimensional volumes of the configuration space. Extensive experiments comparing the enhanced and traditional version of RRT, RRT-Connect, and Bidirectional T-RRT on both a planar hyper-redundant manipulator and the Baxter humanoid robot show significant acceleration, up to two orders of magnitude, on computing a solution. We also explore important implementation issues in the sampling process and discuss the limitations of this method.

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Acknowledgments

The authors would like to thank the generous support of the Google Faculty Research Award program and the National Science Foundation grants (NSF 0953503, 1513203, 1526862, 1637876, 1659514, 1849291).

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Authors and Affiliations

  1. Computer Science and Engineering Department, University of South Carolina, Columbia, SC, USA

    Marios Xanthidis, Ioannis Rekleitis & Jason M. O’Kane

  2. Robotics and Control Engineering Department, United States Naval Academy, Annapolis, MD, USA

    Joel M. Esposito

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  1. Marios Xanthidis
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Correspondence to Marios Xanthidis.

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Xanthidis, M., Esposito, J.M., Rekleitis, I. et al. Motion Planning by Sampling in Subspaces of Progressively Increasing Dimension. J Intell Robot Syst 100, 777–789 (2020). https://doi.org/10.1007/s10846-020-01217-w

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