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A Learning-based Multi-RRT Approach for Robot Path Planning in Narrow Passages

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Abstract

As an important class of sampling-based path planning methods, the Rapidly-exploring Random Trees (RRT) algorithm has been widely studied and applied in the literature. In RRT, how to select a tree to extend or connect is a critical factor, which will greatly influence the efficiency of path planning. In this paper, a novel learning-based multi-RRTs (LM-RRT) approach is proposed for robot path planning in narrow passages. The LM-RRT approach models the tree selection process as a multi-armed bandit problem and uses a reinforcement learning algorithm that learns action values and selects actions with an improved ε-greedy strategy (ε t -greedy). Compared with previous RRT algorithms, LM-RRT can not only enhance the local space exploration ability of each tree, but also guarantee the efficiency of global path planning. The probabilistic completeness and combinatory optimality of LM-RRT are proved based on the geometric characteristics of the configuration space. Simulation and experimental results show the effectiveness of the proposed LM-RRT approach in single-query path planning problems with narrow passages.

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Acknowledgments

This work is sponsored by he NSFC Innovation and Development Joint Foundation of Chinese Automobile Industry under Grant U1564214. The authors would like to thank Dr. Chunming Liu for helpful discussion and conversation. In addition, the authors would like to thank the anonymous reviewers for their valuable comments.

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Authors and Affiliations

  1. College of Intelligence Science, National University of Defense Technology, Changsha, 410073, People’s Republic of China

    Wei Wang, Lei Zuo & Xin Xu

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  1. Wei Wang
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  2. Lei Zuo
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  3. Xin Xu
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Corresponding author

Correspondence to Xin Xu.

Additional information

This work is supported by the NSFC Innovation and Development Joint Foundation of Chinese Automobile Industry under Grant U1564214 and the NSFC International Cooperation Project under grant 61611540348.

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Wang, W., Zuo, L. & Xu, X. A Learning-based Multi-RRT Approach for Robot Path Planning in Narrow Passages. J Intell Robot Syst 90, 81–100 (2018). https://doi.org/10.1007/s10846-017-0641-3

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  • DOI: https://doi.org/10.1007/s10846-017-0641-3

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