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Dynamic RRT: Fast Feasible Path Planning in Randomly Distributed Obstacle Environments

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Abstract

For path planning problems based on Rapidly exploring Random Trees (RRT), most new nodes merely explore the environment unless they are sampled directly from the subset that can optimize the path. This paper proposes the Dynamic RRT algorithm, which aims to plan a feasible path while balancing the convergence time and path length in an environment with randomly distributed obstacles. It estimates the length of a path from the start node to the goal node that is constrained to pass through an extended tree node, and this path length is heuristically taken as the major axis diameter of the informed subset. Then new node sampling is performed directly in this subset to optimize the estimated path. In addition, the idea of dynamic programming is employed to decompose the planning problem into subproblems by updating the node selected through Pareto dominance as the new start node to optimize the distance to the goal. Simulation results confirm the performance of the proposed algorithm in balancing the convergence time and path length and demonstrate that the convergence time is faster than that of RRT, while the path length is better than that of RRT*. Dynamic RRT also shows better performance than Lower Bound Tree-RRT(LBT-RRT), and Informed RRT* takes more time to compute a path of the same length.

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Funding

This work is supported by the Key project's funding of NSFC (No.61836010).

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Authors and Affiliations

  1. School of Electronic Science and Engineering, Xiamen University, Xiamen, 361005, China

    Penglei Zhao, Hongyin Luo, Zhixin Zhou, Yanping Qiao, Ying Li, Chenhui Zhao, Zenan Huang, Bijing Liu, Xiaojie Liu, Shan He & Donghui Guo

  2. Academy for Network & Communications of CETC, Shijiazhuang, 050299, China

    Yinghui Chang & Weikang Wu

Authors
  1. Penglei Zhao
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  2. Yinghui Chang
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  11. Xiaojie Liu
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  12. Shan He
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  13. Donghui Guo
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Contributions

All authors contributed to the study conception and design. Penglei Zhao wrote the manuscript and did the research. Technical support was provided by Yinghui Chang, Weikang Wu, Hongyin Luo, Zhixin Zhou. Valuable comments on manuscript revisions were put forward by Yanping Qiao, Ying Li, Chenhui Zhao, Zenan Huang, Bijing Liu, Xiaojie Liu, Shan He. Prof. Donghui Guo provided manuscript writing guidance. All authors read and approved the final manuscript.

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Correspondence to Donghui Guo.

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Zhao, P., Chang, Y., Wu, W. et al. Dynamic RRT: Fast Feasible Path Planning in Randomly Distributed Obstacle Environments. J Intell Robot Syst 107, 48 (2023). https://doi.org/10.1007/s10846-023-01823-4

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