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An improved Quick Informed-RRT* algorithm based on hybrid bidirectional search and adaptive adjustment strategies

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Abstract

Rapidly exploring random trees (RRTs) have been widely used for single-query motion planning problems due to their efficient searching capability in high-dimensional and complex spaces. Optimal RRTs (RRT*s) address the problem of finding optimal solutions, but the slow convergence rate and inefficient blind sampling contradict their single-query nature, which limits their applicability in certain applications, especially in narrow corridor and maze environments. To overcome these limitations, this paper proposes a hybrid bidirectional adaptive Informed-RRT* (HBAI-RRT*) that divides the planning process into three phases. Firstly, HBAI-RRT* implements a dual-tree search with greedy heuristics to find a feasible path faster than the unidirectional one. Afterward, the entire path is divided into two balanced segments through the dual-tree balancing operation. Finally, the dual-tree Quick Informed-RRT* asymptotic optimization is performed within the informed sets based on the two path segments, and the sampling frequency is adjusted based on the area ratio of the two informed subsets. Meanwhile, an adaptive adjustment strategy of segments intersection pair is employed to optimize the entire path. The proposed method significantly improves the convergence rate by efficiently reducing the sampling region. Comparisons of HBAI-RRT* with other algorithms verify that HBAI-RRT* exhibits better performance regarding initial path-finding speed and convergence rate in simulation environments.

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Acknowledgements

This work was supported by the Foundation of Key Laboratory of Green Construction Technology and Equipment of China Nuclear Power Corporation (CNNC-STGCL-KFKT-2022-001).

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Authors and Affiliations

  1. School of Naval Architecture and Ocean Engineering, Dalian University of Technology, Dalian, 116024, China

    Yan Lin & Linmao Zhang

  2. State Key Laboratory of Structural Analysis for Industrial Equipment, Dalian University of Technology, Dalian, 116024, China

    Yan Lin

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Conceptualization, methodology, coding, validation, writing and editing were performed by Linmao Zhang. Yan Lin contributed to the supervision and funding acquisition. All authors have read and agreed to the published version of the manuscript.

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Correspondence to Linmao Zhang.

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Appendix

Appendix

Table

Table 7 Summary of results for six algorithms in four simulation environments
Full size table

7 provides detailed computational data for the six algorithms. In this table, \(n_{5\% }\) represents the total number of nodes in the search tree when the path cost reaches \(c_{5\% }\). For HBAI-RRT* and Bidirectional Informed-RRT*, this value corresponds to the sum of nodes in the two trees.

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Lin, Y., Zhang, L. An improved Quick Informed-RRT* algorithm based on hybrid bidirectional search and adaptive adjustment strategies. Intel Serv Robotics 17, 847–870 (2024). https://doi.org/10.1007/s11370-024-00541-6

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