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ACB-RRT*: Adaptive Companion Points Bidirectional RRT* Algorithm

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Transactions on Intelligent Welding Manufacturing (RWIA 2022)

Part of the book series: Transactions on Intelligent Welding Manufacturing ((TRINWM))

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Abstract

To solve the problems of long search time, poor convergence and many redundant points in the RRT* algorithm, an adaptive companion point bidirectional RRT* algorithm (ACB-RRT*) is proposed. This algorithm adopts a hybrid strategy which including target-biased, dynamic step, and companion point generation. The target-biased strategy takes the target node as a sampling point according to the size of the random sampling probability to enhance the guidance. The dynamic step adopts different step sizes for expansion according to the random sampling probability to accelerate the convergence of the algorithm. The companion point generation determines multiple corresponding companion points based on the obtained expanded point to reduce the number of iterations. It also dynamically adjusts the angle of generating companion points based on the number of failed expansions. After obtaining a feasible path, trajectory optimization is performed on it. Greedy algorithm and cubic B-spline curve fitting are used to optimize nodes and smooth the trajectory, and finally an optimal collision-free path is obtained. Compared with RRT, RRT*, RRT-GoalBias, and B-RRT* algorithms, the results show that ACB-RRT* algorithm outperforms them in search time, path length, and number of iterations, indicating the superiority of this algorithm. Additionally, the algorithm has been successfully applied to welding scenarios.

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Authors and Affiliations

  1. Key Laboratory of Smart Manufacturing in Energy Chemical Process, Ministry of Education, East China University of Science and Technology, Shanghai, China

    Junnan Jiang, Heng Zhi, Xiaoxiang Tang, Can Cui & Xuewu Wang

Authors
  1. Junnan Jiang
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  2. Heng Zhi
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  3. Xiaoxiang Tang
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  4. Can Cui
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  5. Xuewu Wang
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Contributions

Made substantial contributions to conception and design of the study and performed algorithm analysis and interpretation: Jiang JN, Zhi H, Tang XX, Cui C, and Wang XW.

Corresponding author

Correspondence to Xuewu Wang .

Editor information

Editors and Affiliations

  1. Shanghai Jiao Tong University, Shanghai, China

    Shanben Chen

  2. Department of Electrical and Computer Engineering, University of Kentucky, Lexington, KY, USA

    Yuming Zhang

  3. Oak Ridge National Laboratory, Oak Ridge, TN, USA

    Zhili Feng

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Availability of Data and Materials

In this paper, algorithm and system are designed by the authors. The algorithm can be shared, but the developed system cannot be shared.

Financial Support and Sponsorship

The authors appreciate the support of National key research and development program (2022YFB4602104), National Natural Science Foundation of China (62076095, 61973120).

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All authors declared that there are no conflicts of interest.

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Not applicable.

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© The Author(s) 2023.

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© 2024 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

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Jiang, J., Zhi, H., Tang, X., Cui, C., Wang, X. (2024). ACB-RRT*: Adaptive Companion Points Bidirectional RRT* Algorithm. In: Chen, S., Zhang, Y., Feng, Z. (eds) Transactions on Intelligent Welding Manufacturing. RWIA 2022. Transactions on Intelligent Welding Manufacturing. Springer, Singapore. https://doi.org/10.1007/978-981-99-9629-2_8

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