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Research on optimal path sampling algorithm of manipulator based on potential function

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Abstract

Aiming at the problems of low success rate, long time and tortuous path of the traditional Rapidly-exploring Random Trees series of algorithms for path planning, this paper proposes the optimal path sampling algorithm based on the potential function (AP-RRT*), which solves the path planning problem of the manipulator in three-dimensional space. First, the potential function is defined and the concept of sampling termination distance is proposed. Second, a secondary sampling strategy is proposed in combination with the potential function to improve the planning speed and increase the coverage rate. Third, adaptive weights and adaptive step size are used to dynamically adjust the planning direction and distance, thereby improving the planning efficiency. Moreover, when performing node reconnection, dynamic retrieval circles are set to ensure path quality while reducing computational consumption. Finally, the improved algorithm, along with other algorithms, is simulated and experimentally verified in MATLAB and ROS. The results show that the AP-RRT* algorithm is superior in terms of path length, planning time, iterations, number of waypoints and success rate.

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Some or all data, models, or code that support the findings of this study are available from the corresponding author upon reasonable request.

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Acknowledgements

This work was supported in part by Jiangsu Province Graduate Practice Innovation Project (422003272), the Humanities and Social Sciences of Ministry of Education Planning Fund (21YJA630111), the Natural Science Foundation of Jiangsu Province (BK20201162), Changzhou Science and Technology Program Project (CM20223014) and Chang-Zhou Science and Technology Program Project (CJ20220207).

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Authors and Affiliations

  1. College of Mechanical and Electrical Engineering, Hohai University, Changzhou, Jiangsu, China

    Rui Shu, Minghai Yuan, Zhenyu Liang, Yingjie Sun & Fengque Pei

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  1. Rui Shu
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  2. Minghai Yuan
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  3. Zhenyu Liang
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  4. Yingjie Sun
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Shu, Liang, and Sun have written the code section of this article and the article, while Yuan and Pei have controlled the overall content

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Correspondence to Rui Shu.

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Shu, R., Yuan, M., Liang, Z. et al. Research on optimal path sampling algorithm of manipulator based on potential function. Int J Intell Robot Appl (2024). https://doi.org/10.1007/s41315-023-00316-9

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