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Mechanical arm obstacle avoidance path planning based on improved artificial potential field method

Tianying Xu (School of Mechanical and Electrical Engineering, Central South University, Changsha, China and State Key Laboratory of High Performance Complex Manufacturing, Central South University, Changsha, China)
Haibo Zhou (School of Mechanical and Electrical Engineering, Central South University, Changsha, China and State Key Laboratory of High Performance Complex Manufacturing, Central South University, Changsha, China)
Shuaixia Tan (Zhuzhou Times New Material Technology Co., Ltd, Zhuzhou, China, and)
Zhiqiang Li (School of Mechanical and Electrical Engineering, Central South University, Changsha, China and State Key Laboratory of High Performance Complex Manufacturing, Central South University, Changsha, China)
Xia Ju (School of Mechanical and Electrical Engineering, Central South University, Changsha, China and State Key Laboratory of High Performance Complex Manufacturing, Central South University, Changsha, China)
Yichang Peng (School of Mechanical and Electrical Engineering, Central South University, Changsha, China and State Key Laboratory of High Performance Complex Manufacturing, Central South University, Changsha, China)

Industrial Robot

ISSN: 0143-991x

Article publication date: 14 October 2021

Issue publication date: 11 February 2022

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Abstract

Purpose

This paper aims to resolve issues of the traditional artificial potential field method, such as falling into local minima, low success rate and lack of ability to sense the obstacle shapes in the planning process.

Design/methodology/approach

In this paper, an improved artificial potential field method is proposed, where the object can leave the local minima point, where the algorithm falls into, while it avoids the obstacle, following a shorter feasible path along the repulsive equipotential surface, which is locally optimized. The whole obstacle avoidance process is based on the improved artificial potential field method, applied during the mechanical arm path planning action, along the motion from the starting point to the target point.

Findings

Simulation results show that the algorithm in this paper can effectively perceive the obstacle shape in all the selected cases and can effectively shorten the distance of the planned path by 13%–41% with significantly higher planning efficiency compared with the improved artificial potential field method based on rapidly-exploring random tree. The experimental results show that the improved artificial potential field method can effectively plan a smooth collision-free path for the object, based on an algorithm with good environmental adaptability.

Originality/value

An improved artificial potential field method is proposed for optimized obstacle avoidance path planning of a mechanical arm in three-dimensional space. This new approach aims to resolve issues of the traditional artificial potential field method, such as falling into local minima, low success rate and lack of ability to sense the obstacle shapes in the planning process.

Keywords

Acknowledgements

Declaration of interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

This work was supported in part by the National Natural Science Foundation of China [grant numbers 51975590].

Citation

Xu, T., Zhou, H., Tan, S., Li, Z., Ju, X. and Peng, Y. (2022), "Mechanical arm obstacle avoidance path planning based on improved artificial potential field method", Industrial Robot, Vol. 49 No. 2, pp. 271-279. https://doi.org/10.1108/IR-06-2021-0120

Publisher

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Emerald Publishing Limited

Copyright © 2021, Emerald Publishing Limited

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