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AUV Global Security Path Planning Based on a Potential Field Bio-Inspired Neural Network in Underwater Environment

Xiang Cao1,2,*, Ling Chen1, Liqiang Guo3, Wei Han4

1 School of Physics and Electronic Electrical Engineering, Huaiyin Normal University, Huai’an, 223300, China
2 School of Automation, Southeast University, Nanjing, 210096, China
3 School of Computer Science and Technology, Huaiyin Normal University, Huai’an, 223300, China
4 State Grid Jiangsu Electric Power Co., Ltd., Huai’an Power Supply Branch, Huai’an, 223001, China

* Corresponding Author: Xiang Cao. Email:

Intelligent Automation & Soft Computing 2021, 27(2), 391-407.


As one of the classical problems in autonomous underwater vehicle (AUV) research, path planning has obtained a lot of research results. Many studies have focused on planning an optimal path for AUVs. These optimal paths are sometimes too close to obstacles. In the real environment, it is difficult for AUVs to avoid obstacles according to such an optimal path. To solve the safety problem of AUV path planning in a dynamic uncertain environment, an algorithm combining a bio-inspired neural network and potential field is proposed. Based on the environmental information, the bio-inspired neural network plans the optimal path for the AUV. The potential field function adjusts the path planned by the bio-inspired neural network so that the actual AUV can avoid obstacles. The proposed approach for AUV path planning with safety consideration is capable of planning a real-time “comfortable” trajectory by overcoming either “too close” or “too far” shortcomings. Simulation and experimental results show that the proposed algorithm considers both AUV security and path rationality. The planned path can meet the need for collision-free navigation of AUVs in a dynamic, uncertain environment.


Cite This Article

X. Cao, L. Chen, L. Guo and W. Han, "Auv global security path planning based on a potential field bio-inspired neural network in underwater environment," Intelligent Automation & Soft Computing, vol. 27, no.2, pp. 391–407, 2021.


This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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