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Comparison of biological swarm intelligence algorithms for AUVs for three-dimensional path planning in ocean currents’ conditions

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Abstract

This study aims to address the three-dimensional path planning problem of autonomous underwater vehicle (AUV) in the environment of ocean current and seabed terrain obstacles, based on five biological swarm intelligent algorithm. Firstly, a three-dimensional seabed environment model and a Lamb vortex current environment model are established. Subsequently, a three-dimensional path planning mathematical model is established by considering the navigation distance, seabed terrain constraints and ocean current constraints. Furthermore, five biological swarm intelligent optimization algorithms are applied to solve the multi-objective nonlinear optimization problem. Finally, the experimental results show that the optimal path performance of the particle swarm optimization (PSO) algorithm is better than other algorithms. The planning speed of PSO algorithm is the fastest, and the robustness of WPA algorithm is the best. However, the planning time is the longest. The PSO algorithm is more suitable for three-dimensional path planning of AUV under the influence of seabed terrain obstacles and ocean currents.

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Data available on request from the authors.

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Acknowledgements

The authors would like to thank the editors and the anonymous reviewers for their valuable comments and constructive suggestions. This work was supported by National Natural Science Foundation of China (62073054).

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Authors and Affiliations

  1. College of Marine Electrical Engineering, Dalian Maritime University, Dalian, 116026, China

    Xiaohong Li & Shuanghe Yu

  2. School of Information Science and Engineering, Dalian Polytechnic University, Dalian, 116034, China

    Xiaohong Li

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  1. Xiaohong Li
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  2. Shuanghe Yu
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Correspondence to Shuanghe Yu.

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Li, X., Yu, S. Comparison of biological swarm intelligence algorithms for AUVs for three-dimensional path planning in ocean currents’ conditions. J Mar Sci Technol 28, 832–843 (2023). https://doi.org/10.1007/s00773-023-00960-7

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  • DOI: https://doi.org/10.1007/s00773-023-00960-7

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