Abstract
An improved multi-objective particle swarm optimization algorithm is proposed to address the problem that traditional particle swarm algorithms are prone to fall into local optimums in complex environment for the path planning of autonomous underwater vehicles (AUVs). Firstly, the Sine double-sequence perturbation interpolation method is proposed to generate the initial particle swarm to reduce the randomness of initialization. Secondly, the algorithm introduces techniques adaptive differential evolution, iterative local search, and traction operation to enhance the global search capability of the algorithm and avoid falling into local optimality, and smooths the paths by cubic spline interpolation method. Finally, the objective function is designed for the ocean current environment, considering the influence of the route length, smoothness, deflection angle and ocean current, and the penalty function method is applied to solve the constrained optimization problem to find the optimal path. The simulation experimental results show that this algorithm can effectively plan a short-range and high-security path to meet the navigation requirements of AUV in ocean current environment, which verifies the effectiveness and practicality of the proposed algorithm.
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Funding
This work was supported in part by the National Natural Science Foundation of China under Grant 52271321, 61873161, and Natural Science Foundation of Shanghai under Grant 22ZR1426700.
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Niu, N., Sun, B., Su, Z. (2025). Improved Multi-objective Particle Swarm Algorithm for AUV Path Planning in Ocean Currents Environment. In: Lan, X., Mei, X., Jiang, C., Zhao, F., Tian, Z. (eds) Intelligent Robotics and Applications. ICIRA 2024. Lecture Notes in Computer Science(), vol 15206. Springer, Singapore. https://doi.org/10.1007/978-981-96-0792-1_2
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DOI: https://doi.org/10.1007/978-981-96-0792-1_2
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