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.

本研究旨在基于五种生物群智能算法，解决洋流和海底地形障碍环境下自主水下航行器（AUV）的三维路径规划问题。首先建立三维海底环境模型和兰姆涡流环境模型。随后，考虑航行距离、海底地形约束和洋流约束，建立三维路径规划数学模型。此外，应用五种生物群智能优化算法来解决多目标非线性优化问题。最后，实验结果表明，粒子群优化（PSO）算法的最优路径性能优于其他算法。PSO算法的规划速度最快，WPA算法的鲁棒性最好。但规划时间却是最长的。PSO算法更适合海底地形障碍物和洋流影响下AUV的三维路径规划。