Sun Bing, Lv Ziang
Logistics Engineering College, Shanghai Maritime University, Shanghai 201306, China.
Biomimetics (Basel). 2025 Aug 15;10(8):536. doi: 10.3390/biomimetics10080536.
Aiming at the cooperative path-planning problem of multiple autonomous underwater vehicles in underwater three-dimensional terrain and dynamic ocean current environments, a hybrid algorithm based on the Improved Multi-Objective Particle Swarm Optimization (IMOPSO) and Dynamic Window (DWA) is proposed. The traditional particle swarm optimization algorithm is prone to falling into local optimization in high-dimensional and complex marine environments. It is difficult to meet multiple constraint conditions, the particle distribution is uneven, and the adaptability to dynamic environments is poor. In response to these problems, a hybrid initialization method based on Chebyshev chaotic mapping, pre-iterative elimination, and boundary particle injection (CPB) is proposed, and the particle swarm optimization algorithm is improved by combining dynamic parameter adjustment and a hybrid perturbation mechanism. On this basis, the Dynamic Window Method (DWA) is introduced as the local path optimization module to achieve real-time avoidance of dynamic obstacles and rolling path correction, thereby constructing a globally and locally coupled hybrid path-planning framework. Finally, cubic spline interpolation is used to smooth the planned path. Considering factors such as path length, smoothness, deflection Angle, and ocean current kinetic energy loss, the dynamic penalty function is adopted to optimize the multi-AUV cooperative collision avoidance and terrain constraints. The simulation results show that the proposed algorithm can effectively plan the dynamic safe path planning of multiple AUVs. By comparing it with other algorithms, the efficiency and security of the proposed algorithm are verified, meeting the navigation requirements in the current environment. Experiments show that the IMOPSO-DWA hybrid algorithm reduces the path length by 15.5%, the threat penalty by 8.3%, and the total fitness by 3.2% compared with the traditional PSO algorithm.
针对水下三维地形和动态海流环境中多自主水下航行器的协同路径规划问题，提出了一种基于改进多目标粒子群优化算法（IMOPSO）和动态窗口算法（DWA）的混合算法。传统粒子群优化算法在高维复杂海洋环境中容易陷入局部最优，难以满足多约束条件，粒子分布不均匀，对动态环境的适应性较差。针对这些问题，提出了一种基于切比雪夫混沌映射、预迭代消除和边界粒子注入的混合初始化方法（CPB），并结合动态参数调整和混合扰动机制对粒子群优化算法进行改进。在此基础上，引入动态窗口算法（DWA）作为局部路径优化模块，实现对动态障碍物的实时避障和滚动路径修正，从而构建全局与局部耦合的混合路径规划框架。最后，采用三次样条插值对规划路径进行平滑处理。考虑路径长度、平滑度、偏转角和海流能量损失等因素，采用动态惩罚函数优化多自主水下航行器协同避碰和地形约束。仿真结果表明，所提算法能够有效规划多自主水下航行器的动态安全路径。通过与其他算法对比，验证了所提算法的效率和安全性，满足当前环境下的航行要求。实验表明，与传统粒子群优化算法相比，IMOPSO-DWA混合算法的路径长度减少了15.5%，威胁惩罚降低了8.3%，总适应度降低了3.2%。
