Collaborative Path Planning for Multiple Autonomous Underwater Vehicles Based on Improved Particle Swarm Optimization Algorithm

To solve the collaborative path planning problem of multiple autonomous underwater vehicles in complex environments, an improved particle swarm optimization algorithm is proposed to address the problems of traditional particle swarm optimization algorithms being prone to falling into local optima and having slow convergence speed, resulting in unreasonable search results. Firstly, a dynamic optimization formation transformation obstacle avoidance strategy was adopted for the multi AUV formation problem, and a formation obstacle avoidance strategy was designed that includes multiple modes of formation zero transformation, isomorphic transformation, and heterogeneous transformation. At the same time, this algorithm nonlinearly functionalizes the invariant inertia weights and acceleration constants in traditional particle swarm optimization algorithms, thereby optimizing the optimization ability and solving the problem of traditional particle swarm algorithms being prone to falling into local optima.