Three-Dimensional Path Planning for Unmanned Underwater Vehicles Based on Age-improved Particle Swarm Optimization

In response to the problems of poor planning accuracy and susceptibility to local optima in traditional swarm intelligence algorithms for path planning of multiple underwater unmanned vehicles (UUVs) in complex environments, we propose an age-improved particle swarm algorithm (AIPSO), which is designed to address the path planning problem of multiple UUVs in complex obstacle environments. Firstly, we use sine chaotic mapping for the initialization process to enhance the dispersion of the initialized particles. Additionally, we introduce a Gauss-Cauchy mutation strategy to promote population diversity and expand the search area. Furthermore, an age factor is incorporated to mitigate the local optima problem by suppressing aged particles, thereby improving planning efficiency and reducing the risk of local optima. Finally, we apply the AIPSO algorithm to UUV path planning. Simulation results demonstrate that the AIPSO outperforms many common algorithms in terms of planning accuracy and planning time for path planning. It is capable of generating safe and smooth paths at a faster speed while satisfying constraints and minimizing costs. These results validate the effectiveness and feasibility of the proposed method in planning trajectories for multiple UUVs in a 3D environment.