Global Path Planning for AUV Based on Charts and the Improved Particle Swarm Optimization Algorithm

When the AUV (autonomous underwater vehicle) plans a global path in sea area with complex conditions, there exist various problems, such as the lack of environmental information, the difficulty in environmental modeling, and the high complexity and the weak solution ability of conventional algorithms. To solve the problems, a global path planning method based on charts and the improved PSO (particle swarm optimization) algorithm is proposed. Firstly, a 3-dimensional static environment model based on the prior knowledge of electronic charts, and functions of the path range, the hazard degree and the smoothness are constructed. In order to improve the solution ability of the algorithm, the search factor and homology factor are introduced into the PSO algorithm to adjust the parameters adaptively, and the jump process of the fish swarm algorithm is also combined. Meanwhile, the security violation degree and the optimization rules are established to improve the security of the planned path. Finally, the simulation results show that the proposed method is more capable of planning short-range and high-security global paths compared with the traditional PSO algorithms and the ant colony algorithm (ACA), and can meet the requirements of AUV global path planning in complex sea conditions.