The Application of an Improved Chaos-Particle Swarm Optimization Algorithm to the Real Submersible Path-Planning

Path planning is one of hot research topics of underwater vehicle, and the real submarine path planning needs a concise and short line. This paper presents a new improved chaos particle swarm algorithm (GCPSO), where the particle swarm algorithm (PSO) is changed by using nonlinear strategy to change the inertial weights and a variable learning factor. The numerical example shows that the improved GCPSO has better convergence and stronger optimization ability than standard particle swarm algorithm. On this basis, the algorithm is used in the simulation of underwater vehicle path planning. The path planning problem is transformed into the optimization problem of pursuing path points through the novel modeling with condition constraint to get a better path, and then an optimal line is obtained.