Path planning of underwater vehicle based on improved particle swarm algorithm

Particle swarm algorithm can overcome the shortcomings of insufficient optimization ability, large calculation amount and difficulty in planning the optimal path of traditional algorithms in the path planning of underwater vehicles with complex processing environment and many constraints. However,traditional particle swarm algorithms have drawbacks such as low accuracy and easy falling into local optima. In order to solve this problem, This paper sets an inertia weight that decreases with the number of iterations and an asynchronous shrinkage learning factor for the particle swarm algorithm under the condition of converting from a spatial coordinate system to a spherical coordinate system and then applies it to the path planning of underwater vehicle.Simulation experiments show that the improved particle swarm algorithm (LINW-SPSO) proposed in this paper is superior to the traditional particle swarm algorithm in terms of algorithm stability and path planning effect.