An Investigation into Path Planning of Underwater Vehicle Based on Improved Particle Swarm Optimization Algorithm

Abstract

Addressing the challenges that unmanned underwater vehicles (UUVs) face in path planning, including complex constraints, unstable optimization algorithm performance, and unsmooth paths, this paper proposes a path planning method using an improved particle swarm optimization (PSO) algorithm. Firstly, the function simulation method is employed to construct the underwater terrain and obstacle environment. Secondly, in terms of designing optimization objectives, the paper adds to the conventional minimization of path length, optimizing changes in the unmanned underwater vehicle's attitude angle and the uniform distribution of steering node positions, making it more adaptable to actual conditions, all with a focus on reducing energy consumption. Following this, the article analyzes the effect of inertia weight in the PSO algorithm on algorithm performance, introducing improvement measures to enhance the algorithm's optimization performance. Lastly, the calculated initial path is smoothed via B-spline curves to derive the final trajectory for robot motion planning. The simulation results demonstrate that compared to traditional ant colony optimization and standard PSO algorithms, the devised path optimization method reduces overall energy consumption by 56.6% and 19.3%, respectively, presenting superior problem-solving ability and convergence performance.

Keywords

• unmanned underwater vehicle
• path planning
• particle swarm optimization
• inertia weight
• optimization objectives
• path smoothing