Research on an Autonomous Underwater Vehicle Intelligent Path Planning Algorithm Based on Improved Artificial Potential Field Method Optimization

Autonomous Underwater Vehicles (AUVs) encounter various challenges, such as navigation and obstacle avoidance, in complex underwater environments. This paper presents an intelligent path planning algorithm for AUVs that integrates the Artificial Potential Field (APF) method with the Particle Swarm Optimization (PSO) algorithm. Initially, two significant enhancements to the traditional APF method are introduced: a tangential force derived from existing virtual forces, and a correction factor for the repulsive force function that considers the distance to the target. Furthermore, to tackle the issue of multi-parameter tuning in the improved algorithm, the PSO algorithm is utilized to optimize these parameters. Simulation experiments demonstrate that the proposed AUV path planning algorithm, grounded in the enhanced APF method, effectively improves the algorithm's ability to escape force equilibrium, addresses unreachable target points, and optimizes parameter selection and path planning across various static obstacle environments.