Application Research of Enhanced Particle Swarm Optimization Algorithm for Underwater Path Planning

This study investigates the application of Enhanced Particle Swarm Optimization (EPSO) for underwater path planning. By analyzing the performance of traditional PSO, an improved method is proposed, focusing on dynamic adjustments of the fitness function and optimization of particle position update strategies. During the research, multiple test cases suitable for underwater navigation were designed to model the underwater environment, and MATLAB was used for simulation experiments to evaluate the algorithm’s path planning efficiency, optimal path quality, and computation time. Experimental results indicate that the EPSO algorithm significantly outperforms traditional PSO in real-time performance, path length, and obstacle avoidance, effectively addressing underwater robot navigation challenges. Specifically, the path planning time using this algorithm was reduced by approximately 30%, and the average path length was also shortened. These findings demonstrate the practicality of the EPSO algorithm in dynamic environments, offering new insights and methods for future underwater navigation technologies.