Underwater Manipulator Trajectory Planning Based on Improved Particle Swarm Optimization Algorithm

Abstract

This study presents an innovative motion planning approach for underwater robotic arms, grounded in the multistrategy improved particle swarm optimization (PSO) (strategy adaptive particle swarm optimization [SAPSO]) algorithm. The SAPSO algorithm amalgamates the sine–cosine algorithm with the sparrow search algorithm, thereby enhancing the convergence efficiency and the capability to escape local optima inherent in PSO. Through the implementation of a 3–5–3 polynomial trajectory planning method, the proposed approach ensures a seamless transition from the initial to the target position while maintaining the continuity and fluidity of movement. Both simulation and underwater experimental analyses have validated the precision and efficacy of the SAPSO algorithm in collision detection, joint parameter optimization, and target capture operations. The outcomes underscore that the SAPSO algorithm considerably amplifies the speed and stability of trajectory planning and exhibits innovation and efficiency in the domain of underwater robotic arm motion planning.