Multi-Target Point Path Planning and Tracking of Unmanned Surface Vehicle Using SFLOS Algorithm and Incremental PID Control With Crane-Assisted

Abstract

To enhance the multi-target path planning and tracking performance of Unmanned Surface Vehicles (USVs), a combined control strategy integrating the Serret-Frenet Line-of-Sight (SFLOS) algorithm and an incremental PID controller is proposed to achieve precise path tracking in complex environments. Additionally, a novel finite-time observer is presented to compensate for the sideslip angle caused by environmental disturbances in real time. The heading controller of the USV is designed using incremental PID control, and an improved Particle Swarm Optimization (PSO) algorithm is proposed to optimize the PID parameters. The stability and effectiveness of the proposed method are validated through simulations. Furthermore, a USV equipped with a crane-assisted experimental platform is constructed to conduct verification experiments for course control, guidance law, and planned path tracking control. Both simulation and real-world tests demonstrate the effectiveness and feasibility of the proposed approach.