Global Vision-Based Trajectory Planning and Tracking Control for Underactuated Unmanned Surface Vehicles
Maoyong Cao, Bangkun Liu, Fengying Ma, Peng Ji · 2024
This study proposes a global vision-based approach for trajectory planning and tracking control of Underactuated Unmanned Surface Vehicles (USVs). This scheme first utilizes global vision to obtain the position and surrounding environment information of USVs, and combines the A * algorithm with B-spline curves to generate trajectories that can be used for vehicle tracking and control. Next, design an extended state observer (ESO) to estimate the lumped disturbance and unmeasured velocity state. Based on ESO, design a model predictive controller and sliding mode controller that can achieve trajectory tracking control solely relying on position information and yaw angle information. Finally, simulation experiments were conducted on the Webots simulation platform, and the results showed the feasibility of the proposed solution.