Potential Function-Based Collision Avoidance Control for Unmanned Surface Vehicles with Unknown Parameters

This paper investigates a collision avoidance control scheme for unmanned surface vehicles (USVs) subject to unknown model parameters and ocean disturbances. First, the problem of collision avoidance for multiple static obstacles is artfully described as a safety zone constraint problem, which needs to be addressed. A novel potential function is designed to ensure the USV arrives at the desired position and simultaneously satisfies the safety zone constraints during the movement process. Moreover, a robust adaptive collision avoidance control algorithm is presented, which is easy to apply online based on the potential function and robust adaptive control methods. Then, using Lyapunov analysis and Barbalat’s lemma, it is proven that the gradient of potential function and the ship’s velocity vector can converge asymptotically to zero without violating safety zone constraints, despite the presence of unknown model parameters and ocean disturbances. Finally, simulations for USVs verify the feasibility of the proposed scheme.