Robust Collision Avoidance and Path-Following of USVs With Reduced Conservativeness: A Control Barrier Function-Based Approach

Abstract

The collision avoidance and path-following problem is fundamental for unmanned surface vehicles (USVs) to accomplish various tasks in different water environments. However, addressing this issue is challenging, as USVs are inevitably affected by environmental disturbances in practice. In this study, we address the robust collision avoidance path-following problem for USVs with unknown bounded environmental disturbances by using the control barrier function (CBF)-based approach. To reduce the conservativeness for the collision avoidance actions, the elliptical shape of the USV is considered when designing the control law. Furthermore, a high-order control barrier function (HOCBF)-based approach is proposed to achieve the robust collision avoidance with respect to both static and dynamic obstacles. Specifically, a nominal robust path-following controller is first designed utilizing the predefined-time observers without considering the collision avoidance requirement. Then, by considering the elliptical USV and the circular obstacles, collision avoidance input constraints are derived by the proper HOCBFs. Finally, a local quadratic programming (QP)-based controller is proposed to achieve robust collision avoidance and path-following of the USV. Simulation and experimental results are presented to demonstrate the effectiveness of the proposed control strategy.