Autonomous collision avoidance method for MASSs based on precise potential field modelling and COLREGs constraints in complex sailing environments

Autonomous collision avoidance (CA) for maritime autonomous surface ships (MASSs) in complex navigational environments requires accurate environment modelling and efficient CA algorithms. This study proposes a fast construction method for complex environmental potential field models applicable to vector charts. It accurately represents all complex static obstacles, including concave polygons, and dynamically adjusts the range of influence of a potential field according to its danger level. For dynamic ships, a dynamic obstacle modelling method, based on the quaternion ship domain, is designed to highlight the uncertain motion characteristics of ships. Based on the coupled modelling of dynamic and static obstacles, the virtual potential field of target ships, constrained by the International Regulations for Preventing Collisions at Sea (COLREGs), was constructed. By combining the first-order Nomoto model and optimal field prediction strategy, an optimal CA path, satisfying ship dynamics and COLREGs constraints, was realised, and the local optimal problem of the traditional artificial potential field method was solved. Simulation results show that the proposed method accomplishes fast and accurate modelling of complex environments and realises autonomous path planning and real-time multivessel CA tasks for MASSs in complex water considering the COLREGs constraints. Thus, the proposed method can be implemented in actual ships.