Research on Collision Avoidance Methods for Unmanned Surface Vehicles Based on Boundary Potential Field

In recent years, unmanned surface vehicles (USVs) have gained increasing attention in industry due to their efficiency and versatility in marine operations. Artificial potential field (APF) methods, with their strong adaptability and simplicity of implementation, are widely used in USV path planning tasks. However, the naive APF method struggles in static complex environments, due to the local minima problem. Not to mention that actual navigations may involve other dynamic traffic participants. In this work, an improved APF algorithm integrating the boundary potential field method and the International Regulations for Preventing Collisions at Sea (COLREGs) is proposed. By incorporating the boundary potential field method, this novel approach effectively reduces the computational burden caused by clusters of land obstacles in complex environments, significantly improving computational efficiency. Furthermore, the APF method is refined to ensure the algorithm strictly adheres to COLREGs in head-on, overtaking, and crossing encounters, generating smooth and safe collision avoidance paths. The proposed method was tested in numerous complex scenarios derived from electronic navigational charts. The simulation results demonstrated the robustness and efficiency of the proposed algorithm for collision avoidance within complex maritime environments, providing reliable technical support for autonomous obstacle avoidance in dynamic ocean conditions.