The unstructured, dynamic marine environmental information and the cooperative obstacle avoidance problem greatly challenge the online path planner for unmanned surface vehicles. Efficiency and optimization are crucial for online path planning schemes. Thus, we proposed an algorithmic combination of the optimal rapidly exploring random tree and artificial potential field methods. First, we built a repulsive potential field by considering the relative velocity and position of the unmanned surface vehicle to obstacles and the international regulations for preventing collisions at sea, wherein we designed a repulsive force calculation method using radar readings to avoid irregular obstacles. Then, we guided the sampling process of rapidly exploring random tree using the potential field to accelerate the convergence rate of rapidly exploring random tree to low-cost obstacle avoidance paths. Finally, we planned for multiple paths based on the leader–follower architecture with the guidance of a cooperative potential field. In the experiments, the proposed method consistently outperformed the benchmark methods. We also verified the effectiveness of the algorithmic modifications by conducting ablation experiments.

非结构化、动态的海洋环境信息和协同避障问题极大地挑战了无人水面航行器的在线路径规划器。效率和优化对于在线路径规划方案至关重要。因此，我们提出了一种最优快速探索随机树和人工势场方法的算法组合。首先，考虑无人水面航行器与障碍物的相对速度和位置以及国际海上防撞规则，构建斥力势场，设计了一种利用雷达读数的斥力计算方法，以避开不规则障碍物。然后，我们利用势场引导快速探索随机树的采样过程，以加快快速探索随机树到低成本避障路径的收敛速度。最后，我们在合作势场的指导下，基于leader-follower架构规划了多条路径。在实验中，所提出的方法始终优于基准方法。我们还通过消融实验验证了算法修改的有效性。