LAUSR

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.