To address the challenges of path planning for unmanned surface vessels (USVs) in complex environments and the relatively low efficiency of existing algorithms, this paper proposes a novel path planning method that integrates the artificial potential field (APF) approach with the rapidly-exploring random tree star (RRT*) algorithm—referred to as the bidirectional APF-RRT algorithm. The proposed method first introduces a goal-biased strategy, guiding newly generated nodes to expand preferentially toward the target direction. Additionally, a bidirectional search mechanism is adopted to drive two random trees toward each other, thereby accelerating the convergence speed of the algorithm. During node expansion, the attractive force from the artificial potential field guides the expansion toward the target, while the repulsive force enables effective obstacle avoidance. Finally, simulation experiments are conducted on the MATLAB platform to compare the proposed algorithm with traditional RRT* and APF-RRT* algorithms. Experimental results demonstrate that the bidirectional APF-RRT* algorithm outperforms the others in terms of the number of path nodes, path length, and planning efficiency across multiple typical scenarios, indicating superior planning performance and environmental adaptability.