Research on Cooperative Path Planning Method for Maritime Buoy Inspection Using USV and UAV

To improve the efficiency of maritime buoy inspection and reduce operation and maintenance costs, this paper proposes a cooperative path optimization method for Unmanned Surface Vessel (USV) and Unmanned Aerial Vehicle (UAV). The method first employs a recursive K-Means clustering algorithm to divide buoy points into several subregions, with each cluster center serving as a USV anchoring point. Subsequently, an Ant Colony Optimization (ACO) algorithm is applied to optimize both the USV cruising route among cluster centers and the UAV multi-mission inspection routes within each cluster, under UAV range constraints. Experimental validation was conducted on 418 buoys located in the coastal waters of Shanghai. The results show that, compared with conventional USV-only inspection, the proposed USV–UAV cooperative approach reduces the total cost by approximately 79.67%. The proposed method effectively achieves full-coverage inspection while significantly lowering costs, providing a feasible technical framework for intelligent operation and maintenance of maritime navigation aids.