A Study on UAV Path Planning for Navigation Mark Inspection Using Two Improved SOM Algorithms

With the widespread application of unmanned aerial vehicle technology in navigation mark inspection, path planning algorithm efficiency has become crucial to improve inspection effectiveness. The traditional self-organizing mapping (SOM) algorithm suffers from dual limitations in UAV inspection path optimization, including insufficient global exploration during early training stages and susceptibility to local optima entrapment in later stages, resulting in limited inspection efficiency and increased operational costs. For this reason, this study proposes two improved self-organizing mapping algorithms. First, the ORC_SOM algorithm incorporating a generalized competition mechanism and local infiltration strategy is developed. Second, the ORCTS_SOM hybrid optimization model is constructed by integrating the Tabu Search algorithm. Validation using two different scale navigation mark datasets shows that compared with traditional methods, the proposed improved methods achieve significantly enhanced path planning optimization. This study provides effective path planning methods for unmanned aerial vehicle navigation mark inspection, offering algorithmic support for intelligent maritime supervision system construction.