UAV Navigation Mark Inspection Path Planning Based on Improved GWO

Abstract

Navigation mark inspections are critical to ensuring maritime navigation safety, and the efficiency of unmanned aerial vehicle (UAV) inspection path planning directly affects inspection costs and operations. This problem is formulated as a Traveling Salesman Problem (TSP), and the traditional Grey Wolf Optimizer (GWO) has limitations in discrete optimization, including weak search capabilities, simple neighborhood structures, and poor local optimization. To address these issues, this paper proposes an improved Grey Wolf Optimizer (IGWO). First, this paper introduces three neighborhood search operators: reverse, insertion, and swap. Second, an adaptive step size mechanism based on Euclidean distance is designed. Third, the 3-opt local optimization algorithm is integrated. Finally, experiments are conducted using real navigation mark data from Pingtan and Tianjin, and IGWO is compared with traditional algorithms. Results show that IGWO effectively adapts GWO to discrete spaces and achieves optimal paths across datasets of varying scales. Its path length reduction rates improve by 0.51% to 58.01% over the other seven algorithms. These findings provide efficient UAV path planning solutions for navigation mark inspection and offer technical support for smart maritime supervision systems.