An Adaptive Nearest Point Routing Method Based on Charging Nest Deployment Optimization for UAVs Power Tower Inspection

In this article, we propose a framework involving the inspection of electric power distribution towers by unmanned aerial vehicles (UAVs) and selecting charging nest sites to enhance the efficiency of acquiring defect data. Due to UAVs being constrained by battery capacity and required to perform multiple round-trip inspection missions, choosing appropriate locations for charging nests and arranging the sequence of UAV inspections for towers are essential. Thus, we introduce a weighted Monte Carlo sample based on Pareto optimality, which utilizes weight to adjust random sampling probability and multi-objective optimization to maximize the number of covered towers. In addition, an adaptive nearest point strategy is designed to ensure safe and efficient round-trip inspection missions while adhering to the maximum flight distance constraint. In experiments conducted with two distribution maps derived from distinct real-world scenarios, the coverage rates of the charging nests achieved 96.7% and 49.51%, respectively. Furthermore, the completion rates of multi-UAV inspections attained 86.85% and 91.74% within the designated time constraints. The results substantiate the efficacy of our methods for large-scale inspections of distribution network towers.