Path Planning for UAV Power Inspection Based on Improved Ant Colony Optimization Algorithm

With the swift advancement of smart grids and the ongoing enlargement of power network infrastructure, the spatial distribution of power line equipment shows significant non-uniform characteristics. As an important tool for power inspection, the energy saving and economy of unmanned aerial vehicle (UAV) inspection routes have become critical challenge that need to be solved urgently. This paper proposes an improved ant colony optimization (ACO) algorithm based on dynamic pheromone regulatory mechanism. By introducing an adaptive heuristic factor adjustment strategy, the algorithm can dynamically optimize the path selection probability according to the distribution characteristics of inspection points, thereby significantly improving the global search capability and convergence speed. The experimental findings demonstrate that, in comparison to the conventional approach, the enhanced algorithm successfully circumvents the issue of local optimality and produces a more refined and higher-quality inspection trajectory.This provides an efficient and reliable path planning solution for UAV power inspection tasks.