Drone Coverage Path Planning for Visual Inspection of Building Facades

To efficiently plan the coverage path of UAVs during the visual inspection of building facades, the task is divided into two parts: viewpoint planning and path planning. First, a K-medoid clustering method is proposed to generate the initial set of candidate viewpoints based on the point cloud model of the building facade. A greedy optimization algorithm is then employed to select the optimal subset of viewpoints, addressing the viewpoint planning problem. Second, an incomplete graph is constructed, and the objective function is defined for path planning, which is solved using the Gaussian Zenith-Ant Colony Optimization (GZ-ACO) algorithm. The path length and turning angles are considered optimization objectives. Simulation results demonstrate that the proposed viewpoint planning method reduces the number of viewpoints by 66.67% and 98.67%, respectively, compared to the iterative random sampling method and the displacement method. Furthermore, the GZ-ACO algorithm outperforms the traditional ant colony algorithm, achieving a 19.97% reduction in the objective function value and a 21.76% decrease in the average steering angle. The validity and feasibility of the proposed method are thus verified.