An Efficient UAV Coverage Path Planning Method for 3-D Structures

The use of Unmanned Aerial Vehicles (UAVs) to perceive surface images and other information of complex three-dimensional (3-D) structures is a key component in infrastructure inspection tasks, such as for wind turbines and bridges. As a result, UAV path planning for 3-D structure coverage has attracted significant attention from researchers. However, existing studies primarily focus on minimizing the viewpoint connection path length under the full coverage condition, neglecting UAV flight constraints, which leads to a significant discrepancy between the planned path and the actual UAV flight trajectory. This paper presents an efficient UAV path-planning method for 3-D structure coverage using an improved ant colony optimization (ACO) algorithm. A multi-constraint UAV flight-trajectory-length minimization problem is formulated to guarantee full coverage of 3-D surfaces under kinematic limits, turning-angle and attitude-rotation constraints, thereby enhancing path consistency and smoothness. A cost function based on Minimum Snap trajectory planning replaces simple waypoint-connection length to more accurately reflect actual flight effort. A beta distribution-driven ant state transition rule and an adaptive pheromone evaporation strategy mitigate the tendency of traditional ACO to stall in local optima, balancing exploration and exploitation for rapid and reliable convergence. Simulation results demonstrate that the method proposed in this paper outperforms existing typical 3-D structure coverage path-planning methods, validating its feasibility and effectiveness.