Multi-UAV Cooperative 3D Coverage Path Planning Based on Asynchronous Ant Colony Optimization

UAVs carrying visual sensors to capture the defects on the building surface has the advantages of high efficiency, low cost, flexibility and convenience. This kind of inspection task is called coverage path planning problem in 3D space. When the target to be detected is a large complex building, multi-UAV collaboration is usually required. So, how to obtain the optimal path of multi-UAV becomes a great challenge. In order to solve the problem, Asynchronous Ant Colony Optimization (AACO), which makes multiple ant colonies move forward asynchronously, is proposed here to conquer the difficulty. Firstly, the random sampling method is used to get the potential UAV waypoints in the 3D free environment, based on which a Primitive Coverage Graph (PCG) is constructed. Also, the visibility matrix and coverage rate are defined to quantify the coverage performance of UAV path primitives. Next, Asynchronous Ant Colony Optimization combined with reward strategy is proposed to solve the problem by selecting jump cities in turn. Finally, several simulations are provided to verify the feasibility and effectiveness of the algorithm.