# Large-Area Coverage Path Planning Method Based on Vehicle-UAV Collaboration
###### Abstract  
With the widespread application of unmanned aerial vehicles (UAV) in surveying, disaster search and rescue, agricultural spraying, war reconnaissance, and other fields, coverage path planning is one of the most important problems to be explored. In this paper, a large-area coverage path planning (CCP) method based on vehicle-UAV collaboration is proposed. The core idea of the proposed method is adopting a divide-and conquer-strategy to divide a large area into small areas, and then completing efficient coverage scanning tasks through the collaborative cooperation of vehicles and UAVs. The supply points are generated and adjusted based on the construction of regular hexagons and a Voronoi diagram, and the segmentation and adjustment of sub-areas are also achieved during this procedure. The vehicle paths are constructed based on the classical ant colony optimization algorithm, providing an efficient way to traverse all supply points within the coverage area. The classic zigzag CCP method is adopted to fill the contours of each sub-area, and the UAV paths collaborate with vehicle supply points using few switching points. The simulation experiments verify the effectiveness and feasibility of the proposed vehicle-UAV collaboration CCP method, and two comparative experiments demonstrate that the proposed method excels at large-scale CCP scenarios, and achieves a significant improvement in coverage efficiency. insufficient endurance and load limitation of UAVs, the maximum scanning coverage area of a single drone flight is significantly limited [13]. Therefore, with regard to coverage scanning tasks for large-area regions, a certain position usually needs to be selected as a supply point for battery replacement or fertilizer or pesticide resupply of the UAV. The conventional coverage path planning (CCP) method for large areas is usually processed by multiple UAV flights. Firstly, the coverage scanning paths of UAVs need to be generated due to the boundary contours of a large area. Then, the scanning paths need to be split based on the endurance and load capacity of the UAV. The segmentation points in these paths are used as the switching points for the multiple flights, and special consideration needs to be given to the remaining endurance of the UAV to support flying back to the supply point. Finally, the above procedures are repeated until the coverage scanning task of the whole area is completed, as shown in Figure 1. The switching point is marked as \(\mathbf{M}\); the supply point is marked as \(\mathbf{P}\). It can be easily seen that a conventional UAV coverage scanning path based on multiple flights is composed of switching points, and a large number of UAV flight paths between supply point and switching points waste the valuable energy of UAV, reduce the coverage scanning efficiency of UAV, and are not conducive to the development of green UAVs. Obviously, the coverage scanning efficiency problem of UAVs becomes more severe as the working area becomes larger. In order to solve the above problems, this paper proposes a large-area coverage path planning method based on vehicle-UAV collaboration. The main idea of the method is to subdivide the large scanning coverage area into relatively uniform sub-areas and set a supply point within each sub-area. The divide-and-conquer algorithm and the shortest-path algorithm are adopted in the proposed method. The vehicle serving as the mobile supply point collaborates with the UAV to complete the coverage scanning task of sub-areas. The vehicle-mounted UAV automatically takes off from the vehicle to perform scanning tasks, then returns to the vehicle platform to complete energy supply, and repeats the above process to complete the coverage scanning work in each sub-area. The proposed method leverages the respective advantages of UAVs and vehicles, and the vehicle-UAV collaboration can significantly improve the efficiency of large-area coverage scanning scenarios, and provide us the benefit gains of efficiency and economy.