# Large-Area Coverage Path Planning Method Based on Vehicle-UAV Collaboration
## 3 Coverage Path Planning Method Based on Vehicle-UAV Collaboration
### Generation of Vehicle-UAV Cooperative Coverage Paths
The contour of the sub-area is adopted for generating the classic zigzag coverage path [38], which is illustrated in Figure 1. The zigzag coverage path ensures efficient coverage by the UAV, while maintaining path simplicity and operability. Through the above steps, mixed paths can be generated from the proposed vehicle-UAV collaboration method, and provide the vehicle with the shortest-length path to traverse all supply points and the UAV with a coverage path with simplicity and path operability. The mixed paths are illustrated in Figure 9b. In each sub-area, the vehicle and the UAV cooperate to complete the power supply and the transfer of collected data, and travel together to the next sub-area. It should be noted that the proposed vehicle-UAV collaboration method is suitable for a coverage area with relatively flat terrain features. By projecting this terrain onto a 2D plane, the obtained 2D polygons of the coverage area can be subjected to coverage path planning using the vehicle-UAV collaboration method proposed in this work. With regard to coverage areas containing spatial obstacles, such as signal towers in the wilderness, tall buildings outdoors, etc., to some extent, the following two approaches can be used to break through this constraint. On the one hand, the projection plane can be lifted to a certain height to ensure that these spatial obstacles disappear in the projection plane; on the other hand, an obstacle polygon can be placed inside the contour of the projection scanning area  
Figure 8: Schematic of the vehicle path planning method based on the ACO algorithm. in advance, which means the internal area of the obstacle polygon is located outside of the coverage area; thus the coverage path would not be generated in the obstacle polygon. Furthermore, the proposed vehicle-UAV collaboration method also provides the ability for collaboration between many vehicles and UAVs, which can improve the total efficiency of entire coverage areas. This is because the proposed CCP method provides a new mode to address the collaborative coverage problem between vehicles and UAVs, and generates the UAV paths in each sub-area and the vehicle paths for connecting all these sub-areas. In essence, these UAV paths and vehicle paths offer a collaborative from of sequential mode, which is suitable for single vehicles and single UAVs. From a programming perspective, these coverage tasks for sub-areas possess the characteristics of low coupling and strong independence. Thus, by virtue of the parallel model, the efficiency of overall coverage tasks can be improved. That is to say, the proposed method can be extended to many vehicles and UAVs. Since the entire vehicle path connects all these sub-areas and can be divided into several intervals based on the vertices (supply points) defined in the path, then these sub-areas related to the interval can be covered collaboratively by a combination of vehicles and UAVs. To some extent, there may be signal interference or signal delay issues in vehicle and drone control, but this is not the main focus of this research work. Figure 9: Simulation experiment results using different methods. (**a,c**) are the outputs of the proposed vehicle–UAV collaboration method; (**b,d**) are the generates of the conventional zigzag path method.