# Large-Area Coverage Path Planning Method Based on Vehicle-UAV Collaboration
## 2 Related Works  
The collaboration model between vehicles and UAVs was first proposed by [PERSON] and [PERSON] [14] and has been widely used in reality, for purposes such as intelligence gathering, detection and surveillance of specific targets [15], parcel delivery [16] and circuit inspection [17]. Based on the roles of vehicles and UAVs in mission execution, vehicle-UAV cooperation problems can be categorized into four types:  
1. Vehicle-UAV independent mode: Drones and vehicles perform their tasks independently without affecting each other. [PERSON] et al. [18] proposed a simulation and optimization based decision-support system to facilitate disaster relief coordination between private and relief organizations, in which the trucks and UAVs are adopted to transport the relief goods from transfer points to demand points, but more attention is paid to the coordination between the private and relief organizations. [PERSON] et al. [19] presented a dynamic vehicle routine problem with heterogeneous fleets to improve same-day delivery performance and proposed a policy function approximation based on geographical districting to decide whether an order is delivered by a drone or by a vehicle in the same-day context. 2. UAV-first mode: UAVs take the primary role in the mission, with vehicles acting as support vehicles. [PERSON] et al. [20] studied an approximation algorithm for the traveling salesperson problem with neighborhoods to choose sampling locations from disks and generate a route to visit these locations while minimizing the sum of the travel and measurement times. [PERSON] et al. [21] introduced cooperative mission planning for a class of carrier-vehicle system and presented a heuristic solution for cooperation between a ship and a helicopter to solve two mission planning problems: one involving visiting a list of points sequentially under the hypothesis that the takeoff/landing sequence is not determined a priori, and a second in which the visiting sequence of points is optimized. [PERSON] et al. [22] addressed the task scheduling and path planning problem for a team of cooperating vehicles performing autonomous deliveries while ensuring the quadrotor delivered items at all requested locations along the shortest cooperative route. 3. Vehicle-first model: The vehicle has a higher priority in the task, and the cooperative optimization problem mainly describes the objective function of the vehicle, with the UAV acting as an auxiliary tool. [PERSON] et al. [23] designed a solution based on the genetic algorithm and the nearest-neighbor heuristic to generate a route with minimization of total route length while ensuring take-off and land-on locations. [PERSON] et al. [24] explored a UAV-based method to minimize the path availability's dependence on vehicular density and cooperation while enhancing the availability of a connectivity path, as well as reducing the end-to-end package delivery delay. 4. Vehicle-UAV Collaboration Model: In this model, the vehicle and the UAV must work together, and one may need to wait for the other. Despite the high cost of collaboration, [PERSON] et al. [25] and [PERSON] et al. [26] demonstrated that the delivery time in the vehicle-UAV collaboration mode is usually less than that of using the vehicle alone. [PERSON] et al. [27] showed that using UAV-equipped trucks for cargo delivery saves about 10% to 40% of the transportation cost. [PERSON] et al. [28] constructed a model and algorithmic framework for vehicle and UAV collaboration for aerial and ground surveillance. [PERSON] et al. [29] optimized the initial solution using a stochastic variable domain heuristic and verified that the method works better in solving large-scale cases. [PERSON] et al. [30] modeled the effect of UAV load on its energy consumption and used a simulated annealing algorithm to solve a two-tier path planning problem for vehicle-mounted UAVs for collaborative distribution. [PERSON] et al. [31] proposed a method for exploring planetary surfaces based on a hybrid UGV-UAV system. [PERSON] et al.