# Mission Planning of UAVs and UGV for Building Inspection in Rural Area
###### Abstract  
Unmanned aerial vehicles (UAVs) have become increasingly popular in the civil field, and building inspection is one of the most promising applications. In a rural area, the UAVs are assigned to inspect the surface of buildings, and an unmanned ground vehicle (UGV) is introduced to carry the UAVs to reach the rural area and also serve as a charging station. In this paper, the mission planning problem for UAVs and UGV systems is focused on, and the goal is to realize an efficient inspection of buildings in a specific rural area. Firstly, the mission planning problem (MPP) involving UGVs and UAVs is described, and an optimization model is established with the objective of minimizing the total UAV operation time, fully considering the impact of UAV operation time and its cruising capability. Subsequently, the locations of parking points are determined based on the information about task points. Finally, a hybrid ant colony optimization-genetic algorithm (ACO-GA) is designed to solve the problem. The update mechanism of ACO is incorporated into the selection operation of GA. At the same time, the GA is improved and the defects that make GA easy to fall into local optimal and ACO have insufficient searching ability are solved. Simulation results demonstrate that the ACO-GA algorithm can obtain reasonable solutions for MPP, and the search capability of the algorithm is enhanced, presenting significant advantages over the original GA and ACO. building inspection; mission planning; UAVs and UGV; ACO-GA +
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damage information [7]. [PERSON] et al. employed a field research experimental approach, emphasizing the feasibility and effectiveness of utilizing UAV technology for inspections [8]. Additionally, mission planning comprises task allocation and path planning. Typically, inspection mission planning employs a collaborative approach involving UGV and multiple UAVs. While UGV boasts high payload capacity and endurance, it faces challenges in rough terrain. Similarly, UAVs are constrained by factors such as limited payload capacity and flight duration, and a single UAV may even have defects such as a high risk of failure and long task time. In the face of a wide range of inspections and complex tasks, reliability is reduced, and it is often unable to ensure the smooth completion of the task. Hence, the collaborative technique of UGVs and UAVs offers distinct advantages. [PERSON] et al. successfully planned rational paths for medical or emergency supply delivery using UAV-UGV cooperation in disaster scenarios [9], while [PERSON] et al. effectively employed UAV-UGV synergy for continuous urban surveillance [10]. The MPP of UGV-UAVs is a challenging and pivotal problem. MPP is a general term for task assignment issues and path planning problems in UAVs [11]. By assigning one or more ordered tasks to a group of UAVs, redundancy and conflicts between UAVs are avoided, optimizing overall task efficiency. Typically, the optimization objective aims to minimize the flight cost of multiple UAVs, such as by minimizing flight path length or overall cost. To obtain rational task allocation solutions, it is imperative to establish mathematical models for collaborative UAV planning and employ algorithms for solving. Common algorithms of MPP include market-based algorithms [12], clustering algorithms [13], and metaheuristic algorithms [14]. Currently, research on the algorithms of MPP primarily focuses on metaheuristic algorithms due to their ability to strike a balance between solution quality and computational time, producing satisfactory solutions within acceptable time frames. These algorithms have low computational complexity, fast execution, and robustness, making them more adaptable to complex task and resource variations compared to market-based and clustering algorithms. Metaheuristic algorithms draw inspiration from natural phenomena or biological behaviors [15].