# Mission Planning of UAVs and UGV for Building Inspection in Rural Area
###### Abstract
Typical algorithms include Genetic Algorithm (GA) [16], Ant Colony Optimization (ACO) [17], Simulated Annealing (SA) [18], Particle Swarm Optimization (PSO) [19], and Tabu Search (TS) [20]. Local search algorithms such as TS and SA search for a single solution and prevent local optima using strategies like maintaining a Tabu list or probabilistic acceptance of inferior solutions. These algorithms are simple and easy to operate, but they are often inefficient for solving complex problems. GA, inspired by biological evolution processes of chromosome selection, crossover, and mutation, exhibits good optimization capabilities and population diversity. [PERSON] et al. improved GA convergence by incorporating a gravity search mechanism into the update process, solving collaborative reconnaissance task planning problems for multiple UAVs [21]. [PERSON] et al. addressed the task pre-allocation problem for multiple UAVs with different types of targets using a genetic algorithm with dual-chromosome encoding, achieving satisfactory results [22]. PSO, originating from the study of bird flock foraging, demonstrates good global search capabilities, fast convergence speed, and robustness [23]. [PERSON] et al. proposed an inertia weight adaptive strategy to overcome PSO's susceptibility to local optima [24], though efficiency concerns remained unaddressed. ACO simulates the optimization of foraging routes by ants, exhibiting good convergence speed and optimal pathfinding. [PERSON] introduced a dynamic evaporation strategy to adapt ACO, improving its convergence speed and alleviating the tendency to fall into local optima [25]. While much research has focused on improving traditional algorithms and enhancing optimization outcomes, inherent flaws in singular algorithms persist. Consequently, scholars have attempted to integrate two algorithms to complement each other's strengths and weaknesses. [PERSON] et al. proposed a genetic-ant colony algorithm for reconnaissance task planning [26]; meanwhile, [PERSON] et al. combined genetic concepts to propose an improved particle swarm optimization algorithm for solving multi-UAV task planning problems [27]. And [PERSON] et al. proposed an ant colony-single parent genetic algorithm to solve large-scale multiple traveling salesman problems, improving algorithm convergence speed, yet optimization outcomes warrant further improvement [28]. From the aforementioned studies, several issues in current research emerge: firstly, most studies of MPP focus solely on multiple UAVs without considering UGV and neglecting the issues caused by the low endurance of the UAVs. Secondly, in optimization processes, most research prioritizes the shortest paths or flight times, overlooking UAV operation processes or treating them homogeneously. However, the complexity of tasks at various points in real environments varies, leading to differences in operation times. Lastly, many studies reveal flaws in algorithms during the solving process, indicating room for improvement in overall optimization. Thus, addressing these issues, this paper calculates parking point positions before task planning, fully considering the positions of various task points and their impact on operation times. Subsequently, an ACO-GA is proposed, integrating the search process of ACO into GA's selection operation to enhance the initial solution quality. Furthermore, a series of improvements are made to the crossover and mutation operations to increase search capabilities and prevent falling into local optima. During optimization, considerations are given to the impact of UAV battery levels and operation time, aiming to solve the multi-UAV task problem effectively.