## 2 Related Works  
In recent years, the application of UAV technology in the field of MEC has become increasingly widespread. More research works focus on key issues such as UAV trajectory optimization, resource scheduling, and computation offloading:  
1. UAV trajectory optimization. The authors in [6] proposed a reinforcement learning (RL) solution acceleration framework based on an incomplete information communication model and quadcopter UAV energy consumption model, and they designed two responsive UAV trajectory planning algorithms to achieve energy consumption optimization. In [7], the authors proposed a three-layer integrated vehicle networking-based UAV-assisted MEC architecture, which optimizes the trajectory of the UAV, while satisfying the constraints of the evolution law of the vehicle energy consumption state, and maximizes the total system utility. In [8], [PERSON] et al. proposed a secure communication scheme for a dual UAV MEC system based on successive convex approximation and block coordinate descent, effectively planning the UAV flight trajectory to maximize the user's minimum secure computing capability. In [9], [PERSON] et al. proposed multiple multi-objective trajectory planning algorithms based on metaheuristic algorithms and Pareto optimality theory, which accurately plan the trajectory of UAVs to minimize the energy consumption of data transmission and UAV flight in IoT devices. In [10], the authors considered the deadline for completing tasks for ground users and proposed an offline optimal trajectory design algorithm and an online approximation algorithm suitable for multiple flight scenarios. By optimizing the number of flights and flight trajectories, the energy consumption of UAVs is minimized to the greatest extent possible. A low-complexity UAV trajectory prediction and vehicle selection method is proposed using federated learning, which exploits related information from past trajectories in [11]. In [12], the authors proposed an energy-aware grid-based solution for obstacle avoidance. 2. Resource scheduling and computation offloading. The authors aimed to minimize the total power and adopted centralized multi-agent algorithms and federated multi-agent RL algorithms to handle resource allocation and power control problems in the system in [13]. In [14], [PERSON] et al. proposed two offloading schemes based on game theory with the objectives of minimizing computation time and energy consumption, and they successfully determined the optimal offloading ratio. [PERSON] et al. [15] considered the energy limitation of UAVs and proposed a hybrid approach based on semi-Markov decision processes and deep reinforcement learning (DRL) with the goal of maximizing user task transfer throughput. [PERSON] et al. [16] aimed to reduce system energy consumption under latency constraints, and they designed a two-layer iterative algorithm based on a genetic algorithm framework to maximize system energy efficiency by optimizing service caching mechanisms, computation offloading strategies, and resource allocation methods. [PERSON] et al. [17] proposed a collaborative multi-agent DRL framework that uses Markov decision processes to optimize the computation offloading strategy of UAVs, and they achieved system latency minimization and fairness between ground terminals. [PERSON] et al. [18] applied the idea of cooperative transmission to improve channel conditions and maximized computing efficiency (CE) by using reconfigurable intelligent surfaces (RISs) in MEC. 3. In addition to the research on specific issues mentioned above, there are also many studies that jointly optimize UAV trajectories, resource scheduling, and computation offloading. [PERSON] et al. [19] proposed an offloading scheme based on near-end strategy optimization to minimize task processing latency, which synergistically optimizes the flight trajectory, task offloading rate, and communication resource allocation of UAVs. [PERSON]. [PERSON] et al.