Mobile Edge Computing (MEC) reduces the computational burden on terminal devices by shortening the distance between these devices and computing nodes. Integrating Unmanned Aerial Vehicles (UAVs) with enhanced MEC networks can leverage the high mobility of UAVs to flexibly adjust network topology, further expanding the applicability of MEC. However, in highly dynamic and complex real-world environments, it is crucial to balance task offloading effectiveness with algorithm performance. This paper investigates a multi-UAV communication network equipped with edge computing nodes to assist terminal users in task computation. Our goal is to reduce the task processing delay for users through the joint optimization of discrete computation modes, continuous 3D trajectories, and resource assignment. To address the challenges posed by the mixed action space, we propose a Multi-UAV Edge Computing Resource Scheduling (MUECRS) algorithm, which comprises two key components: 1) trajectory optimization, and 2) computation mode and resource management. Experimental results demonstrate our method effectively designs the 3D flight trajectories of UAVs, enabling rapid terminal coverage. Furthermore, the proposed algorithm achieves efficient resource deployment and scheduling, outperforming comparative algorithms by at least 16.7%, demonstrating superior adaptability and robustness.

翻译：移动边缘计算（MEC）通过缩短终端设备与计算节点之间的距离，减轻了终端设备的计算负担。将无人机（UAV）与增强的MEC网络相结合，可以利用无人机的高机动性灵活调整网络拓扑，进一步拓展MEC的适用性。然而，在高度动态且复杂的现实环境中，平衡任务卸载效果与算法性能至关重要。本文研究了一种配备边缘计算节点的多无人机通信网络，以辅助终端用户进行任务计算。我们的目标是通过联合优化离散计算模式、连续三维轨迹和资源分配，降低用户的任务处理时延。为应对混合动作空间带来的挑战，我们提出了一种多无人机边缘计算资源调度（MUECRS）算法，该算法包含两个关键组成部分：1）轨迹优化，以及2）计算模式与资源管理。实验结果表明，我们的方法能有效设计无人机的三维飞行轨迹，实现快速的终端覆盖。此外，所提算法实现了高效的资源部署与调度，性能优于对比算法至少16.7%，展现了卓越的适应性与鲁棒性。