Unmanned aerial vehicles (UAVs) are recognized as promising technologies for area coverage due to the flexibility and adaptability. However, the ability of a single UAV is limited, and as for the large-scale three-dimensional (3D) scenario, UAV swarms can establish seamless wireless communication services. Hence, in this work, we consider a scenario of UAV swarm deployment and trajectory to satisfy 3D coverage considering the effects of obstacles. In detail, we propose a hierarchical swarm framework to efficiently serve the large-area users. Then, the problem is formulated to minimize the total trajectory loss of the UAV swarm. However, the problem is intractable due to the non-convex property, and we decompose it into smaller issues of users clustering, UAV swarm hovering points selection, and swarm trajectory determination. Moreover, we design a Q-learning based algorithm to accelerate the solution efficiency. Finally, we conduct extensive simulations to verify the proposed mechanisms, and the designed algorithm outperforms other referred methods.

翻译：无人机因其灵活性和适应性而被视为区域覆盖领域极具前景的技术。然而，单架无人机的能力有限，在大规模三维场景下，无人机集群可以建立无缝的无线通信服务。为此，本研究考虑了一种在障碍物影响下，无人机集群的部署与轨迹规划以满足三维覆盖需求的场景。具体而言，我们提出了一种分层集群框架，以有效服务大范围用户。随后，将问题建模为最小化无人机集群总轨迹损耗。然而，由于问题的非凸特性，其求解十分棘手，我们将其分解为用户聚类、无人机集群悬停点选择和集群轨迹确定等子问题。此外，我们设计了一种基于Q-learning的算法以提高求解效率。最后，我们通过大量仿真验证了所提机制，所设计的算法优于其他参考方法。