Multiple unmanned aerial vehicles (UAVs) play a vital role in monitoring and data collection in wide area environments with harsh conditions. In most scenarios, issues such as real-time data retrieval and real-time UAV positioning are often disregarded, essentially neglecting the communication constraints. In this paper, we comprehensively address both the coverage of the target area and the data transmission capabilities of the flying ad hoc network (FANET). The data throughput of the network is therefore maximized by optimizing the network topology and the UAV trajectories. The resultant optimization problem is effectively solved by the proposed reinforcement learning-based trajectory planning (RL-TP) algorithm and the convex-based topology optimization (C-TOP) algorithm sequentially. The RL-TP optimizes the UAV paths while considering the constraints of FANET. The C-TOP maximizes the data throughput of the network while simultaneously constraining the neighbors and transmit powers of the UAVs, which is shown to be a convex problem that can be efficiently solved in polynomial time. Simulations and field experimental results show that the proposed optimization strategy can effectively plan the UAV trajectories and significantly improve the data throughput of the FANET over the adaptive local minimum spanning tree (A-LMST) and cyclic pruning-assisted power optimization (CPAPO) methods.

翻译：多无人机（UAVs）在条件恶劣的广域环境监测与数据收集中发挥着至关重要的作用。在多数场景中，实时数据检索与无人机实时定位等问题常被忽视，本质上忽略了通信约束。本文综合考虑了目标区域的覆盖范围与飞行自组织网络（FANET）的数据传输能力。通过优化网络拓扑与无人机轨迹，从而最大化网络的数据吞吐量。该优化问题通过所提出的基于强化学习的轨迹规划（RL-TP）算法与基于凸优化的拓扑优化（C-TOP）算法依次有效求解。RL-TP在考虑FANET约束的同时优化无人机路径。C-TOP在约束无人机邻居节点与发射功率的同时最大化网络数据吞吐量，该问题被证明是一个凸问题，可在多项式时间内高效求解。仿真与实地实验结果表明，所提出的优化策略能够有效规划无人机轨迹，并在数据吞吐量方面显著优于自适应局部最小生成树（A-LMST）与循环剪枝辅助功率优化（CPAPO）方法。