Optimization of Flying Ad Hoc Network Topology and Collaborative Path Planning for Multiple UAVs

--Multiple unmanned aerial vehicles (UA Vs) 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 UA V 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 (F ANET). The data throughput of the network is therefore maximized by optimizing the network topology and the UA V 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 C-TOP maximizes the data throughput of the network while simultaneously constraining the neighbors and transmit powers of the UA Vs, 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 UA V trajectories and significantly improve the data throughput of the F ANET over the adaptive local minimum spanning tree (A-LMST) and cyclic pruning-assisted power optimization (CPAPO) methods. ONITORING tasks are generally demanding in forest, desert, alpine tundra and other wide-area environments, where infrastructure and human resources are scarce. However, relying solely on manpower to complete these tasks can be challenging and time consuming. Unmanned aerial vehicles (UA Vs) are therefore introduced as a substitute for humans, and multiple UA Vs compose a flying ad hoc network (FANET) to cover a wide area. FANET has attracted significant interest and found many applications in electric power inspection, security, urban mapping, and so on.