  Despite the increasing popularity of commercial usage of UAVs or
drone-delivered services, their dependence on the limited-capacity on-board
batteries hinders their flight-time and mission continuity. As such, developing
in-situ power transfer solutions for topping-up UAV batteries have the
potential to extend their mission duration. In this paper, we study a scenario
where UAVs are deployed as base stations (UAV-BS) providing wireless Hotspot
services to the ground nodes, while harvesting wireless energy from flying
energy sources. These energy sources are specialized UAVs (Charger or
transmitter UAVs, tUAVs), equipped with wireless power transmitting devices
such as RF antennae. tUAVs have the flexibility to adjust their flight path to
maximize energy transfer. With the increasing number of UAV-BSs and
environmental complexity, it is necessary to develop an intelligent trajectory
selection procedure for tUAVs so as to optimize the energy transfer gain. In
this paper, we model the trajectory optimization of tUAVs as a Markov Decision
Process (MDP) problem and solve it using Q-Learning algorithm. Simulation
results confirm that the Q-Learning based optimized trajectory of the tUAVs
outperforms two benchmark strategies, namely random path planning and static
hovering of the tUAVs.
