  Unmanned Aerial Vehicles (UAVs) have attracted considerable research interest
recently. Especially when it comes to the realm of Internet of Things, the UAVs
with Internet connectivity are one of the main demands. Furthermore, the energy
constraint i.e. battery limit is a bottle-neck of the UAVs that can limit their
applications. We try to address and solve the energy problem. Therefore, a path
planning method for a cellular-connected UAV is proposed that will enable the
UAV to plan its path in an area much larger than its battery range by getting
recharged in certain positions equipped with power stations (PSs). In addition
to the energy constraint, there are also no-fly zones; for example, due to Air
to Air (A2A) and Air to Ground (A2G) interference or for lack of necessary
connectivity that impose extra constraints in the trajectory optimization of
the UAV. No-fly zones determine the infeasible areas that should be avoided. We
have used a reinforcement learning (RL) hierarchically to extend typical
short-range path planners to consider battery recharge and solve the problem of
UAVs in long missions. The problem is simulated for the UAV that flies over a
large area, and Q-learning algorithm could enable the UAV to find the optimal
path and recharge policy.
