  The unmanned aerial vehicle (UAV) plays an vital role in various applications
such as delivery, military mission, disaster rescue, communication, etc., due
to its flexibility and versatility. This paper proposes a deep reinforcement
learning method to solve the UAV online routing problem with wireless power
transfer, which can charge the UAV remotely without wires, thus extending the
capability of the battery-limited UAV. Our study considers the power
consumption of the UAV and the wireless charging process. Unlike the previous
works, we solve the problem by a designed deep neural network. The model is
trained using a deep reinforcement learning method offline, and is used to
optimize the UAV routing problem online. On small and large scale instances,
the proposed model runs from four times to 500 times faster than Google
OR-tools, the state-of-the-art combinatorial optimization solver, with
identical solution quality. It also outperforms different types of heuristic
and local search methods in terms of both run-time and optimality. In addition,
once the model is trained, it can scale to new generated problem instances with
arbitrary topology that are not seen during training. The proposed method is
practically applicable when the problem scale is large and the response time is
crucial.
