WPT-enabled UAV Trajectory Design for Healthcare Delivery Using Reinforcement Learning

Over the last few years, the use of unmanned aerial vehicles (UAVs) has grown, with the goal of being widely deployed in sectors such as deliveries, rescue operations, mining fields, patrolling, and monitoring. However, the limitations of the onboard battery capacity and the flying range pose a problem to most applications while performing daily tasks such as parcel delivery or aerial communications in large areas. This paper proposes a reinforcement learning method to compute optimal trajectories for a UAV, considering both visiting delivery locations and recharging stations. The use of wireless power transfer (WPT) technology allows UAVs to wirelessly recharge their batteries on the fly and therefore to extend their flying range further. In this scenario, we consider several WPT-enabled charging stations placed around the serviced area. The proposed approach leverages a reinforcement learning strategy, and the performance results obtained show its effectiveness in finding an optimal trajectory by minimizing the UAV's travel and service time.