Efficient Charging Coverage Strategy of UAV Based on Improved Deep Reinforcement Learning

Unmanned aerial vehicles (UAVs) as flight service platform are promising to deliver continuous required coverage to target users through efficient charging strategies. The agility-and-resilience of UAVs allow them to be rapidly assigned in accordance with mobile user status and charging strategies that require energy-efficient scheduling without compromising the current quality of service. However, as the limited coverage radius and endurance, efficiently scheduling multi-UAVs to adaptively charge, considering service fairness, and minimizing the replacement consumption of UAVs is difficult. Therefore, a multi-objective charging coverage problem is designed to address these factors, which involves the constrained Pareto dominance principle. Subsequently, an assisted deep reinforcement learning model based on the survival incentive (ADRL-SI) is proposed to adaptively charge UAVs while taking into account user fairness and energy-efficient deployment. ADRL-SI adopts a multi-objective particle swarm optimisation approach to assist different agents in learning. The survival incentive mechanism is responsible for securing the UAVs with minimum residual energy. Finally, simulation results display that the proposed ADRL-SI is more advantage than current DRL-based methods in the matter of user service level, UAV energy efficiency, adaptive charging security, and system convergence.