This paper proposes a new on-demand wireless energy transfer (WET) scheme of multiple unmanned aerial vehicles (UAVs). Unlike the existing studies that simply pursuing the total or the minimum harvested energy maximization at the Internet of Things (IoT) devices, where the IoT devices' own energy requirements are barely considered, we propose a new metric called the hungry-level of energy (HoE), which reflects the time-varying energy demand of each IoT device based on the energy gap between its required energy and the harvested energy from the UAVs. With the purpose to minimize the overall HoE of the IoT devices whose energy requirements are not satisfied, we optimally determine all the UAVs' trajectories and WET decisions over time, under the practical mobility and energy constraints of the UAVs. Although the proposed problem is of high complexity to solve, by excavating the UAVs' self-attentions for their collaborative WET, we propose the multiagent graph reinforcement learning (MAGRL) based approach. Through the offline training of the MAGRL model, where the global training at the central controller guides the local training at each UAV agent, each UAV then distributively determines its trajectory and WET based on the well-trained local neural networks. Simulation results show that the proposed MAGRL-based approach outperforms various benchmarks for meeting the IoT devices' energy requirements.

翻译：本文提出了一种新型的多无人机（UAV）按需无线能量传输（WET）方案。不同于现有研究单纯追求物联网（IoT）设备总收获能量最大化或最小收获能量最大化，且极少考虑物联网设备自身能量需求，我们提出了一种新的度量指标——能量饥饿程度（HoE），该指标基于每个物联网设备所需能量与从无人机获取能量之间的缺口，反映了其随时间变化的能量需求。为了最小化能量需求未获满足的物联网设备的总体HoE，我们在无人机实际移动和能量约束条件下，最优地确定了所有无人机的轨迹以及随时间的WET决策。尽管所提问题的求解复杂度极高，但通过挖掘无人机在协同WET中的自注意力机制，我们提出了基于多智能体图强化学习（MAGRL）的方法。通过MAGRL模型的离线训练（中央控制器的全局训练指导每个无人机智能体的局部训练），每个无人机基于训练好的局部神经网络分布地决定其轨迹和WET方案。仿真结果表明，所提出的基于MAGRL的方法在满足物联网设备能量需求方面优于多种基准方法。