配备无线能量传输（WPT）功能的无人机（UAV）可通过按需供能延长无线可充电传感器网络（WRSN）的生命周期。本文提出一种由中心基站协调的、基于集成感知与通信（ISAC）的按需无人机充电框架。该框架采用优先级充电队列，综合节点剩余能量、业务负载、预估无人机飞行时间及飞行方向对齐度，刻画节点紧急程度与服务成本。这种双向耦合机制确保调度决策影响无人机轨迹规划，而ISAC提供的更新后运动状态估计则动态调整队列顺序。ISAC辅助估计无人机的距离、速度与位置，从而在运动不确定性下更新飞行时间预测。时间分配式部分充电策略依据节点关键性，将有限悬停时间分配至队列中的各节点。仿真结果表明，相较于典型基线方法，本方案在能量利用效率、飞行距离及充电延迟方面均有所提升。文中还讨论了部署相关考量因素，包括计算开销、可扩展性及参数选取，以协助实践者评估该框架在物联网（IoT）场景中的适用性。

Unmanned aerial vehicles (UAVs) equipped with wireless power transfer (WPT) extend the lifetime of wireless rechargeable sensor networks (WRSNs) by delivering energy on demand. This article presents an integrated sensing and communication (ISAC)-enabled on-demand UAV charging framework coordinated by a central base station. A prioritized charging queue captures node urgency and service cost through residual energy, traffic load, estimated UAV travel time, and flight-direction alignment. This bidirectional coupling ensures that scheduling decisions shape the UAV trajectory, while updated mobility estimates from ISAC dynamically reorder the queue. ISAC-assisted estimation of UAV distance, speed, and position updates travel-time predictions under mobility uncertainty. A time-allocated partial charging policy distributes limited hover time across queued nodes according to criticality. Simulations show gains in energy usage efficiency, travel distance, and charging delay compared with representative baselines. We discuss deployment considerations, including computational overhead, scalability, and parameter selection, to aid practitioners evaluating the framework for IoT scenarios.