Efficient Recharging in Urban Low-Altitude Wireless Energy Coordination Systems Based on Energy-Aware and Path-Planning Algorithms

Abstract

Urban low-altitude Unmanned Aerial Vehicles (UAVs) often require intermediate recharging during long-duration missions, and mobile Automated Guided Vehicles (AGVs) provide a flexible ground-side recharging solution in obstacle-constrained urban environments. Although AGV–UAV wireless energy coordination frameworks exist, appropriate matching cannot rely solely on nearest-distance or maximum-energy rules, as flight energy consumption, reachable safety margins, post-charge task feasibility, and low-altitude trajectory executability are tightly coupled. To address this issue, this paper proposes an energy-aware AGV matching method for efficient UAV recharging. For each candidate AGV, a feasible trajectory is generated under obstacle avoidance and altitude constraints, and the corresponding flight energy consumption, arrival energy, received charging energy, and post-charge available energy are quantitatively evaluated. A post-charge energy threshold is introduced to filter out AGVs that are reachable but unable to support subsequent mission execution after recharging. In addition, altitude regularization is employed to suppress excessive vertical maneuvers and improve the practicality of low-altitude trajectories. The proposed method is evaluated using three representative scenarios and three randomly generated scenarios against four baseline approaches. Results show that this method provides more reliable and mission-consistent decisions, avoids inefficient charging choices, and substantially reduces vertical motion.