Laser-Powered UAV Trajectory and Charging Optimization for Sustainable Data-Gathering in the Internet of Things

This work examines the trajectory design and energy charging strategy of a data-gathering unmanned aerial vehicle (UAV). The UAV utilizes laser charging from high-altitude platforms (HAPs) to replenish its battery, enabling sustained travel across multiple data-gathering points. The trajectory is determined by a sequence of hovering positions at which the UAV stays to perform both data collection and energy charging. The UAV's hovering positions affect both the sensors’ transmission rates and the laser-charging efficiency. To minimize the total task completion time, it is necessary to choose hovering positions that consider both data upload and energy charging times. In this work, we first propose the Minimum Completion Time Trajectory and Charging Optimization (MinTime-TCO) algorithm, where the hovering positions and charging energies are optimized in turn using a block coordinate descent approach. Given the UAV's hovering positions, we propose the Minimum Charge Rate Search (MCRS) algorithm to optimize the charging energies at these positions. We show that MCRS is optimal in terms of minimizing the total task completion time. Then, given the charging energies, we propose the Hovering Position Optimization (HPO) algorithm, employing successive convex approximation to address the non-convexity of the optimization problem. We also propose a low-complexity alternative based on dynamic programming to further reduce computational complexity. Simulation results demonstrate the effectiveness of the proposed algorithms against several baseline strategies.