Dynamics of Laser-Charged UAVs: A Battery Perspective

TL;DR

This paper explores laser-based recharging of UAVs, models their battery dynamics for precise energy management, and optimizes path planning to extend mission duration, demonstrating the advantages of a battery-focused approach over traditional energy estimates.

Contribution

It introduces a battery dynamics model for UAVs, proposes an optimal path planning method incorporating laser recharging, and offers a correction technique for conservative energy estimates.

Findings

Laser recharging can extend UAV mission time effectively.

Battery-based models improve energy estimation accuracy.

Optimized path planning outperforms benchmark trajectories.

Abstract

In this paper, we aim to sustain unmanned aerial vehicle (UAV) based missions for longer periods of times through different techniques. First, we consider on-the-mission UAV recharging by a low-power laser source (below 1 kilowatt). In order to achieve the maximal energy gain from the low-power laser source, we propose an operational compromise, which consists of the UAV resting over buildings with cleared line-of-sight to the laser source. Second, to provide a precise energy consumption/harvesting estimation at the UAV, we investigate the latter's dynamics in a mission environment. Indeed, we study the UAV's battery dynamics by leveraging the electrical models for motors and battery. Subsequently, using these models, the path planning problem in a particular Internet-of-Things based use-case is revisited from the battery perspective. The objective is to extend the UAV's operation time using both laser-charging and accurate battery level estimation. Through a graph theory approach, the problem is solved optimally, and compared to benchmark trajectory approaches. Numerical results demonstrate the efficiency of this novel battery perspective for all path planning approaches. In contrast, we found that the energy perspective is very conservative and does not exploit optimally the available energy resources. Nevertheless, we propose a simple adjustment method to correct the energy perspective, by carefully evaluating the energy as a function of the UAV motion regimes. Finally, the impact of several parameters, such as turbulence and distance to charging source, is studied.