Paper Info
Title
Aerial Refueling: Scheduling Wireless Energy Charging for UAV Enabled Data Collection
Abstract
The working scope and working time of small size unmanned aerial vehicles (UAVs) are limited because of the limited battery capacity. In this work, inspired by aerial refueling for aircrafts, we propose a wireless power transmission (WPT) scheme by categorizing the UAVs into charging UAV (CUAV) and mission UAV (MUAV) for charging UAVs without interrupting the mission. In the proposed aerial refueling scheme, mission UAVs (MUAV) can be recharged by charging UAVs on the fly and operate in a perpetual manner. The feasibility of aerially wireless charging for small UAVs is firstly discussed and evaluated. Then we consider a practical application scenario of multiple MUAVs for collecting data from several points of interest, where the MUAVs are recharged by CUAVs. Accordingly, the issue of scheduling the flying path and charging process of each CUAV to minimize the mission time arises. Deep reinforcement learning (DRL) based algorithms for scheduling both single and multiple CUAVs are proposed and deployed. Extensive simulation evaluations demonstrate that, by applying the proposed aerial refueling scheme, CUAVs can explore and optimize the scheduling strategies, thereby improving the system performance in terms of mission completion and charging efficiency.
Year
DOI
Venue
2022
10.1109/TGCN.2022.3164602
IEEE Transactions on Green Communications and Networking
Keywords
DocType
Volume
UAV energy management,wireless charging,deep reinforcement learning
Journal
6
Issue
ISSN
Citations 
3
2473-2400
0
PageRank 
References 
Authors
0.34
24
7
Name
Order
Citations
PageRank
k zhu100.34
Jing-yu Yang26061345.83
Ying Jun (Angela) Zhang31905135.63
j nie400.34
wyb lim500.34
Hongliang Zhang657747.71
Zehui Xiong758654.94