TY - GEN
T1 - WPT-enabled UAV Trajectory Design for Healthcare Delivery Using Reinforcement Learning
AU - Merabet, Adel
AU - Lakas, Abderrahmane
AU - Belkacem, Abdelkader Nasreddine
N1 - Funding Information:
This project was supported by the UAE University's National Space Science and Technology Center Project grant number G00003280
Funding Information:
This project was supported by the UAE University’s National Space Science and Technology Center Project grant number G00003280.
Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - Over the last few years, the use of unmanned aerial vehicles (UAVs) has grown, with the goal of being widely deployed in sectors such as deliveries, rescue operations, mining fields, patrolling, and monitoring. However, the limitations of the onboard battery capacity and the flying range pose a problem to most applications while performing daily tasks such as parcel delivery or aerial communications in large areas. This paper proposes a reinforcement learning method to compute optimal trajectories for a UAV, considering both visiting delivery locations and recharging stations. The use of wireless power transfer (WPT) technology allows UAV s to wirelessly recharge their batteries on the fly and therefore to extend their flying range further. In this scenario, we consider several WPT-enabled charging stations placed around the serviced area. The proposed approach leverages a reinforcement learning strategy, and the performance results obtained show its effectiveness in finding an optimal trajectory by minimizing the UAV's travel and service time.
AB - Over the last few years, the use of unmanned aerial vehicles (UAVs) has grown, with the goal of being widely deployed in sectors such as deliveries, rescue operations, mining fields, patrolling, and monitoring. However, the limitations of the onboard battery capacity and the flying range pose a problem to most applications while performing daily tasks such as parcel delivery or aerial communications in large areas. This paper proposes a reinforcement learning method to compute optimal trajectories for a UAV, considering both visiting delivery locations and recharging stations. The use of wireless power transfer (WPT) technology allows UAV s to wirelessly recharge their batteries on the fly and therefore to extend their flying range further. In this scenario, we consider several WPT-enabled charging stations placed around the serviced area. The proposed approach leverages a reinforcement learning strategy, and the performance results obtained show its effectiveness in finding an optimal trajectory by minimizing the UAV's travel and service time.
KW - delivery
KW - drone
KW - healthcare
KW - reinforcement learning
KW - simulation
KW - unmanned aerial vehicle
KW - wireless power transfer
UR - http://www.scopus.com/inward/record.url?scp=85135292797&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85135292797&partnerID=8YFLogxK
U2 - 10.1109/IWCMC55113.2022.9824768
DO - 10.1109/IWCMC55113.2022.9824768
M3 - Conference contribution
AN - SCOPUS:85135292797
T3 - 2022 International Wireless Communications and Mobile Computing, IWCMC 2022
SP - 271
EP - 277
BT - 2022 International Wireless Communications and Mobile Computing, IWCMC 2022
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 18th IEEE International Wireless Communications and Mobile Computing, IWCMC 2022
Y2 - 30 May 2022 through 3 June 2022
ER -