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Article

In Situ MIMO-WPT Recharging of UAVs Using Intelligent Flying Energy Sources

1
School of Engineering and Information Technology, The University of New South Wales, Canberra, ACT 2600, Australia
2
School of Engineering and Technology, The Central Queensland University, Sydney, NSW 2000, Australia
3
School of Computer Science and Engineering, The University of New South Wales, Sydney, NSW 2052, Australia
*
Author to whom correspondence should be addressed.
Academic Editors: Higinio González Jorge and Luis Miguel González de Santos
Drones 2021, 5(3), 89; https://doi.org/10.3390/drones5030089
Received: 12 July 2021 / Revised: 25 August 2021 / Accepted: 1 September 2021 / Published: 5 September 2021
(This article belongs to the Special Issue Advances in Civil Applications of Unmanned Aircraft Systems)
Unmanned Aerial Vehicles (UAVs), used in civilian applications such as emergency medical deliveries, precision agriculture, wireless communication provisioning, etc., face the challenge of limited flight time due to their reliance on the on-board battery. Therefore, developing efficient mechanisms for in situ power transfer to recharge UAV batteries holds potential to extend their mission time. In this paper, we study the use of the far-field wireless power transfer (WPT) technique from specialized, transmitter UAVs (tUAVs) carrying Multiple Input Multiple Output (MIMO) antennas for transferring wireless power to receiver UAVs (rUAVs) in a mission. The tUAVs can fly and adjust their distance to the rUAVs to maximize energy transfer gain. The use of MIMO antennas further boosts the energy reception by narrowing the energy beam toward the rUAVs. The complexity of their dynamic operating environment increases with the growing number of tUAVs and rUAVs with varying levels of energy consumption and residual power. We propose an intelligent trajectory selection algorithm for the tUAVs based on a deep reinforcement learning model called Proximal Policy Optimization (PPO) to optimize the energy transfer gain. The simulation results demonstrate that the PPO-based system achieves about a tenfold increase in flight time for a set of realistic transmit power, distance, sub-band number and antenna numbers. Further, PPO outperforms the benchmark movement strategies of “Traveling Salesman Problem” and “Low Battery First” when used by the tUAVs. View Full-Text
Keywords: UAVs; wireless power transfer; RF energy harvesting; MIMO; deep reinforcement learning UAVs; wireless power transfer; RF energy harvesting; MIMO; deep reinforcement learning
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MDPI and ACS Style

Hoseini, S.A.; Hassan, J.; Bokani, A.; Kanhere, S.S. In Situ MIMO-WPT Recharging of UAVs Using Intelligent Flying Energy Sources. Drones 2021, 5, 89. https://doi.org/10.3390/drones5030089

AMA Style

Hoseini SA, Hassan J, Bokani A, Kanhere SS. In Situ MIMO-WPT Recharging of UAVs Using Intelligent Flying Energy Sources. Drones. 2021; 5(3):89. https://doi.org/10.3390/drones5030089

Chicago/Turabian Style

Hoseini, Sayed A., Jahan Hassan, Ayub Bokani, and Salil S. Kanhere. 2021. "In Situ MIMO-WPT Recharging of UAVs Using Intelligent Flying Energy Sources" Drones 5, no. 3: 89. https://doi.org/10.3390/drones5030089

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