Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

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

Version 1 : Received: 12 July 2021 / Approved: 23 July 2021 / Online: 23 July 2021 (13:29:51 CEST)

A peer-reviewed article of this Preprint also exists.

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. 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.

Journal reference: Drones 2021, 5, 89
DOI: 10.3390/drones5030089

Abstract

The 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 hold potential in extending their mission time. In this paper, we study the use of 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. The use of MIMO antennas further boost 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. Simulation results demonstrate that with the use of PPO, the system achieves a tenfold flight time extension compared to no wireless recharging. Further, PPO outperforms the benchmark movement strategies of ’Traveling Salesman Problem’ and ’Low Battery First’ when used by the tUAVs.

Keywords

UAVs; Wireless Power Transfer; RF energy harvesting, MIMO; Deep Reinforcement Learning.

Subject

MATHEMATICS & COMPUTER SCIENCE, Algebra & Number Theory

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