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Green wireless power transfer system for a drone fleet managed by reinforcement learning in smart industry
سیستم انتقال برق بی سیم سبز برای ناوگان هواپیماهای بدون سرنشین که با یادگیری تقویتی در صنعت هوشمند مدیریت می شود-2020 The optimal management of a fleet of drones is proposed in this paper for providing connectivity to sensors and
actuators in Industrial Internet of Things (IIoT) scenarios. The persistent mission without any human intervention
on the battery charge is obtained by means of an on-field wind generator supplying a charge station that
adopts resonant wireless power transfer. The objective of the fleet management is to provide the best connectivity
over the time considering the variability of both the bandwidth request and the wind energy availability.
The optimal management is performed by a system controller adopting reinforcement learning (RL) for
deciding the number of drones to take off and, consequently, the instantaneous provided bandwidth. A constant
charge time of drone battery represents a key element of the system because this enables to strongly reduce the
complexity of the system controller task. To this purpose, an adaptive current control for the charge station is
introduced to compensate charge time variabilities due to the coupling factor changes caused by misalignments
that can occur between a pad and a drone. The results have highlighted that the RL provides good performance
improvement in case of green generation. An important aspect arose from this study is the ability of RL to
increase the saved energy even if it is not considered as a target of the controller. Keywords: Artificial intelligence | Drone | Industry 4.0 | Internet of Things | Wind generator | Wireless power transfer |
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