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Green wireless power transfer system for a drone fleet managed by reinforcement learning in smart industry

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  • Faraci, Giuseppe
  • Raciti, Angelo
  • Rizzo, Santi Agatino
  • Schembra, Giovanni

Abstract

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.

Suggested Citation

  • Faraci, Giuseppe & Raciti, Angelo & Rizzo, Santi Agatino & Schembra, Giovanni, 2020. "Green wireless power transfer system for a drone fleet managed by reinforcement learning in smart industry," Applied Energy, Elsevier, vol. 259(C).
    • Handle: RePEc:eee:appene:v:259:y:2020:i:c:s0306261919318914
    DOI: 10.1016/j.apenergy.2019.114204
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    References listed on IDEAS

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    4. Chiang, Wen-Chyuan & Li, Yuyu & Shang, Jennifer & Urban, Timothy L., 2019. "Impact of drone delivery on sustainability and cost: Realizing the UAV potential through vehicle routing optimization," Applied Energy, Elsevier, vol. 242(C), pages 1164-1175.
    5. Lin, Boqiang & Chen, Yufang, 2019. "Impacts of policies on innovation in wind power technologies in China," Applied Energy, Elsevier, vol. 247(C), pages 682-691.
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    Cited by:

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