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Weighted fair energy transfer in a UAV network: A multi-agent deep reinforcement learning approach

Author

Listed:
  • Murshed, Shabab
  • Nibir, Abu Shaikh
  • Razzaque, Md. Abdur
  • Roy, Palash
  • Elhendi, Ahmed Zohier
  • Hassan, Md. Rafiul
  • Hassan, Mohammad Mehedi

Abstract

Flying Energy Sources (FESs) have been proven highly effective in transferring energy to battery-powered Unmanned Aerial Vehicles (UAVs). Such wireless transfer techniques in the literature were able to extend the flight duration of UAVs; however, they overlooked the fair distribution of energy among UAVs, which is of utmost importance for supporting diverse application demands. In this work, we quantify the urgency level of a UAV following its application responsibility, energy charging, and drainage rates and develop a framework for optimal energy transfer from FESs to UAVs in a weighted-fair way. Even though the developed framework can promote a highly balanced and fair energy distribution, its computation cannot always be done in polynomial time. Alternatively, to achieve a real-time solution, we further develop a Multi-Agent Deep Deterministic Policy Gradient (MADDPG) algorithm, namely AERIAL, to effectively model the complex interactions and dependencies among the UAVs and FESs and improve the wireless energy transfer process. The AERIAL system is implemented in the OpenAI Gym simulator platform and its performances have been compared with the state-of-the-art approaches. As high as improvements of 25.2% in fairness and 19.4% in average energy level demonstrated by the AERIAL system prove its effectiveness.

Suggested Citation

  • Murshed, Shabab & Nibir, Abu Shaikh & Razzaque, Md. Abdur & Roy, Palash & Elhendi, Ahmed Zohier & Hassan, Md. Rafiul & Hassan, Mohammad Mehedi, 2024. "Weighted fair energy transfer in a UAV network: A multi-agent deep reinforcement learning approach," Energy, Elsevier, vol. 292(C).
    • Handle: RePEc:eee:energy:v:292:y:2024:i:c:s0360544224002986
    DOI: 10.1016/j.energy.2024.130527
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    References listed on IDEAS

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    1. Yao, Yongming & Wang, Jie & Zhou, Zhicong & Li, Hang & Liu, Huiying & Li, Tianyu, 2023. "Grey Markov prediction-based hierarchical model predictive control energy management for fuel cell/battery hybrid unmanned aerial vehicles," Energy, Elsevier, vol. 262(PA).
    2. Yurdusevimli Metin, Ece & Aygün, Hakan, 2019. "Energy and power aspects of an experimental target drone engine at non-linear controller loads," Energy, Elsevier, vol. 185(C), pages 981-993.
    3. Chang, Chengcheng & Zhao, Wanzhong & Wang, Chunyan & Luan, Zhongkai, 2023. "An energy management strategy of deep reinforcement learning based on multi-agent architecture under self-generating conditions," Energy, Elsevier, vol. 283(C).
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