Unmanned aerial vehicle trajectory design in wireless sensor networks: a deep reinforcement learning method

Jingce Yang; Yulu Yang; Han Xu; Jing Hu; Tiecheng Song

doi:10.1117/12.2685727

15 August 2023 Unmanned aerial vehicle trajectory design in wireless sensor networks: a deep reinforcement learning method

Jingce Yang, Yulu Yang, Han Xu, Jing Hu, Tiecheng Song

Author Affiliations +

Proceedings Volume 12719, Second International Conference on Electronic Information Technology (EIT 2023); 1271929 (2023) https://doi.org/10.1117/12.2685727
Event: Second International Conference on Electronic Information Technology (EIT 2023), 2023, Wuhan, China

Abstract

Unmanned Aerial Vehicle (UAV)-powered Wireless Sensor Networks (WSNs) are considered to be a promising solution to the problem of the limited power of the Sensor Nodes (SNs). In this paper, we introduce a UAV-powered WSN system, where multi-UAVs undertake the role of remote charging stations. To optimize the overall power efficiency, we design the collaborative trajectories of the UAVs. In order to solve the problem of trajectory planning, we first model the service process as a Markov decision process (MDP), and then propose a Multi-Agent Deep Reinforcement Learning (MADRL) based algorithm named Modified Multi-agent Deep Deterministic Policy Gradient (M2DDPG), which is learned centrally and executed discretely. The simulation has demonstrated the validity, efficacy, and superior performance of the proposed M2DDPG algorithm compared to the baseline algorithm.

Citation Download Citation

Jingce Yang, Yulu Yang, Han Xu, Jing Hu, and Tiecheng Song "Unmanned aerial vehicle trajectory design in wireless sensor networks: a deep reinforcement learning method", Proc. SPIE 12719, Second International Conference on Electronic Information Technology (EIT 2023), 1271929 (15 August 2023); https://doi.org/10.1117/12.2685727

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