Literature Database Entry

kurunathan2021deep

Harrison Kurunathan, Kai Li, Wei Ni, Eduardo Tovar and Falko Dressler, "Deep Reinforcement Learning for Persistent Cruise Control in UAV-aided Data Collection," Proceedings of 46th IEEE Conference on Local Computer Networks (LCN 2021), Virtual Conference, October 2021, pp. 347–350.

Abstract

Autonomous UAV cruising is gaining attention due to its flexible deployment in remote sensing, surveillance, and reconnaissance. A critical challenge in data collection with the autonomous UAV is the buffer overflows at the ground sensors and packet loss due to lossy airborne channels. Trajectory planning of the UAV is vital to alleviate buffer overflows as well as channel fading. In this work, we propose a Deep Deterministic Policy Gradient based Cruise Control (DDPG-CC) to reduce the overall packet loss through online training of headings and cruise velocity of the UAV, as well as the selection of the ground sensors for data collection. Preliminary performance evaluation demonstrates that DDPG-CC reduces the packet loss rate by under 5% when sufficient training is provided to the UAV.

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Harrison Kurunathan
Kai Li
Wei Ni
Eduardo Tovar
Falko Dressler

BibTeX reference

@inproceedings{kurunathan2021deep,
    author = {Kurunathan, Harrison and Li, Kai and Ni, Wei and Tovar, Eduardo and Dressler, Falko},
    doi = {10.1109/LCN52139.2021.9525022},
    title = {{Deep Reinforcement Learning for Persistent Cruise Control in UAV-aided Data Collection}},
    pages = {347--350},
    publisher = {IEEE},
    address = {Virtual Conference},
    booktitle = {46th IEEE Conference on Local Computer Networks (LCN 2021)},
    month = {10},
    year = {2021},
   }
   
   

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