Deep Reinforcement Learning for Persistent Cruise Control in UAV-aided Data Collection
Ref: CISTER-TR-211008 Publication Date: 4, Oct, 2021
Deep Reinforcement Learning for Persistent Cruise Control in UAV-aided Data Collection
Ref: CISTER-TR-211008 Publication Date: 4, Oct, 2021Abstract:
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.
Harrison Kurunathan
Kai Li
Wei Ni
Eduardo TovarDocument:
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DownloadThe IEEE Local Computer Networks conference (LCN).
Edmonton, Canada.
Record Date: 27, Oct, 2021