Deep Reinforcement Learning for Real-Time Trajectory Planning in UAV Networks
Ref: CISTER-TR-200401 Publication Date: 15 to 19, Jun, 2020
Deep Reinforcement Learning for Real-Time Trajectory Planning in UAV Networks
Ref: CISTER-TR-200401 Publication Date: 15 to 19, Jun, 2020Abstract:
In Unmanned Aerial Vehicle (UAV)-enabled wireless
powered sensor networks, a UAV can be employed to
charge the ground sensors remotely via Wireless Power Transfer
(WPT) and collect the sensory data. This paper focuses
on trajectory planning of the UAV for aerial data collection
and WPT to minimize buffer overflow at the ground sensors
and unsuccessful transmission due to lossy airborne channels.
Consider network states of battery levels and buffer lengths
of the ground sensors, channel conditions, and location of the
UAV. A flight trajectory planning optimization is formulated
as a Partial Observable Markov Decision Process (POMDP),
where the UAV has partial observation of the network states.
In practice, the UAV-enabled sensor network contains a large
number of network states and actions in POMDP while the
up-to-date knowledge of the network states is not available
at the UAV. To address these issues, we propose an onboard
deep reinforcement learning algorithm to optimize the realtime
trajectory planning of the UAV given outdated knowledge
on the network states.
Kai Li
Wei Ni
Eduardo TovarEvents:
16th International Conference on Wireless Communications & Mobile Computing (IWCMC 2020), pp 958-963.
Online.
DOI:10.1109/IWCMC48107.2020.9148316.
ISBN: 978-1-7281-3129-0.
ISSN: 2376-6506.
Record Date: 6, Apr, 2020