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Cited 5 time in webofscience Cited 5 time in scopus
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Deep Reinforcement Learning Multi-UAV Trajectory Control for Target Tracking

Authors
Moon, JiseonPapaioannou, SavvasLaoudias, ChristosKolios, PanayiotisKim, Sunwoo
Issue Date
Oct-2021
Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Keywords
Target tracking; Unmanned aerial vehicles; Reinforcement learning; Navigation; Location awareness; Time measurement; State estimation; Multiagent deep reinforcement learning (DRL); multitarget tracking; unmanned aerial vehicle (UAV)
Citation
IEEE INTERNET OF THINGS JOURNAL, v.8, no.20, pp.15441 - 15455
Indexed
SCIE
SCOPUS
Journal Title
IEEE INTERNET OF THINGS JOURNAL
Volume
8
Number
20
Start Page
15441
End Page
15455
URI
https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/140623
DOI
10.1109/JIOT.2021.3073973
ISSN
2327-4662
Abstract
In this article, we propose a novel deep reinforcement learning (DRL) approach for controlling multiple unmanned aerial vehicles (UAVs) with the ultimate purpose of tracking multiple first responders (FRs) in challenging 3-D environments in the presence of obstacles and occlusions. We assume that the UAVs receive noisy distance measurements from the FRs which are of two types, i.e., Line of Sight (LoS) and non-LoS (NLoS) measurements and which are used by the UAV agents in order to estimate the state (i.e., position) of the FRs. Subsequently, the proposed DRL-based controller selects the optimal joint control actions according to the Cramer-Rao lower bound (CRLB) of the joint measurement likelihood function to achieve high tracking performance. Specifically, the optimal UAV control actions are quantified by the proposed reward function, which considers both the CRLB of the entire system and each UAV's individual contribution to the system, called global reward and difference reward, respectively. Since the UAVs take actions that reduce the CRLB of the entire system, tracking accuracy is improved by ensuring the reception of high quality LoS measurements with high probability. Our simulation results show that the proposed DRL-based UAV controller provides a highly accurate target tracking solution with a very low runtime cost.
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