Autonomous Tracking Using a Swarm of UAVs: A Constrained Multi-Agent Reinforcement Learning Approach

In this paper, we aim to design an autonomous tracking system for a swarm of unmanned aerial vehicles (UAVs) to localize a radio frequency (RF) mobile target. In the system, UAVs equipped with omnidirectional received signal strength (RSS) sensors can cooperatively search the target with a specified...

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Veröffentlicht in:	IEEE Transactions on Vehicular Technology Jg. 69; H. 11; S. 13702 - 13717
Hauptverfasser:	Chen, Yu-Jia, Chang, Deng-Kai, Zhang, Cheng
Format:	Journal Article
Sprache:	Englisch Japanisch
Veröffentlicht:	New York IEEE 01.11.2020 Institute of Electrical and Electronics Engineers (IEEE) The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Algorithms constrained Markov decision process Feedback control localization and tracking Machine learning Markov processes Multi-agent reinforcement learning Multiagent systems Path planning Radio frequency Real-time systems Robots Sensors Signal strength Target tracking Tracking systems Unmanned aerial vehicles unmanned aerial vehicles (UAVs)
ISSN:	0018-9545, 1939-9359
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Abstract	In this paper, we aim to design an autonomous tracking system for a swarm of unmanned aerial vehicles (UAVs) to localize a radio frequency (RF) mobile target. In the system, UAVs equipped with omnidirectional received signal strength (RSS) sensors can cooperatively search the target with a specified tracking accuracy. To achieve fast localization and tracking in the highly dynamic channel environment (e.g., time-varying transmit power and intermittent signal), we formulate a flight decision problem as a constrained Markov decision process (CMDP) with the main objective of avoiding redundant UAV flight path. Then, we propose an enhanced multi-agent reinforcement learning to coordinate multiple UAVs performing real-time target tracking. The core of the proposed scheme is a feedback control system that takes into account the uncertainty of the channel estimate. We prove that the proposed algorithm can converge to the optimal decision. Our simulation results show that the proposed scheme outperforms standard Q-learning and multi-agent Q-learning algorithms in terms of searching time and successful localization probability.
AbstractList	In this paper, we aim to design an autonomous tracking system for a swarm of unmanned aerial vehicles (UAVs) to localize a radio frequency (RF) mobile target. In the system, UAVs equipped with omnidirectional received signal strength (RSS) sensors can cooperatively search the target with a specified tracking accuracy. To achieve fast localization and tracking in the highly dynamic channel environment (e.g., time-varying transmit power and intermittent signal), we formulate a flight decision problem as a constrained Markov decision process (CMDP) with the main objective of avoiding redundant UAV flight path. Then, we propose an enhanced multi-agent reinforcement learning to coordinate multiple UAVs performing real-time target tracking. The core of the proposed scheme is a feedback control system that takes into account the uncertainty of the channel estimate. We prove that the proposed algorithm can converge to the optimal decision. Our simulation results show that the proposed scheme outperforms standard Q-learning and multi-agent Q-learning algorithms in terms of searching time and successful localization probability.
Author	Chang, Deng-Kai Zhang, Cheng Chen, Yu-Jia
Author_xml	– sequence: 1 givenname: Yu-Jia orcidid: 0000-0001-7563-4073 surname: Chen fullname: Chen, Yu-Jia email: yjchen@ce.ncu.edu.tw organization: Department of Communication Engineering, National Central University, Taoyuan, Taiwan – sequence: 2 givenname: Deng-Kai surname: Chang fullname: Chang, Deng-Kai email: wl01420395@gmail.com organization: Department of Communication Engineering, National Central University, Taoyuan, Taiwan – sequence: 3 givenname: Cheng orcidid: 0000-0003-2135-7546 surname: Zhang fullname: Zhang, Cheng email: cheng.zhang@akane.waseda.jp organization: Department of Communication and Computer Engineering, Waseda University, Tokyo, Japan
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SubjectTerms	Algorithms constrained Markov decision process Feedback control localization and tracking Machine learning Markov processes Multi-agent reinforcement learning Multiagent systems Path planning Radio frequency Real-time systems Robots Sensors Signal strength Target tracking Tracking systems Unmanned aerial vehicles unmanned aerial vehicles (UAVs)
Title	Autonomous Tracking Using a Swarm of UAVs: A Constrained Multi-Agent Reinforcement Learning Approach
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