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
Online-Zugang:	Volltext
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