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dc.contributor.author
Chung, Jen Jen
dc.contributor.author
Rebhuhn, Carrie
dc.contributor.author
Yates, Connor
dc.contributor.author
Hollinger, Geoffrey A.
dc.contributor.author
Tumer, Kagan
dc.date.accessioned
2021-04-08T13:08:30Z
dc.date.available
2019-07-16T13:11:39Z
dc.date.available
2019-07-16T14:56:30Z
dc.date.available
2021-04-08T09:10:07Z
dc.date.available
2021-04-08T13:08:30Z
dc.date.issued
2019-08
dc.identifier.issn
0929-5593
dc.identifier.issn
1573-7527
dc.identifier.other
10.1007/s10514-018-9800-z
en_US
dc.identifier.uri
http://hdl.handle.net/20.500.11850/353410
dc.identifier.doi
10.3929/ethz-b-000353410
dc.description.abstract
There is strong commercial interest in the use of large scale automated transport robots in industrial settings (e.g. warehouse robots) and we are beginning to see new applications extending these systems into our urban environments in the form of autonomous cars and package delivery drones. This new technology comes with new risks—increasing traffic congestion and concerns over safety; it also comes with new opportunities—massively distributed information and communication systems. In this paper, we present a method that leverages the distributed nature of the autonomous traffic to provide improved traffic throughput while maintaining strict capacity constraints across the network. Our proposed multiagent-based dynamic traffic management strategy borrows concepts from both air traffic control and highway metering lights. We introduce controller agents whose actions are to adjust the robots’ perceived “costs” of traveling across different parts of the traffic network. This approach allows each robot the flexibility of using its own (potentially proprietary) navigation algorithm, while still being bound by the “rules of the road.” The control policies of the agents are defined as neural networks whose weights are learned via cooperative coevolution across the entire traffic management team. Results in a real world road network and a simulated warehouse domain demonstrate that our multiagent traffic management system provides substantial improvements to overall traffic throughput in terms of number of successful trips in a fixed amount of time, as well as faster average traversal times.
en_US
dc.format
application/pdf
en_US
dc.language.iso
en
en_US
dc.publisher
Springer
en_US
dc.subject
Multiagent systems
en_US
dc.subject
Traffic management
en_US
dc.subject
Learning for coordination
en_US
dc.title
A multiagent framework for learning dynamic traffic management strategies
en_US
dc.type
Journal Article
dc.date.published
2018-08-21
ethz.journal.title
Autonomous Robots
ethz.journal.volume
43
en_US
ethz.journal.issue
6
en_US
ethz.journal.abbreviated
Auton. Robots
ethz.pages.start
1375
en_US
ethz.pages.end
1391
en_US
ethz.version.deposit
publishedVersion
en_US
ethz.notes
It was possible to publish this article open access thanks to a Swiss National Licence with the publisher.
en_US
ethz.identifier.wos
ethz.identifier.scopus
ethz.publication.place
Dordrecht
en_US
ethz.publication.status
published
en_US
ethz.leitzahl
ETH Zürich::00002 - ETH Zürich::00012 - Lehre und Forschung::00007 - Departemente::02130 - Dep. Maschinenbau und Verfahrenstechnik / Dep. of Mechanical and Process Eng.::02620 - Inst. f. Robotik u. Intelligente Systeme / Inst. Robotics and Intelligent Systems::03737 - Siegwart, Roland Y. / Siegwart, Roland Y.
en_US
ethz.leitzahl.certified
ETH Zürich::00002 - ETH Zürich::00012 - Lehre und Forschung::00007 - Departemente::02130 - Dep. Maschinenbau und Verfahrenstechnik / Dep. of Mechanical and Process Eng.::02620 - Inst. f. Robotik u. Intelligente Systeme / Inst. Robotics and Intelligent Systems::03737 - Siegwart, Roland Y. / Siegwart, Roland Y.
en_US
ethz.date.deposited
2019-07-16T13:11:46Z
ethz.source
FORM
ethz.eth
yes
en_US
ethz.availability
Embargoed
en_US
ethz.date.embargoend
2023-08-21
ethz.rosetta.installDate
2019-07-16T14:56:38Z
ethz.rosetta.lastUpdated
2021-02-15T05:16:59Z
ethz.rosetta.exportRequired
true
ethz.rosetta.versionExported
true
ethz.COinS
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