Adaptive UAV swarm mission planning by temporal difference learning

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dc.contributor.authorGopalakrishnan, Shreevanth Krishnaa
dc.contributor.authorAl-Rubaye, Saba
dc.contributor.authorInalhan, Gokhan
dc.date.accessioned2021-12-08T12:17:52Z
dc.date.available2021-12-08T12:17:52Z
dc.date.issued2021-11-15
dc.description.abstractThe prevalence of Unmanned Aerial Vehicles (UAVs) in precision agriculture has been growing rapidly. This paper tackles the UAV global mission planning problem by incorporating a greater capacity for human-machine teaming in the architecture of a flexibly autonomous, near-fully-distributed Mission Management System for UAV swarms. Subsequently, the two problems of global mission planning are solved simultaneously using an integrated solution. This consists of a geometric clustering algorithm which prioritizes the minimization of overall mission time, and an off-policy, model-free Temporal Difference Learning global agent capable of learning about an initially unknown mission environment through simulations. The latter component makes the solution adaptive to missions with different requirements.en_UK
dc.identifier.citationGopalakrishnan SK, Al-Rubaye S, Inalhan G. (2021) Adaptive UAV swarm mission planning by temporal difference learning. In: Proceedings of the 2021 AIAA/IEEE 40th Digital Avionics Systems Conference (DASC), 3-7 October 2021, San Antonio, America.en_UK
dc.identifier.eissn978-1-6654-3420-1
dc.identifier.eissn2155-7209
dc.identifier.isbn978-1-6654-3421-8
dc.identifier.issn2155-7195
dc.identifier.urihttps://doi.org/10.1109/DASC52595.2021.9594300
dc.identifier.urihttps://dspace.lib.cranfield.ac.uk/handle/1826/17325
dc.language.isoenen_UK
dc.publisherIEEEen_UK
dc.rightsAttribution-NonCommercial 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by-nc/4.0/*
dc.subjectReinforcement Learningen_UK
dc.subjectTemporal Difference Learningen_UK
dc.subjectUAVen_UK
dc.subjectGlobal Mission Planningen_UK
dc.subjectPrecision Agricultureen_UK
dc.titleAdaptive UAV swarm mission planning by temporal difference learningen_UK
dc.typeConference paperen_UK

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