Massive Autonomous UAV Path Planning: A Neural Network Based Mean-Field Game Theoretic Approach Shiri, Hamid, Park, Jihong and Bennis, Mehdi 2019, Massive Autonomous UAV Path Planning: A Neural Network Based Mean-Field Game Theoretic Approach, in GLOBECOM 2019: Proceedings of the IEEE Global Communications Conference, Institute of Electrical and Electronics Engineers, Piscataway, N.J., doi: 10.1109/GLOBECOM38437.2019.9013181. Attached Files Name Description MIMEType Size Downloads Title Massive Autonomous UAV Path Planning: A Neural Network Based Mean-Field Game Theoretic Approach Author(s) Shiri, Hamid Park, Jihong orcid.org/0000-0001-7623-6552 Bennis, Mehdi Conference name IEEE Global Communications. Conference (2019 : Waikoloa, Hl, USA) Conference location Waikoloa, HI, USA Conference dates 2019/12/09 - 2019/12/13 Title of proceedings GLOBECOM 2019: Proceedings of the IEEE Global Communications Conference Publication date 2019 Total pages 7 Publisher Institute of Electrical and Electronics Engineers Place of publication Piscataway, N.J. Keyword(s) unmanned aerial vehicles mathematical model nickel computational modeling collision avoidance real-time systems games ISBN 9781728109626 Language eng DOI 10.1109/GLOBECOM38437.2019.9013181 Indigenous content off HERDC Research category E1.1 Full written paper - refereed Persistent URL http://hdl.handle.net/10536/DRO/DU:30139687 Document type: Conference Paper Collections: Faculty of Science, Engineering and Built Environment School of Information Technology Related Links Link Description Connect to published version Go to link with your DU access privileges     Unless expressly stated otherwise, the copyright for items in DRO is owned by the author, with all rights reserved. Versions Version Filter Type Mon, 06 Jul 2020, 13:13:54 EST Tue, 07 Jul 2020, 05:00:59 EST Tue, 07 Jul 2020, 06:03:55 EST Thu, 23 Jul 2020, 14:46:38 EST Thu, 23 Jul 2020, 14:48:57 EST Sat, 14 Nov 2020, 01:31:01 EST Filtered Full Citation counts:   Cited 3 times in TR Web of Science Cited 7 times in Scopus Search Google Scholar Access Statistics: 58 Abstract Views, 2 File Downloads  -  Detailed Statistics Created: Mon, 06 Jul 2020, 13:13:54 EST

Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au.