File(s) under permanent embargo
Mini-micro unmanned aerial vehicle intelligence: a threat to land vehicles
Version 2 2024-06-06, 08:04Version 2 2024-06-06, 08:04
Version 1 2015-03-26, 10:02Version 1 2015-03-26, 10:02
chapter
posted on 2024-06-06, 08:04 authored by Vu LeVu Le, A Filippidis, Chee Peng Lim, W Abdelrahman, S NahavandiIncreasing use of commercial off-the-shelf Mini-Micro Unmanned Aerial Vehicle (MAV) systems with enhanced intelligence methodologies can potentially be a threat, if this technology falls into the wrong hands. In this study, we investigate the level of threat imposed on critical infrastructure using different MAV swarm artificial intelligence traits and coordination methodologies. The critical infrastructure in consideration is a moving commercial land vehicle that may be transporting for example an important civil servant or politician. Non-dimensional fitness functions used for measuring MAV mission effectiveness have been established for the case studies considered in this paper. The findings indicated that increased in intelligent and coordination level elevate teams' efficiency, therefore poses a higher degree of threat to targeted land vehicle. Observations from the study have suggested that memory-based cooperative technique provides a consistent efficiency compared to other methods for the mission objectives considered in this paper. © 2014 The authors and IOS Press. All rights reserved.
History
Volume
262Chapter number
8Pagination
79-91Publisher DOI
ISSN
0922-6389ISBN-13
9781614994046Language
engPublication classification
B Book chapter, B1 Book chapterCopyright notice
2014, IOS PressExtent
79Editor/Contributor(s)
Breuker J, Guarino N, Kok J, Liu J, Lopez de Mantaras R, Mizoguchi R, Musen M, Pal SK, Zhong NPublisher
IOS PressPlace of publication
Amsterdam, The NetherlandsTitle of book
Smart digital futures 2014Series
Frontiers in artificial intelligence and applicationsUsage metrics
Categories
No categories selectedKeywords
Licence
Exports
RefWorksRefWorks
BibTeXBibTeX
Ref. managerRef. manager
EndnoteEndnote
DataCiteDataCite
NLMNLM
DCDC