Mini-micro unmanned aerial vehicle intelligence: a threat to land vehicles

Le,VT, Filippidis,A, Lim,CP, Abdelrahman,W and Nahavandi,S 2014, Mini-micro unmanned aerial vehicle intelligence: a threat to land vehicles. In Breuker,J, Guarino,N, Kok,J, Liu,J, Lopez de Mantaras,R, Mizoguchi,R, Musen,M, Pal,SK and Zhong,N (ed), Smart digital futures 2014, IOS Press, Amsterdam, The Netherlands, pp.79-91, doi: 10.3233/978-1-61499-405-3-79.

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Title Mini-micro unmanned aerial vehicle intelligence: a threat to land vehicles
Author(s) Le,VTORCID iD for Le,VT
Lim,CPORCID iD for Lim,CP
Nahavandi,SORCID iD for Nahavandi,S
Title of book Smart digital futures 2014
Editor(s) Breuker,J
Lopez de Mantaras,R
Publication date 2014
Series Frontiers in artificial intelligence and applications
Chapter number 8
Total chapters 79
Start page 79
End page 91
Total pages 13
Publisher IOS Press
Place of Publication Amsterdam, The Netherlands
Keyword(s) Artificial Intelligent
Science & Technology
Computer Science, Artificial Intelligence
Computer Science, Theory & Methods
Computer Science
Summary Increasing 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.
ISBN 9781614994046
ISSN 0922-6389
Language eng
DOI 10.3233/978-1-61499-405-3-79
Field of Research 080101 Adaptive Agents and Intelligent Robotics
080110 Simulation and Modelling
091303 Autonomous Vehicles
Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences
HERDC Research category B1 Book chapter
ERA Research output type B Book chapter
Copyright notice ©2014, IOS Press
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