NDLI: Ant colony optimization based swarms: implementation for the mine detection application

Content Provider	IEEE Xplore Digital Library
Author	Munirajan, V.K. Sahin, F. Cole, E.
Copyright Year	2004
Description	Author affiliation: Adv. Technol. Res. Center, Sytex Group Inc., VA (Munirajan, V.K.)
Abstract	Mine detection is a sensitive task confronting the battlefield strategists. There is an ever-increasing demand for proper and sophisticated resources for many issues involved in the task. Traditional practices still involve human force directly in executing the tasks in spite of the advances in technology for tools and implements for the operation [GAO, 2001]. The problem includes various facets inherently: two of the prominent issues are location of mines over a minefield and secondly removal of the mines once located [GAO, 2001]. These two issues are not totally independent as technology used for one can directly or indirectly affect the other. Developments in artificial intelligence, natural heuristics, computational optimization and robotics have endowed us with the ability to realize unmanned robots (or robot like vehicles) that work intelligently on a real time basis in attempting at the problem of mine detection. In this paper we focus on the algorithms developed using ant colony optimization based approaches to the mine detection application and its implementation on a real-time basis. We focus on certain optimization techniques that could be used for effective realization of the algorithm. Generic groundscout robots had been already built at the MABL, RIT [Sahin F. et al., 2003]. These robots have been used to demonstrate the implementation
Starting Page	716
Ending Page	721
File Size	418057
Page Count	6
File Format	PDF
ISBN	0780385667
ISSN	1062922X
DOI	10.1109/ICSMC.2004.1398386
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2004-10-10
Publisher Place	Netherlands
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Ant colony optimization Intelligent robots Robot kinematics Particle swarm optimization Artificial intelligence Navigation Intelligent vehicles Recruitment Humans Vehicle detection
Content Type	Text
Resource Type	Article

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