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

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