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Mathematical Problems in Engineering
Volume 2014 (2014), Article ID 678210, 20 pages
Research Article

Cooperative Behaviours with Swarm Intelligence in Multirobot Systems for Safety Inspections in Underground Terrains

1Department of Computer Science, Faculty of Science, University of Cape Town, Private Bag X3, Rondebosch, Cape Town 7701, South Africa
2School of Computing, College of Science, Engineering and Technology, University of South Africa (UNISA), P.O. Box 392, Pretoria 0003, South Africa
3Department of Computer Science, Faculty of Science, University of Western Cape, Private Bag X17, Bellville 7535, South Africa

Received 6 February 2014; Revised 16 May 2014; Accepted 26 May 2014; Published 20 July 2014

Academic Editor: Leo Chen

Copyright © 2014 Chika Yinka-Banjo et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Underground mining operations are carried out in hazardous environments. To prevent disasters from occurring, as often as they do in underground mines, and to prevent safety routine checkers from disasters during safety inspection checks, multirobots are suggested to do the job of safety inspection rather than human beings and single robots. Multirobots are preferred because the inspection task will be done in the minimum amount of time. This paper proposes a cooperative behaviour for a multirobot system (MRS) to achieve a preentry safety inspection in underground terrains. A hybrid QLACS swarm intelligent model based on Q-Learning (QL) and the Ant Colony System (ACS) was proposed to achieve this cooperative behaviour in MRS. The intelligent model was developed by harnessing the strengths of both QL and ACS algorithms. The ACS optimizes the routes used for each robot while the QL algorithm enhances the cooperation between the autonomous robots. A description of a communicating variation within the QLACS model for cooperative behavioural purposes is presented. The performance of the algorithms in terms of without communication, with communication, computation time, path costs, and the number of robots used was evaluated by using a simulation approach. Simulation results show achieved cooperative behaviour between robots.