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Mathematical Problems in Engineering
Volume 2014 (2014), Article ID 678210, 20 pages
http://dx.doi.org/10.1155/2014/678210
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.

Linked References

  1. J. Leitner, “Multi-robot cooperation in space: a survey,” in Proceedings of the Advanced Technologies for Enhanced Quality of Life (AT-EQUAL '09), vol. 37, pp. 144–151, IEEE Computer Society, Iași, Romania, July 2009. View at Publisher · View at Google Scholar · View at Scopus
  2. G. Dudek, M. R. M. Jenkin, E. Milios, and D. Wilkes, “A taxonomy for multi-agent robotics,” Autonomous Robots, vol. 3, no. 4, pp. 375–397, 1996. View at Google Scholar · View at Scopus
  3. A. Chella, G. Lo Re, I. Macaluso, M. Ortolani, and D. Peri, “Chapter 1. A networking framework for multi-robot coordination,” in Recent Advances in Multi Robot Systems, A. Lazinica, Ed., pp. 1–14, 2008. View at Publisher · View at Google Scholar
  4. Yinka-Banjo, C. O. ; Osunmakinde, and I. O. ; Bagula A, “Autonomous multi-robot behaviours for safety inspection under the constraints of underground mine Terrains,” Ubiquitous Computing and Communication Journal, vol. 7, no. 5, pp. 1316–1328, 2012. View at Google Scholar
  5. S. Yarkan, S. Güzelgöz, H. Arslan, and R. Murphy, “Underground mine communications: a survey,” IEEE Communications Surveys & Tutorials, vol. 11, no. 3, pp. 125–142, 2009. View at Publisher · View at Google Scholar · View at Scopus
  6. M. Peasgood, J. McPhee, and C. Clark, Complete and Scalable Multi-Robot Planning in Tunnel Environments, Digitalcommons, 2006.
  7. F. E. Schneider, D. Wildermuth, and M. Moors, “Methods and experiments for hazardous area activities using multi-robot system,” in Proceedings of the IEEE International Conference on Robotics and Automation, pp. 3559–3564, May 2004. View at Scopus
  8. R. Zlot and A. Stentz, “Market-based multirobot coordination for complex tasks,” International Journal of Robotics Research, vol. 25, no. 1, pp. 73–101, 2006. View at Publisher · View at Google Scholar · View at Scopus
  9. L. E. Parker, “Current research in multi-robot systems,” in Proceedings of the 7th International Symposium on Artificial Life and Robotics (ISAROB '03), vol. 7, pp. 1–15, 2003.
  10. C. Thorp and H. Durrant-Whyte, “Field robots,” in Proceedings of the 10th International Symposium on Robotics Research, vol. 6, pp. 329–3240, 2003.
  11. S. R. Teleka, J. J. Green, S. Brink, J. Sheer, and K. Hlophe, “The automation of the “Making Safe” process in South African Hard-Rock underground mines,” in International Journal of Engineering and Advanced Technology (IJEAT '12), vol. 1, pp. 1–7, April 2012.
  12. L. Cragg and H. Hu, “Application of reinforcement learning to a mobile robot in reaching recharging station operation,” in Intelligent Production Machines and Systems, pp. 357–363, 2005. View at Google Scholar
  13. R. A. C. Bianchi, C. H. C. Ribeiro, and A. H. R. Costa, “On the relation between ant colony optimization and heuristically accelerated reinforcement learning,” in Proceedings of the 1st International Workshop on Hybrid Control of Autonomous System, pp. 49–55, 2009.
  14. E. Bonabeau, M. Dorigo, and G. Theraulaz, Swarm Intelligence, from Natural to Artificial Systems, Oxford University Press, Oxford, UK, 1999.
  15. Y. Liu and K. M. Passino, Swarm Intelligence: Literature Overview, Department of Electrical Engineering, University of Ohio, 2000.
  16. L. Li, A. Martinoli, and Y. S. Abu-Mostafa, “Emergent specialization in swarm systems,” in Intelligent Data Engineering and Automated Learning—IDEAL 2002, vol. 2412 of Lecture Notes in Computer Science, pp. 261–266, 2002. View at Publisher · View at Google Scholar
  17. B. Englot and F. Hover, “Multi-goal feasible path planning using ant colony optimization,” in Proceedings of the IEEE International Conference on Robotics and Automation (ICRA '11), pp. 2255–2260, May 2011. View at Publisher · View at Google Scholar · View at Scopus
  18. Z. N. Naqiv, H. Matheru, and K. Chadha, “Review of ant colony optimization on vehice routing problems and introduction to estimated-based ACO,” in International Conference on Environment Science and Engineering (IPCBEE '11), vol. 8, pp. 161–166, 2011.
  19. M. Dorigo and T. Stützle, “The ant colony optimization metaheuristic: algorithms, applications and advances,” Tech. Rep. IRIDIA/2000-32, 2000. View at Google Scholar
  20. R. Claes and T. Holvoe, “Cooperative ant colony optimization in traffic route calculations,” in Advances on Practical Applications of Agents and Multi-Agent Systems, vol. 155 of Advances in Intelligent and Soft Computing, pp. 23–34, 2012. View at Publisher · View at Google Scholar