JACIII Vol.15 No.3 pp. 304-312
doi: 10.20965/jaciii.2011.p0304


Intelligent Automated Guided Vehicle Controller with Reverse Strategy

Shigeru Kato* and Kok Wai Wong**

*Niihama National College of Technology, 7-1 Yagumo-cho, Niihama-shi, Ehime 792-8580, Japan
**Murdoch University, South St., Murdoch, WA 6150, Australia

December 22, 2010
March 12, 2011
May 20, 2011
fuzzy rule interpolation (FRI), automated guided vehicle (AGV), obstacle avoidance

This paper describes the intelligent Automated Guided Vehicle (AGV) control system using Fuzzy Rule Interpolation (FRI) method. The AGV used in this paper is a virtual vehicle simulated using computer. The purpose of the control system is to control the simulated AGV by moving along the given path towards a goal. Some obstacles can be placed on or near the path to increase the difficulties of the control system. The intelligent AGV should follow the path by avoiding these obstacles. This system consists of two fuzzy controllers. One is the original FRI controller that mainly controls the forward movement of the AGV. Another one is the proposed reverse movement controller that deals with the critical situation. When the original FRI controller faces the critical situation, our proposed reverse controller will control the AGV to reverse and move forward towards the goal. Our proposed reverse controller utilizes the advantage of FRI method. In our system, we also develop a novel switching system to switch from original to the developed reverse controller.

  1. [1] L. A. Zadeh, “Fuzzy Sets,” Information and Control, Vol.8, Issue 3, pp. 338-353, 1965.
  2. [2] E. H. Mamdani and S. Assilian, “An Experiment in Linguistic Synthesis with a Fuzzy Logic Controller,” Int. J. of Human-Computer Studies, Vol.7, pp. 1-13, 1975.
  3. [3] K. W. Wong, D. Tikk, T. D. Gedeon, and L. T. Kóczy, “Fuzzy Rule Interpolation for Multidimensional Input Spaces with Applications: A Case Study,” IEEE Trans. of Fuzzy Systems, Vol.13, No.6, pp. 809-819, December 2005.
  4. [4] Sz. Kovács, “Fuzzy Rule Interpolation in Practice,” Proc. of the Joint 3rd Int. Conf. on Soft Computing and Intelligent Systems and 7th Int. Symposium on advanced Intelligent Systems (SCIS & ISIS 2006), (invited talk), p. 6, 2006.
  5. [5] Z. C. Johanyak, D. Tikk, S. Kovács, and K. W. Wong, “Fuzzy Rule Interpolation Matlab Toolbox - FRI Toolbox,” Proc. of IEEE Int. Conf. on Fuzzy Systems 2006, pp. 1427-1433, 2006.
  6. [6] J. Cselényi, Sz. Kováacs, L. Pap, and L. T. Kóczy, “New Concepts in the Fuzzy Logic Controlled Path Tracking Strategy of the Differential Steered AGVs,” 5th Int. Workshop on Robotics in Alpe-Adria-Danube Region, RAAD’96, pp. 587-592, June 1996.
  7. [7] Sz. Kovács, “Similarity Based Control Strategy Reconfiguration by Fuzzy Reasoning and Fuzzy Automata,” Proc. of the IECON-2000, IEEE Int. Conf. on Industrial Electronics, Control and Instrumentation, pp. 542-547, October 2000.
  8. [8] Sz. Kovács and L. T. Kóczy, “Application of the Approximate Fuzzy Reasoning Based on Interpolation in the Vague Environment of the Fuzzy Rulebase in the Fuzzy Logic Controlled Path Tracking Strategy of Differential Steered AGVs,” Computational Intelligence – Theory and Applications, Lecture Notes in Computer Science, Vol.1226, Springer, pp. 456-467, 1997.
  9. [9] Sz. Kovács and L. T. Kóczy, “Application of an Approximate Fuzzy Logic Controller in an AGV Steering System, Path Tracking and Collision Avoidance Strategy,” Fuzzy Set Theory and Applications, TatraMountains Mathematical Publications, Mathematical Institute Slovak Academy of Sciences, Vol.16, pp. 456-467, Bratislava, Slovakia, 1999.
  10. [10] S. Kato and K. W. Wong, “Intelligent Automated Guided Vehicle with Reverse Strategy: A Comparison Study,” Lecture Notes in Computer Science 5506, Springer Heidelberg, pp. 638-646, August 2009.
  11. [11] S. Kato and K. W. Wong, “Automated Guided Vehicle with Reverse Strategy,” Australian J. of Intelligent Information Processing Systems, Vol.10, No.2, pp. 29-35, 2008.
  12. [12] S. Kato and K. W. Wong, “The Automated Guided Vehicle Using Fuzzy Control and CBR Techniques,” Proc. of Joint 4th Int. Conf. on Soft Computing and Intelligent Systems and 9th Int. Symposium on advanced Intelligent Systems, pp. 1788-1792, September 2008.
  13. [13] J. Hoffmann, M. Jüngel, and M. Lötzsch, “A Vision Based System for Goal-Directed Obstacle Avoidance,” 8th Int. Workshop on RoboCup 2004, LNAI Springer, Vol.3276, pp. 418-425, 2005.
  14. [14] C. C. Wong, C. T. Cheng, K. H. Huang, and Y. T. Yang, “Design and Implementation of Humanoid Robot for Obstacle Avoidance,” Proc. of FIRA Robot World Congress, 2007.
  15. [15] G. Zong, L. Deng, and W. Wang, “A Method for Robustness Improvement of Robot Obstacle Avoidance Algorithm,” Proc. of IEEE Int. Conf. on ROBOTICS and BIOMIMETICS, pp. 115-119, 2006.

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Last updated on Jul. 29, 2016