Applied Intelligence

, Volume 37, Issue 2, pp 175–188 | Cite as

Autonomous navigation system using Event Driven-Fuzzy Cognitive Maps

  • Márcio Mendonça
  • Lúcia Valéria Ramos de Arruda
  • Flávio NevesJr.
Article

Abstract

This study developed an autonomous navigation system using Fuzzy Cognitive Maps (FCM). Fuzzy Cognitive Map is a tool that can model qualitative knowledge in a structured way through concepts and causal relationships. Its mathematical representation is based on graph theory. A new variant of FCM, named Event Driven-Fuzzy Cognitive Maps (ED-FCM), is proposed to model decision tasks and/or make inferences in autonomous navigation. The FCM’s arcs are updated from the occurrence of special events as dynamic obstacle detection. As a result, the developed model is able to represent the robot’s dynamic behavior in presence of environment changes. This model skill is achieved by adapting the FCM relationships among concepts. A reinforcement learning algorithm is also used to finely adjust the robot behavior. Some simulation results are discussed highlighting the ability of the autonomous robot to navigate among obstacles (navigation at unknown environment). A fuzzy based navigation system is used as a reference to evaluate the proposed autonomous navigation system performance.

Keywords

Mobile robot Autonomous navigation Fuzzy Cognitive Maps Intelligent decision systems 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Schraff RD (1994) Mechatronics and robotics for service applications. IEEE Robot Autom Mag 1(4):31–35 CrossRefGoogle Scholar
  2. 2.
    Mandow A, Gomes-de-Gabriel JM, Martinéz JL, Muñoz VF, Ollero A, García-Cerezo A (1996) The autonomous mobile robot AURORA for greenhouse operation. IEEE Robot Autom Mag 3(4):18–28 CrossRefGoogle Scholar
  3. 3.
    Chohra A, Farah A, Benmehrez C (1998) Neural navigation approach for intelligent autonomous vehicles (IAV) in partially structured environments. Appl Intell 8(3):219–233 CrossRefGoogle Scholar
  4. 4.
    Driankov D, Saffiotti A (2001) Fuzzy logic techniques for autonomous vehicle navigation. Physica-Verlag, Heidelberg Google Scholar
  5. 5.
    Pratihar DK, Deb K, Ghosh A (2002) Optimal path and gait generations simultaneously of a six-legged robot using a GA-fuzzy approach. Robot Auton Syst 41(2):1–20 CrossRefGoogle Scholar
  6. 6.
    Azouaoui O, Chohra A (2002) Soft computing based pattern classifiers for the obstacle avoidance behavior of intelligent autonomous vehicles (IAV). Appl Intell 16(3):249–272 MATHCrossRefGoogle Scholar
  7. 7.
    Hui NB, Mahendar V, Pratihar DK (2006) Time-optimal, collision-free navigation of a car-like mobile robot using neuro-fuzzy approaches. Fuzzy Sets Syst 157(16):2171–2204 MathSciNetMATHCrossRefGoogle Scholar
  8. 8.
    Xuefeng D, Hongmin Z, Yan S (2007) Autonomous navigation for wheeled mobile robots-a survey. In: 2nd international conference on innovative computing, information and control Google Scholar
  9. 9.
    Calvo R, Romero RAF (2006) A Hierarchical self-organizing controller for navigation of mobile robots. In: Proc of int joint conference on neural networks, IEEE World congress computational intelligence Google Scholar
  10. 10.
    Brooks RA (1986) A robust layered control system for a mobile robot. IEEE J Robot Autom 2(1):14–23 CrossRefGoogle Scholar
  11. 11.
    Murphy R (2000) Introduction to AI robotics. MIT Press, Cambridge Google Scholar
  12. 12.
    Arkin RC (1999) Behavior-based robotics. MIT Press, Cambridge Google Scholar
  13. 13.
    Connell J (1992) A hybrid architecture applied to robot navigation. In: Proc of the IEEE conf on robotics and automation, pp 2719–2724 CrossRefGoogle Scholar
  14. 14.
    Dickerson JA, Kosko B (1994) Virtual worlds as fuzzy cognitive maps. Presence 3:173–189 Google Scholar
  15. 15.
    Stylios DC, Groumpos P (2000) Fuzzy cognitive maps in modeling supervisory control systems. J Intell Fuzzy Syst 8(2):83–98 Google Scholar
  16. 16.
    Kosko B (1986) Fuzzy cognitive maps. Int J Man-Mach Stud 24:65–75 MATHCrossRefGoogle Scholar
  17. 17.
    Axelrod R (1976) Structure of decision: the cognitive maps of political elites. Princeton University Press, Princeton Google Scholar
  18. 18.
    Sutton R, Barto A (1998) Reinforcement learning: an introduction. MIT Press, Cambridge Google Scholar
  19. 19.
    Min HQ, Hui JX, Lu Y-S, Jiang J (2006) Probability fuzzy cognitive map for decision-making in soccer robotics. In: Proc of the IEEE/WIC/ACM int conf on intelligent agent technology Google Scholar
  20. 20.
    Yeap WK, Wong CK, Schmidt J (2006) Initial experiments with a mobile robot on cognitive mapping. In: Proc of the int symp on practical cognitive agents and robots Google Scholar
  21. 21.
    Pipe AG (2000) An architecture for building “potential field” cognitive maps in mobile robot navigation. Adapt Behav 8(2):173–203 CrossRefGoogle Scholar
  22. 22.
    Gaskett C, Fletcher L, Zelinsky A (2000) Reinforcement learning for a vision based mobile robot. In: Proc of the IEEE/RSJ int conference on intelligent robots and systems Google Scholar
  23. 23.
    Smart WD, Kaelbling LP (2001) Reinforcement learning for robot control. In: Mobile robots XVI proc. SPIE, vol 4573 Google Scholar
  24. 24.
    Zhu W, Levinson S (2001) Vision-based reinforcement learning for robot navigation. In: Proc int joint conference on neural networks Google Scholar
  25. 25.
    Yen G, Hickey T (2002) Reinforcement learning algorithms for robotic navigation in dynamic environments. In: Int joint conference on neural networks, vol 2, pp 1444–1449 Google Scholar
  26. 26.
    Tunstel E, Jamshidi M (1994) Fuzzy logic and behavior control strategy for autonomous mobile robot mapping. In: Proc of IEEE int conference on fuzzy systems Google Scholar
  27. 27.
    Bhanu B, Leang P, Cowden C, Yin L, Patterson M (2001) Real-time robot learning. In: IEEE international conference on robotics and automation, pp 491–498 Google Scholar
  28. 28.
    Macek K, Petrovic I, Peric N (2002) A reinforcement learning approach to obstacle avoidance of mobile robots. In: Int workshop on advanced motion control, pp 462–466 Google Scholar
  29. 29.
    Li A, Li Z, Chen J (2011) Microassembly path planning using reinforcement learning for improving positioning accuracy of a 1 cm3 omni-directional mobile microrobot. Appl Intell 34(2):211–225 CrossRefGoogle Scholar
  30. 30.
    Miao Y, Miao C, Tao X, Shen Z, Liu Z (2010) Transformation of cognitive maps. IEEE Trans Fuzzy Syst 18:114–124 CrossRefGoogle Scholar
  31. 31.
    Carvalho JP, Tomé JA (2000) Rule based fuzzy cognitive maps—qualitative systems dynamics. In: Proc 19th int conf North Amer fuzzy inf process soc, pp 407–411 Google Scholar
  32. 32.
    Stylios DC, Georgopoulos VC, Malandraki GA, Chouliara S (2008) Fuzzy cognitive map architectures for medical decision support systems. Appl Soft Comput 8(3):1243–1251 CrossRefGoogle Scholar
  33. 33.
    Passino K, Yurkovich S (1998) Fuzzy control. Addison-Wesley, Reading Google Scholar
  34. 34.
    Gomide F, Pedrycz W (1998) An introduction to fuzzy sets: analysis and design. MIT Press, Cambridge MATHGoogle Scholar
  35. 35.
    http://www.drrobit.com. Accessed November 11, 2010

Copyright information

© Springer Science+Business Media, LLC 2011

Authors and Affiliations

  • Márcio Mendonça
    • 1
  • Lúcia Valéria Ramos de Arruda
    • 1
  • Flávio NevesJr.
    • 1
  1. 1.Graduate School in Electrical Engineering and Applied Computer SciencesFederal University of Technology—ParanáCuritibaBrazil

Personalised recommendations