Advertisement

Use and Perspectives of Fuzzy Cognitive Maps in Robotics

  • Ján VaščákEmail author
  • Napoleon H. Reyes
Chapter
Part of the Intelligent Systems Reference Library book series (ISRL, volume 54)

Abstract

Fuzzy Cognitive Maps (FCM) started in the last decade to penetrate to areas as decision-making and control systems including robotics, which is characterized by its distributiveness, need for parallelism and heterogeneity of used means. This chapter deals with specification of needs for a robot control system and divides defined tasks into three basic decision levels dependent on their specification of use as well as applied means. Concretely, examples of several FCMs applications from the low and middle decision levels are described, mainly in the area of navigation, movement stabilization, action selection and path cost evaluation. Finally, some outlooks for future development of FCMs are outlined.

Keywords

Membership Function Path Planning Soccer Player Legged Robot High Decision Level 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgments

Research supported by the National Research and Development Project Grant 1/0667/12 “Incremental Learning Methods for Intelligent Systems” 2012–2015 and by the “Center of Competence of knowledge technologies for product system innovation in industry and service” with ITMS project number: 26220220155 for years 20012–2015.

Supplementary material

Supplementary material 1 (AVI 25,594 KB)

Supplementary material 1 (AVI 28,004 KB)

Supplementary material 1 (AVI 28,863 KB)

Supplementary material 1 (AVI 11,397 KB)

304354_1_En_15_MOESM5_ESM.pdf (227 kb)
Supplementary material 1 (PDF 227 KB)

References

  1. 1.
    Beeson, P., Modayil, J., Kuipers, B.: Factoring the mapping problem: mobile robot map-building in the hybrid spatial semantic hierarchy. Int. J. Robot. Res. 29(4), 428–459 (2010)CrossRefGoogle Scholar
  2. 2.
    Blažič, S., Škrjanc, I., Matko, D.: Globally stable direct fuzzy model reference adaptive control. Fuzzy Sets Syst. 139(1), 3–33 (2003)CrossRefzbMATHGoogle Scholar
  3. 3.
    Golmohammadi, S.K., Azadeh, A., Gharehgozli, A.: Action selection in robots based on learning fuzzy cognitive map, pp. 731–736. In: Proceeding of IEEE International Conference on Industrial Informatics, Singapore (2006)Google Scholar
  4. 4.
    Kannappan, A., Tamilarasi, A., Papageorgiou, E.: Analyzing the performance of fuzzy cognitive maps with non-linear hebbian learning algorithm in predicting autistic disorder. Expert Syst. Appl. 38(3), 1282–1292 (2011)CrossRefGoogle Scholar
  5. 5.
    Kosko, B.: Fuzzy cognitive maps. Int. J. Man Mach. Stud. 24(1), 65–75 (1986)CrossRefzbMATHGoogle Scholar
  6. 6.
    LaValle, S.M.: Planning Algorithms. Cambridge University Press, Cambridge. http://planning.cs.uiuc.edu/ (2006)
  7. 7.
    Matarić, M.J.: Learning in behavior-based multi-robot systems: policies, models, and other agents. Cogn. Syst. Res. 2(1), 81–93 (2001)CrossRefGoogle Scholar
  8. 8.
    Medgyes, K., Johanyák, Z.C.: Survey on routing algorithms. In: Proceeding of 3rd International Scientific and Expert Conference (TEAM 2011), Trnava, Slovakia, pp. 312–315 (2012)Google Scholar
  9. 9.
    Mendonça, M., de Arruda, L., Neves, F.: Autonomous navigation system using event driven-fuzzy cognitive maps. Appl. Intel. 37, 175–188 (2012)CrossRefGoogle Scholar
  10. 10.
    Motlagh, O.: An FCM-based design for balancing of legged robots. J. Artif.Intel. 4(4), 295–299 (2011)CrossRefGoogle Scholar
  11. 11.
    Motlagh, O., Tang, S.H., Ismail, N., Ramli, A.R.: An expert fuzzy cognitive map for reactive navigation of mobile robots. Fuzzy Sets Syst. 201, 105–121 (2012)MathSciNetCrossRefGoogle Scholar
  12. 12.
    Papageorgiou, E.: Learning algorithms for fuzzy cognitive maps:a review study. Syst. Man Cybern. Part C Appl. Rev. IEEE Trans. 42(2), 150–163 (2012)CrossRefGoogle Scholar
  13. 13.
    Papageorgiou, E.I., Froelich, W.: Multi-step prediction of pulmonary infection with the use of evolutionary fuzzy cognitive maps. Neurocomputing 92, 28–35 (2012)CrossRefGoogle Scholar
  14. 14.
    Papageorgiou, E.I., Iakovidis, D.K.: Intuitionistic fuzzy cognitive maps. IEEE Trans. Fuzzy Syst. 21(2), 342–354 (2013)Google Scholar
  15. 15.
    Papageorgiou, E.I., Kannappan, A.: Fuzzy cognitive map ensemble learning paradigm to solve classification problems: application to autism identification. Appl. Soft Comput. 12(12), 3798–3809 (2012)CrossRefGoogle Scholar
  16. 16.
    Papageorgiou, E.I., Salmeron, J.L.: Learning fuzzy grey cognitive maps using nonlinear hebbian-based approach. Int. J. Approx. Reason. 53(1), 54–65 (2012)MathSciNetCrossRefzbMATHGoogle Scholar
  17. 17.
    Papageorgiou, E.I., Salmeron, J.L.: A review of fuzzy cognitive maps research during the last decade. IEEE Trans. Fuzzy Syst. 21(1), 66–79 (2013)Google Scholar
  18. 18.
    Parenthoën, M., Reignier, P., Tisseau, J.: Put fuzzy cognitive maps to work in virtual worlds. In: Proceeding of the 10th IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), vol. 1, pp. 252–255, Melbourne, Australia (2001)Google Scholar
  19. 19.
    Pozna, C., Troester, F., Precup, R.E., Tar, J.K., Preitl, S.: On the design of an obstacle avoiding trajectory: method and simulation. Math. Comput. Simul. 79(7), 2211–2226 (2009)MathSciNetCrossRefzbMATHGoogle Scholar
  20. 20.
    Precup, R.E., Hellendoorn, H.: A survey on industrial applications of fuzzy control. Comput. Ind. 62(3), 213–226 (2011)CrossRefGoogle Scholar
  21. 21.
    Stach, W., Kurgan, L., Pedrycz, W., Reformat, M.: Genetic learning of fuzzy cognitive maps. Fuzzy Sets Syst. 153(3), 371–401 (2005)Google Scholar
  22. 22.
    Vaščák, J.: Fuzzy cognitive maps in path planning. Acta Tech. Jaurinensis 1(3), 467–479 (2008)Google Scholar
  23. 23.
    Vaščák, J.: Decision-making systems in mobile robotics. In: Mls, K. (ed.) Autonomous Decision Systems Handbook, pp. 56–88. BEN, Prague (2011)Google Scholar
  24. 24.
    Vaščák, J., Hirota, K.: Integrated decision-making system for robot soccer. J. Adv. Comput. Intel. Intel. Inf. 15(2), 156–163 (2011)Google Scholar
  25. 25.
    Vaščák, J., Madarász, L.: Adaptation of fuzzy cognitive maps—a comparison study. Acta Polytech. Hung. 7(3), 109–122 (2010)Google Scholar
  26. 26.
    Vaščák, J., Paľa ,M.: Adaptation of fuzzy cognitive maps for navigation purposes by migration algorithms. Int. J. Artif. Intel. 8(S12), 20–37 (2012)Google Scholar
  27. 27.
    Zelinka, I.: Artificial Intelligence in Problems of Global Optimization. BEN, Prague (2002)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  1. 1.Center for Intelligent TechnologiesTechnical University of KošiceKošiceSlovakia
  2. 2.Institute of Natural and Mathematical SciencesMassey UniversityAucklandNew Zealand

Personalised recommendations