Fuzzy logic-based formulation of the organizer of intelligent robotic systems

  • H. M. Stellakis
  • K. P. Valavanis


A fuzzy logic-based methodology is proposed to model the organization level of an intelligent robotic system. The user input commands to the system organizer are linguistic in nature and the primitive events-tasks from the task domain of the system are, in general, interpreted via fuzzy sets. Fuzzy relations are introduced to connect every event with a specific user input command. Approximate reasoning is accomplished via a modifier and the compositional rule of inference, whereas the application of the conjunction rule generates those fuzzy sets with elements all possible (crisp) plans. Themost possible plan among all those generated, that is optimal under an application dependent criterion, is chosen and communicated to the coordination level. Off-line feedback information from the lower levels is considered asa-priori known and is used to update all organization level information. An example demonstrates the applicability of the proposed algorithm to intelligent robotic systems.

Key words

Fuzzy logic linguistic variable fuzzy events binary fuzzy relations approximate reasoning possibilistic analysis 


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  1. 1.
    De Mello, L.H. and Sanderson, A.C., AND/OR representation of assembly plans,Proc. AIII-86, Philadelphia, PA., August, 1986, pp. 1113–1119.Google Scholar
  2. 2.
    Fu, K. S., Learning control systems — Review and outlook,IEEE Trans. Automat. Control AC- 15, 210–221 (1970).Google Scholar
  3. 3.
    Fu, K.S., Learning control systems and intelligent control systems. An intersection of artificial intelligence and automatic control,IEEE Trans. Automat. Control AC- 15, 70–72 (1971).Google Scholar
  4. 4.
    Saridis, G.N., Toward the realization of intelligent controls,Proc. IEEE 67, 1115–1133 (1979).Google Scholar
  5. 5.
    Saridis, G.N.,Self-Organizing Control of Stochastic Systems, Marcel Dekker, New York, 1977.Google Scholar
  6. 6.
    Waltz, M.D. and Fu, K.S., A heuristic approach to reinforcement learning control systems,IEEE Trans. Automat. Control AC- 10, 390–398 (1965).Google Scholar
  7. 7.
    Nikolic, Z.J. and Fu, K.S., An algorithm for learning without external supervision and its application to learning control systems,IEEE Trans. Automat. Control AC- 11, 414–422 (1966).Google Scholar
  8. 8.
    McLaren, R.W., A stochastic automata model for the synthesis of learning systems,IEEE Trans. SSC. SSC- 2, 109–114 (1966).Google Scholar
  9. 9.
    Narenda, K.S. and Thathachar, M.L., Learning automata — A survey,IEEE Trans. Systems Man Cybernet,SMC- 4, 323–334 (1974).Google Scholar
  10. 10.
    Graham, J.H. and Saridis, G.N.,Linguistic Methods for Hierarchically Intelligent Control, TR-EE 80–34, October 1980. Purdue University, West Lafayette, Indiana.Google Scholar
  11. 11.
    Tsetlin, M.L., On the behavior of finite automata in a random media,Automation and Remote Control 22, No. 10 (1961).Google Scholar
  12. 12.
    McCurthy, G.J. and Fu, K.S., A variable structure automaton used as a multimodal searching technique,IEEE Trans. Automat. Control AC- 11, 379–387 (1966).Google Scholar
  13. 13.
    Saridis, G.N., Intelligent robotic control,IEEE Trans. Automat. Control 547–557 (1983).Google Scholar
  14. 14.
    Valavanis, K.P.,A mathematical formulation for the analytical design of intelligent machines, PhD Dissertation, Rensselaer Polytechnic Institute, Troy, N.Y., 1986.Google Scholar
  15. 15.
    Narenda, K.S. and Viswanathan, R., A two-level system of stochastic automata for periodic random environments,IEEE Trans. Systems Man Cybernet SMC- 2, 285–289 (1972).Google Scholar
  16. 16.
    Saridis, G.N. and Stefanou, H.E., A hierarchically intelligent control for a bionic arm,Proc. 1975 Conf. Decision and Control, Houston, TX.Google Scholar
  17. 17.
    Saridis, G.N., A hierarchical approach to the control of a prosthetic arm,IEEE Trans. Systems Man Cybernet SMC- 7, 407–420 (1972).Google Scholar
  18. 18.
    Mesarovic, M.D., Macko, K., and Takahara, Y.,Theory of Multi-Level Systems, Academic Press, New York, 1970.Google Scholar
  19. 19.
    Conant, R.C., Laws of information which govern systems,IEEE Trans. Systems Man Cybernet,SMC-6, No. 4 (1976).Google Scholar
  20. 20.
    Haves-Roth et al.,Building Expert Systems, Addison-Wesley, Reading, Mass., 1982.Google Scholar
  21. 21.
    IEEE Computer Society Press,1987 Third Conf. Artificial Intelligence Applications, IEEE Computer Society Press, Washington, D.C., 1987.Google Scholar
  22. 22.
    Broekstra, G., On the representation and identification of structure systems,Internat. J. General Systems 9 1271–1293 (1978).Google Scholar
  23. 23.
    Conant, R.C., Information flows in hierarchical systems,IEEE Trans. Systems Man Cybernet SMC-6, No. 4 (1976).Google Scholar
  24. 24.
    Saridis, G.N. and Valavanis, K.P., Analytical design of intelligent machines,Automatica, 1988.Google Scholar
  25. 25.
    Boettcher, K.L., An information theoretic model of the decision maker, MS Thesis, LIDS-TH-1096, MIT, Cambridge, Mass, June 1981.Google Scholar
  26. 26.
    Hall, S.A., Information theoretic models of storage and memory, LIDS-TH-1232, MIT, Cambridge, MA, August 1982.Google Scholar
  27. 27.
    Feigenbaum, E.A. and Feldam, J.,Computers and Thought, McGraw-Hill, San Fransisco, 1963.Google Scholar
  28. 28.
    Meystel, A., ‘Intelligent control in robotics,J. Robotic Systems 5, (1988).Google Scholar
  29. 29.
    Meystel, A., Intelligent motion control in anthropomorphic machines, in S. Andriole, (ed.),Applied Artificial Intelligence, Pentrocellis Books, Princeton, NJ, 1985.Google Scholar
  30. 30.
    Valavanis, K.P. and Saridis, G.N., Information theoretic modeling of intelligent robotic systems,IEEE Trans. Systems Man Cybernet Nov/Dec 1988.Google Scholar
  31. 31.
    Antsaklis, P.J., Passino, K.M., and Wang, S.J., Towards intelligent autonomous control systems, architecture and fundamental issues.J. Intelligent Robotic Systems 1, 315–342 (1989).Google Scholar
  32. 32.
    Valavanis, K.P. and Yuan, P.H., Hardware and software for intelligent robotic systems,J. Intelligent Robotic Systems 1, 343–373 (1989).Google Scholar
  33. 33.
    Yuan, P.H., Design of an intelligent robotic system organizer via expert systems techniques, MS Thesis, Northeastern University, Boston, 1988.Google Scholar
  34. 34.
    Saridis, G.N., Analytical formulation of the principle of increasing precision with decreasing intelligence for intelligent machines,Automatica 1989.Google Scholar
  35. 35.
    Valavanis, K.P. and Carelo, S.J., An efficient planning technique for robotic assemblies and intelligent robotic systems,Intelligent Robotic Systems 3, 321–347 (1990).Google Scholar
  36. 36.
    Zimmermann, H.J.,Fuzzy Set Theory and its Applications, Kluwer-Nijhoff, Dordrecht, 1985.Google Scholar
  37. 37.
    Kandel, A.,Fuzzy Mathematical Techniques with Applications, Addison-Wesley, Reading, Mass., 1986.Google Scholar
  38. 38.
    Yager, Ovchinnicov and Tong, Nguyen,Fuzzy Sets and Applications Selected papers by L. A. Zadeh, Willey-Interscience, New York, 1987.Google Scholar
  39. 39.
    Zimmermann, H.J.,Fuzzy Sets, Decision Making, and Expert Systems, Addison-Wesley, Reading, Mass. 1985.Google Scholar
  40. 40.
    Kanal, L.N. and Lemmer, J.F.,Uncertainty in Artificial Intelligence, Elsevier, Amsterdam, 1986.Google Scholar
  41. 41.
    Zadeh, L.A., Fuzzy sets as a basis for a theory of possibility,Fuzzy Sets and Systems,1, 3–28 (1978).Google Scholar
  42. 42.
    Stellakis, H.M., Fuzzy logic based modeling of the organization level of intelligent robotic systems, MSc Thesis, Northeastern University, 1989.Google Scholar

Copyright information

© Kluwer Academic Publishers 1991

Authors and Affiliations

  • H. M. Stellakis
    • 1
  • K. P. Valavanis
    • 1
  1. 1.Robotics Laboratory, Department of Electrical and Computer EngineeringNortheastern UniversityBostonUSA

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