Journal of Intelligent and Robotic Systems

, Volume 3, Issue 4, pp 321–347 | Cite as

An efficient planning technique for robotic assemblies and intelligent robotic systems

  • Kimon P. Valavanis
  • Socrates J. Carelo


An efficient planning algorithm for the organization and formulation of complete plans applicable to both robotic assemblies and intelligent robotic systems is proposed. The constraint of task precedence and the concepts of the criticality of tasks-events and valid repetitive orderings are introduced to facilitate and optimize the formulation of every complete plan capable of executing a user-requested job. Two examples demonstrate the applicability of the proposed algorithm to both robotic assemblies and intelligent robotic systems.

Key words

Organization level robotic assemblies and systems task precedence criticality of tasks-events valid repetive orderings automated planning 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Fox, B.R. and Kempf, K.G., Opportunistic scheduling for robotic assembly, 1985 IEEE International Conference on Robotics and Automation, pp. 880–889, IEEE Computer Society (1985).Google Scholar
  2. 2.
    Fikes, R.E., et al., Learning and executing generalized robot plans, Artificial Intelligence 3, 251–288 (1972).Google Scholar
  3. 3.
    Sacerdoti, E.D., A Structure of Plans and Behavior, Elsevier North-Holland, Amsterdam (1977).Google Scholar
  4. 4.
    De Mello, L.H. and Sanderson, A.C., AND/OR representation of assembly plans, Proceedings of the AIII-86, pp. 1113–1119, Philadelphia, Pa. (1986).Google Scholar
  5. 5.
    Fu, K.S., Learning control systems — Review and outlook, IEEE Trans. on AC AC-15, April 1970, pp. 210–221.Google Scholar
  6. 6.
    Fu, K.S., Learning control systems and intelligent control systems: an intersection of artificial intelligence and automatic control, IEEE Trans. on AC AC-15, February 1971, pp. 70–72.Google Scholar
  7. 7.
    Saridis, G.N., Toward and realization of intelligent controls, Proc. of the IEEE 67, No. 8, August 1979, pp. 1115–1133.Google Scholar
  8. 8.
    Saridis, G.N., Self-Organizing Control of Stochastic Systems, Marcel Dekker, N.Y. (1977).Google Scholar
  9. 9.
    Waltz, M.D. and Fu, K.S., A heuristic approach to reinforcement learning control systems, IEEE Trans. on AC AC-10, No. 4, October 1965, pp. 390–398.Google Scholar
  10. 10.
    Nikolic, Z.J. and Fu, K.S., An algorithm for learning without external supervision and its application to learning control systems, IEEE Trans. on AC AC-11, No. 3, July 1966, pp. 414–422.Google Scholar
  11. 11.
    McLaren, R.W., A stochastic automata model for the synthesis of learning systems, IEEE Trans. on SSC SSC-2, No. 2, December 1966, pp. 109–114.Google Scholar
  12. 12.
    Narendra, K.S. and Thathachar, M.L., Learning automata — A survey, IEEE Trans. on SMC SMC-4, July 1974, pp. 323–334.Google Scholar
  13. 13.
    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
  14. 14.
    Tsetlin, M.L., On the behavior of finite automata in a random media, Automation and Remote Control 22, No. 10, October 1961.Google Scholar
  15. 15.
    McMurthy, G.J. and Fu, K.S., A variable structure automata used as a multimodal searching technique, IEEE Trans. on AC AC-11. No. 3, July 1966, pp. 379–387.Google Scholar
  16. 16.
    Saridis, G.N., Intelligent robotic control, IEEE Trans. on AC AC-28, No. 5, May 1983, pp. 546–557.Google Scholar
  17. 17.
    Smith, R.E. and Gini, M., Robot tracking and control issues in an intelligent error recovery system, 1986 Int. Conf. on Robotics and Automation, Vol. 2, April 1986, SF, CA.Google Scholar
  18. 18.
    Valavanis, K.P., A mathematical formulation for the design of intelligent machines, PhD dissertation, Rensselaer Polytechnic Institute, Troy, NY (1986).Google Scholar
  19. 19.
    Fu, K.S., Syntactic Pattern Recognition, Academic Press, New York (1974).Google Scholar
  20. 20.
    Narendra, K.S. and Viswanathan, R., A two-level system of stochastic automata for periodic random environments, IEEE Trans. on SMC SMC-2, April 1972, pp. 285–289.Google Scholar
  21. 21.
    Sardis, G.N. and Stephanou, H.E., A hierarchically intelligent control for a bionic arm, Proc. of 1975 Conf. on Decisions and Control, December 1975, Houston, TX.Google Scholar
  22. 22.
    Saridis, G.N., A hierarchical approach to the control of a prostethic arm, IEEE Trans. on SMC SMC-7, No. 6, June, 1972, pp. 407–420.Google Scholar
  23. 23.
    Mesarovic, M.D., Macko, K., and Takahara, Y., Theory of Multi-Level Systems, Academic Press, New York (1970).Google Scholar
  24. 24.
    Conant, R.C., Laws of information which govern systems, IEEE Trans. on SMC SMC-6, No. 4, April 1976.Google Scholar
  25. 25.
    Duda and Hart, Pattern Classification and Scene Analysis, John Wiley, New York.Google Scholar
  26. 26.
    Hayes-Roth et al., Building Expert Systems, Addison-Wesley, Reading, MA (1982).Google Scholar
  27. 27.
    IEEE Computer Society Press, 1987 Third Conference on Artificial Intelligence Applications, IEEE Computer Society Press, Washington, DC (1987).Google Scholar
  28. 28.
    Broekstra, G., On the representation and identification of structure systems, IJGS 9, 1271–1293 (1978).Google Scholar
  29. 29.
    Conant, R.C., Information flows in hicrarchical systems, IEEE Trans. on SMC SMC-6, No. 4, April 1976.Google Scholar
  30. 30.
    Saridis, G.N. and Valavanis, K.P., Analytical design of intelligent machines, Automatica (1988).Google Scholar
  31. 31.
    Boettcher, K.L., An information theoretic model of the decision maker, MS thesis, LIDS-TH-1096, MIT, Cambridge, MA, June 1981.Google Scholar
  32. 32.
    Hall, S.A., Information theoretic models of storage and memory, LIDS-TH-1232, MIT, Cambridge, MA, August 1982.Google Scholar
  33. 33.
    Feigenbaum, E.A. and Feldman, J., Computers and Thought, McGraw-Hill, San Francisco (1963).Google Scholar
  34. 34.
    Meystel, A., Intelligent control in robotics, J. Robotic Systems 5, 4 (1988).Google Scholar
  35. 35.
    Meystel, A., Intelligent motion control in anthropomorphic machines, Applied Artificial Intelligence, S. Andriole (ed.), Pentrocellis Books, Priceton (1985).Google Scholar
  36. 36.
    Valavanis, K.P. and Saridis, G.N., Information theoretic modeling of intelligent robotic systems, IEEE Trans. on SMC, Nov/Dec 1988.Google Scholar
  37. 37.
    Antsaklis, P.J., Passino, K.M., and Wang, S.J., Towards intelligent autonomous control systems; architecture and fundamental issues, to appear.Google Scholar
  38. 38.
    Valavanis, K.P. and Yuan, P.H., Hardware and software for intelligent robotic systems, J. Intelligent and Robotic Systems 1, 343–373 (1989).Google Scholar
  39. 39.
    Yuan, P.H., Design of an intelligent robotic system organizer via expert system techniques, MS thesis, Northeastern University, Boston, MA (1988).Google Scholar
  40. 40.
    Saridis, G.N., Analytical formulation of the principle of increasing precision with decreasing intelligence for intelligent machines, to appear.Google Scholar
  41. 41.
    Carelo, S.J., An efficient planning technique for robotic systems, MS thesis, Northeastern University, November 1987.Google Scholar

Copyright information

© Kluwer Academic Publishers 1990

Authors and Affiliations

  • Kimon P. Valavanis
    • 1
  • Socrates J. Carelo
    • 1
  1. 1.Robotics Laboratory, Department of Electrical and Computer EngineeringNortheastern UniversityBostonUSA

Personalised recommendations