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Journal of Intelligent and Robotic Systems

, Volume 19, Issue 3, pp 339–356 | Cite as

Neurofuzzy Motion Planners for Intelligent Robots

  • L. H. Tsoukalas
  • E. N. Houstis
  • G. V. Jones
Article

Abstract

A neurofuzzy methodology is presented for motion planning in semi-autonomous mobile robots. The robotic automata considered are devices whose main feature is incremental learning from a human instructor. Fuzzy descriptions are used for the robot to acquire a repertoire of behaviors from an instructor which it may subsequently refine and recall using neural adaptive techniques. The robot is endowed with sensors providing local environmental input and a neurofuzzy internal state processing predictable aspects of its environment. Although it has no prior knowledge of the presence or the position of any obstructing objects, its motion planner allows it to make decisions in an unknown terrain. The methodology is demonstrated through a robot learning to travel from some start point to some target point without colliding with obstacles present in its path. The skills acquired are similar to those possessed by an automobile driver. The methodology has been successfully tested with a simulated robot performing a variety of navigation tasks.

intelligent robots instructible robots anticipatory systems motion planners neurofuzzy control collision avoidance 

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References

  1. 1.
    Ahrikencheikh, C. and Seireg, A.: Optimized-Motion Planning, John Wiley and Sons, New York, 1994.Google Scholar
  2. 2.
    Bourbakis, N. G.: Artificial Intelligence Methods and Applications, World Scientific, Singapore, 1992.Google Scholar
  3. 3.
    Bourbakis, N. G.: Design of an autonomous navigation system, in: IEEE Control Systems Magazine, 1988, pp. 25–28.Google Scholar
  4. 4.
    Bourbakis, N. G.: Knowledge extraction and acquisition during real-time navigation in unknown environments, Int. J. Pattern Recogn. Art. Intel. 9(1) (1995), 83–99.Google Scholar
  5. 5.
    Cameron, S. and Probert, P.: Advanced Guided Vehicles, Aspects of the Oxford AGV Project, World Scientific, Singapore, 1994.Google Scholar
  6. 6.
    Crangle, C. and Suppes, P.: Language and Learning for Robots, CLSI Publications, Stanford, CA, 1994.Google Scholar
  7. 7.
    Gat, E.: On the role of stored internal state in the control of autonomous mobile robots, AI Magazine(1993), 64–73.Google Scholar
  8. 8.
    Gupta, M. M. and Qi, J.: Theory of T-normsand fuzzy inference methods, Fuzzy Sets and Systems 40(1991), 431–450.Google Scholar
  9. 9.
    Hwang, Y. K. and Ahuja, N.: A potential field approach to path planning, IEEE Trans. Robotics Automat. 8(1) (1992), 23–32.Google Scholar
  10. 10.
    Hwang Y. K. and Ahuja, N.: Gross motion planning–a survey, ACM Computing Surveys 24(3) (1992).Google Scholar
  11. 11.
    Khatib, O.: Real-time obstacle avoidance for manipulators and mobile robots, Int. J. Robotics Res. 5(1) (1986), 90–98.Google Scholar
  12. 12.
    Kwan, H. K. and Cai, Y.: Fuzzy neural network and its application to pattern recognition, IEEE Trans. Fuzzy Systems 2(3) (1994), 185–193.Google Scholar
  13. 13.
    Latombe, J-C.: Robot Motion Planning, Kluwer Academic Publishers, Boston, 1991.Google Scholar
  14. 14.
    Mikio Maeda, Yasushi Maeda and Shuta Murakami: Fuzzy drive control of an autonomous mobile robot, Fuzzy Sets and Systems(1991), 195–204.Google Scholar
  15. 15.
    Rao, N. S. V.: Algorithmic framework for learned robot navigation in unknown terrain’s, IEEE Computer(1989), 37–43.Google Scholar
  16. 16.
    Sheu, P. C-Y. and Xue, Q.: Intelligent Robotic Planning Systems, World Scientific, Singapore, 1993.Google Scholar
  17. 17.
    Tuscillo, A. and Bourbakis, N. G.: A neural and fuzzy control of a robotic hand, IEEE Transactions on SMC(1996).Google Scholar
  18. 18.
    Terano, T., Asai, K., and Sugeno, M.: Fuzzy Systems Theory and its Applications, Academic Press, Boston, 1992.Google Scholar
  19. 19.
    Tsoukalas, L. H. and Uhrig, R. E.: Fuzzy and Neural Approaches in Engineering, John Wiley and Sons, New York, 1997.Google Scholar
  20. 20.
    Tzafestas, S.: Introduction to intelligent robotic systems, in: S. Tzafestas (ed.), Intelligent Robotic Systems, Marcel Dekker, New York, 1991.Google Scholar
  21. 21.
    Zadeh, L. A.: A computational approach to fuzzy quantifiers in natural languages, Comp. Math. 9(1983), 149–184.Google Scholar
  22. 22.
    Zadeh, L. A.: Fuzzy sets as a basis for theory of possibility, Fuzzy Sets and Systems 1(1978), 3–28.Google Scholar
  23. 23.
    Zadeh, L. A.: Fuzzy sets, Inform. and Control 8(1965), 338–353.Google Scholar

Copyright information

© Kluwer Academic Publishers 1997

Authors and Affiliations

  • L. H. Tsoukalas
    • 1
  • E. N. Houstis
    • 1
  • G. V. Jones
    • 2
  1. 1.Purdue UniversityW. LafayetteU.S.A.
  2. 2.The University of TennesseeKnoxvilleU.S.A

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