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Fuzzy Logic in Autonomous Navigation

  • Alessandro Saffiotti
Part of the Studies in Fuzziness and Soft Computing book series (STUDFUZZ, volume 61)

Abstract

The goal of autonomous mobile robotics is to build physical systems that can move purposefully and without human intervention in unmodified environments — that is, in real-world environments that have not been specifically engineered for the robot. The development of techniques for autonomous navigation constitutes one of the major trends in the current research on robotics. This trend is motivated by the current gap between the available technology and the new application demands. On the one hand, the techniques employed in current industrial robots lack the ability to provide flexibility and autonomy: typically, industrial robots perform pre-programmed sequences of operations in highly constrained environments, and are not able to operate in new environments or to face unexpected situations. On the other hand, there is a clear emerging market for truly autonomous robots. Possible applications include intelligent service robots for offices, hospitals, and factory floors; maintenance robots operating in hazardous or inaccessible areas; domestic robots for cleaning or entertainment; autonomous and semi-autonomous vehicles for help to the disabled and the elderly; and so on.

Keywords

Fuzzy Logic Mobile Robot Fuzzy Rule Fuzzy Control Fuzzy Controller 
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.

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References

  1. 1.
    R. C. Arkin. Behavior-Based Robotics. MIT Press, Cambridge, MA, 1998.Google Scholar
  2. 2.
    J. W. Baxter and J. R. Bumby. Fuzzy logic guidance and obstacle avoidance algorithms for autonomous vehicle control. In Proc. of the Int. Workshop on Intelligent Autonomous Vehicles, pages 41–52, Southampton, UK, 1993.Google Scholar
  3. 3.
    H. Berenji, Y-Y. Chen, C-C. Lee, J-S. Jang, and S. Murugesan. A hierarchical approach to designing approximate reasoning-based controllers for dynamic physical systems. In Proc. of the Conf. on Uncertainty in Artif. Intell., pages 362–369, Cambridge, MA, 1990.Google Scholar
  4. 4.
    H. R. Berenji. The unique strength of fuzzy logic control. IEEE Expert, page 9, August 1994. Response to Elkan’s “The paradoxical success of fuzzy logic” , same issue.Google Scholar
  5. 5.
    T. Bilgiç and I. B. Türkşen. Measurement of membership functions: theoretical and empirical work. In H.J. Zimmermann, editor, Practical Applications of Fuzzy Technologies, volume 6 of Handbooks of Fuzzy Sets. Kluwer Academic, Norwell, MA, 1999.Google Scholar
  6. 6.
    R. A. Brooks. A robust layered control system for a mobile robot. IEEE Journal of Robotics and Automation, RA-2(1):14–23, 1986.CrossRefGoogle Scholar
  7. 7.
    M. Drummond. Situated control rules. In Proc. of the Int. Conf. on Knowledge Representation and Reasoning, pages 103–113, 1989.Google Scholar
  8. 8.
    E. Fabrizi, G. Oriolo, and G. Ulivi. Accurate map building via fusion of laser and ultrasonic range measures. In D. Driankov and A. Saffiotti, eds, Fuzzy Logic Techniques for Autonomous Vehicle Navigation, Physica-Verlag, Heidelberg, New York, 2000, pages 257–280.Google Scholar
  9. 9.
    J. Gasós. Integrating linguistic descriptions and sensor observations for the navigation of autonomous robots. In D. Driankov and A. Saffiotti, eds, Fuzzy Logic Techniques for Autonomous Vehicle Navigation, Physica-Verlag, Heidelberg, New York, 2000, pages 313–340.Google Scholar
  10. 10.
    E. Gat. Three-layer architectures. In R.P. Bonasso D. Kortenkamp and R. Murphy, editors, Artificial intelligence and mobile robots, chapter 8, pages 195–210. MIT Press, Cambridge, MA, 1998.Google Scholar
  11. 11.
    R. Ghanea-Hercock and D. P. Barnes. An evolved fuzzy reactive control system for co-operating autonomous robots. In Proc. of the Int. Conf. on Simulation and Adaptive Behavior (SAB), Cap Cod, 1996.Google Scholar
  12. 12.
    J. Godjevac and N. Steele. Neuro fuzzy control for basic mobile robot behaviours. In D. Driankov and A. Saffiotti, eds, Fuzzy Logic Techniques for Autonomous Vehicle Navigation, Physica-Verlag, Heidelberg, New York, 2000, pages 98–118.Google Scholar
  13. 13.
    S. G. Goodridge and M. G. Kay. Multi-layered fuzzy behavior fusion for reactive control of autonomous robots. In D. Driankov and A. Saffiotti, eds, Fuzzy Logic Techniques for Autonomous Vehicle Navigation, Physica-Verlag, Heidelberg, New York, 2000, pages 179–204.Google Scholar
  14. 14.
    S. Hanks and R. J. Firby. Issues and architectures for planning and execution. In Workshop on Innovative APproaches to Planning, Scheduling and Control, San Diego, CA, 1990.Google Scholar
  15. 15.
    F. Hoffmann. The role of fuzzy logic control in evolutionary robotics. In D. Driankov and A. Saffiotti, eds, Fuzzy Logic Techniques for Autonomous Vehicle Navigation, Physica-Verlag, Heidelberg, New York, 2000, pages 119–148.Google Scholar
  16. 16.
    U. Höhle. A survey on the fundamentals of fuzzy set theory. In F. Kreith, editor, The CRC Handbook of Mechanical Engineering. CRC Press 1998Google Scholar
  17. 17.
    O. Khatib. Real-time obstacle avoidance for manipulators and mobile robots. The International Journal of Robotics Research, 5(1):90–98, 1986.MathSciNetCrossRefGoogle Scholar
  18. 18.
    H. Kiendl and J. J. Rüger. Stability analysis of fuzzy control systems using facets functions. Fuzzy Sets and Systems, 70:275–285, 1995.MathSciNetMATHCrossRefGoogle Scholar
  19. 19.
    K. Konolige, K.L. Myers, E.H. Ruspini, and A. Saffiotti. The Saphira architecture: A design for autonomy. Journal of Experimental and Theoretical Artificial Intelligence, 9(1):215–235, 1997.CrossRefGoogle Scholar
  20. 20.
    B. J. Kuipers. The spatial semantic hierarchy. Artificial Intelligence, 119:191–233 2000.MathSciNetMATHCrossRefGoogle Scholar
  21. 21.
    J. C. Latombe. Robot Motion Planning. Kluwer Academic Publishers, Boston, 1991.CrossRefGoogle Scholar
  22. 22.
    H. J. Levesque and R. J. Brachman. A fundamental tradeoff in knowledge representation and reasoning. In R. J. Brachman and H. J. Levesque, editors, Readings in Knowledge Representation, pages 41–70. Morgan Kaufmann, Los Altos, CA, 1985.Google Scholar
  23. 23.
    M. López-Sánchez, R. Lṕez de Màntaras, and C. Sierra. Map generation by cooperative autonomous robots using possibility theory. In D. Driankov and A. Saffiotti, eds, Fuzzy Logic Techniques for Autonomous Vehicle Navigation, Physica-Verlag, Heidelberg, New York, 2000, pages 281–312.Google Scholar
  24. 24.
    M. Maeda, Y. Maeda, and S. Murakami. Fuzzy drive control of an autonomous mobile robot. Fuzzy Sets and Systems. 39:195–204. 1991.CrossRefGoogle Scholar
  25. 25.
    F. Michaud. Selecting behaviors using fuzzy logic. In Proc. of the IEEE Int. Conf. on Fuzzy Systems, pages 585–592, Barcelona, SP, 1997.Google Scholar
  26. 26.
    R. R. Murphy. Fuzzy logic for fusion of tactical influences on vehicle speed control. In D. Driankov and A. Saffiotti, eds, Fuzzy Logic Techniques for Autonomous Vehicle Navigation, Physica-Verlag, Heidelberg, New York, 2000, pages 73–98.Google Scholar
  27. 27.
    A. Ollero, J. Ferruz, O. Sanchez, and G. Heredia. Mobile robot path tracking and visual target tracking using fuzzy logic. In D. Driankov and A. Saffiotti, eds, Fuzzy Logic Techniques for Autonomous Vehicle Navigation, Physica-Verlag, Heidelberg, New York. 2000. Dages 51–72.Google Scholar
  28. 28.
    J. Pan, D. J. Pack, A. Kosaka, and A. C. Kak. FUZZY-NAV: a vision-based robot navigation architecture using fuzzy inference for uncertainty reasoning. In Proc. of the World Congress on Neural Networks, pages 602–607, Washington, DC, 1995.Google Scholar
  29. 29.
    F. Pin and Y. Watanabe. Resolving conflict between behaviors using suppression and inhibition. In D. Driankov and A. Saffiotti, eds, Fuzzy Logic Techniques for Autonomous Vehicle Navigation, Physica-Verlag, Heidelberg, New York, 2000, pages 151–178.Google Scholar
  30. 30.
    P. Pirjanian and M. Mataric. Multiple objective vs. fuzzy behavior coordination. In D. Driankov and A. Safiiotti, eds, Fuzzy Logic Techniques for Autonomous Vehicle Navigation, Physica-Verlag, Heidelberg, New York, 2000, pages 235–254.Google Scholar
  31. 31.
    J. K. Rosenblatt and D. W. Payton. A fine-grained alternative to the subsumption architecture for mobile robot control. In Proc of the IEEE Int. Conf. on Neural Networks, volume 2, pages 317–324, Washington, DC, 1989. IEEE Press.Google Scholar
  32. 32.
    E. H. Ruspini. Fuzzy logic in the Flakey robot. In Proc. of the Int. Conf. on Fuzzy Logic and Neural Networks (IIZUKA ), pages 767–770, Iizuka, JP, 1990.Google Scholar
  33. 33.
    E. Ruspini. On the semantics of fuzzy logic. Int. J. of Approximate Reasoning, 5:45–88, 1991.MathSciNetMATHCrossRefGoogle Scholar
  34. 34.
    E. H. Ruspini. Truth as utility: A conceptual synthesis. In Proc. of the Conf. on Uncertainty in Artif. Intell., pages 316–322, Los Angeles, CA, 1991.Google Scholar
  35. 35.
    A. Saffiotti, K. Konolige, and E. H. Ruspini. A multivalued-logic approach to integrating planning and control. Artificial Intelligence, 76(1–2):481–526, 1995.CrossRefGoogle Scholar
  36. 36.
    A. Saffiotti, E. H. Ruspini, and K. Konolige. Blending reactivity and goaldirectedness in a fuzzy controller. In Proc. of the IEEE Int. Conf. on Fuzzy Systems, pages 134–139, San Francisco, California, 1993. IEEE Press.Google Scholar
  37. 37.
    A. Saffiotti and L. P. Wesley. Perception-based self-localization using fuzzy locations. In L. Dorst, M. van Lambalgen, and F. Voorbraak, editors, Reasoning with Uncertainty in Robotics, number 1093 in LNAI, pages 368–385. SpringerVerlag, Berlin, DE, 1996.Google Scholar
  38. 38.
    M Sugeno, M. F. Griffin, and A. Bastian. Fuzzy hierarchical control of an unmanned helicopter. In Proc. of Int. Fuzzy System Association Conference (IFSA), pages 179–182, Seoul, KR, 1993.Google Scholar
  39. 39.
    M. Sugeno and M. Nishida. Fuzzy control of model car. Fuzzy Sets and Systems, 16:103–113, 1985.CrossRefGoogle Scholar
  40. 40.
    H. Surmann and L. Peters. MORIA - a robot with fuzzy controlled behaviour. In D. Driankov and A. Saffiotti, eds, Fuzzy Logic Techniques for Autonomous Vehicle Navigation, Physica-Verlag, Heidelberg, New York, 2000, pages 343–366.Google Scholar
  41. 41.
    T. Takeuchi, Y. Nagai, and N. Enomoto. Fuzzy control of a mobile robot for obstacle avoidance. Information Sciences, 43:231–248, 1988.CrossRefGoogle Scholar
  42. 42.
    E. W. Tunstel. Fuzzy-behavior synthesis, coordination, and evolution in an adaptive behavior hierarchy. In D. Driankov and A. Saffiotti, eds, Fuzzy Logic Techniques for Autonomous Vehicle Navigation, Physica-Verlag, Heidelberg, New York, 2000, pages 205–234.Google Scholar
  43. 43.
    C. von Altrok, B. Krause, and H. J. Zimmermann. Advanced fuzzy logic control of a model car in extreme situations. Fuzzy Sets and Systems, 48:41–52, 1992.CrossRefGoogle Scholar
  44. 44.
    C. Voudouris. Fuzzy hierarchical control for autonomous mobile robots. MSc Thesis, Dept. of Computer Science, Univ. of Essex, Esssex, UK, Sept 1993.Google Scholar
  45. 45.
    L.-X. Wang. A mathematical formulation of hierarchical systems using fuzzy logic systems. In Proc. of the IEEE Int. Conf. on Fuzzy Systems, pages 183–188, Orlando, FL, 1994.Google Scholar
  46. 46.
    J. Yen and N. Pfluger. A fuzzy logic based extension to Payton and Rosenblatt’s command fusion method for mobile robot navigation. IEEE Trans. on Systems, Man, and Cybernetics, 25(6):971–978, 1995.CrossRefGoogle Scholar
  47. 47.
    L. A. Zadeh. Fuzzy sets as a basis for a theory of possibility. Fuzzy Sets and Systems, 1:3–28, 1978.MathSciNetMATHCrossRefGoogle Scholar
  48. 48.
    J. Zhang and A. Knoll. Integrating deliberative and reactive strategies via fuzzy modular control. In D. Driankov and A. Saffiotti, eds, Fuzzy Logic Techniques for Autonomous Vehicle Navigation, Physica-Verlag, Heidelberg, New York, 2000, pages 367–387.Google Scholar

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© Springer-Verlag Berlin Heidelberg 2001

Authors and Affiliations

  • Alessandro Saffiotti

There are no affiliations available

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