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Multiple Objective vs. Fuzzy Behavior Coordination

  • Paolo Pirjanian
  • Maja Matarić
Part of the Studies in Fuzziness and Soft Computing book series (STUDFUZZ, volume 61)

Abstract

In the behavior-based paradigm [4,16], the control of a robot is shared between a set of purposive processes, called behaviors. Based on selective sensory in-formation, each behavior produces/proposes reactions to control the robot with respect to a particular objective, i.e., a narrow aspect of the robot’s overall task. Behaviors with different and possibly incommensurable objec-tives may produce conflicting actions that are seemingly irreconcilable. Thus, the design of effective behavior coordination mechanisms is a major research issue.

Keywords

Mobile Robot Action Selection Fuzzy Approach Discrete Event System Behavior Coordination 
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.
    Philip E. Agre and David Chapman. What Are Plans for? Robotics and Au­tonomous Systems, 6:17–34, 1990.CrossRefGoogle Scholar
  2. 2.
    Ronald C. Arkin. Towards the Unification of Navigational Planning and Reactive Control. AAAI Spring Symposium on Robot Navigation, March 1989.Google Scholar
  3. 3.
    Ronald C. Arkin. Integrating Behavioral, Perceptual and World Knowledge in Reactive Navigation. Robotics and Autonomous Systems, 6:105–122, June 1990.CrossRefGoogle Scholar
  4. 4.
    Ronald C. Arkin. Behavior-Based Robotics. Intelligent Robotics and Autonomous Agents series. MIT Press, May 1998.Google Scholar
  5. 5.
    Rodney A. Brooks. A Robust Layered Control System for a Mobile Robot. IEEE Journal of Robotics and Automation, 2(l):14–23, March 1986.Google Scholar
  6. 6.
    Vira Chankong and Yacov Y. Haimes. Multiobjective Decision Making - Theory and Methodology, volume 8. North-Holland, 1983.Google Scholar
  7. 7.
    Dieter Fox, Wolfram Burgard, and Sebastian Thrun. The Dynamic Window Approach to Collision Avoidance. IEEE Robotics and Automation Magazine, 4(1):23–33, March 1997.CrossRefGoogle Scholar
  8. 8.
    Joel Hoff and George Bekey. An Architecture for Behavior Coordination Learning. In IEEE International Conference on Neural Networks, Perth, Australia, volume 5, pages 2375–2380, November 1995.Google Scholar
  9. 9.
    Peter G. W. Keen. The Evolving Concept of Optimality. In Martin K. Starr and Milan Zeleny, editors, Multiple Criteria Decision Making, volume 6 of Studies in the management sceiences, pages 31–57. North-Holland Publishing Company, 1977.Google Scholar
  10. 10.
    Omar Khatib. Real-Time Obstacle Avoidance for Manipulators and Mobile Robots. The International Journal of Robotics Research, 5(l):90–98, 1986.CrossRefGoogle Scholar
  11. 11.
    Kurt Konolige. Saphira Software Manual. ActivMedia Inc., version 6.1 edition, 1998.Google Scholar
  12. 12.
    Jana Kosecka and Ruzena Bajcsy. Discrete Event Systems for Autonomous Mobile Agents. Proceedings Intelligent Robotic Systems ’93 Zakopane, pages 21–31, July 1993.Google Scholar
  13. 13.
    Steen Kristensen. Sensor Planning with Bayesian Decision Theory. Technical Report, Laboratory of Image Analysis, Aalborg University, June 1995.Google Scholar
  14. 14.
    Edward W. Large, Henrik I. Christensen, and Ruzena Bajcsy. Scaling the Dynamic Approach to Path Planning and Control: Competition among Behavioral Constraints. International Journal of Robotics Research, 18:37–58, January 1997.Google Scholar
  15. 15.
    Pattie Maes. How To Do the Right Thing. Technical Report NE 43–836, AI-Laboratory, Massachusetts Institute of Technology, 545 Technology Square, Cambridge, MA 02139, USA, 1989.Google Scholar
  16. 16.
    Maja Mataric. Behavior-Based Control: Examples from Navigation, Learn­ing, and Group Behavior. Journal of Experimental and Theoretical Artificial Intelligence, special issue on Software Architectures for Physical Agents, 9(2–3):323–336, 1997.Google Scholar
  17. 17.
    David W. Payton. Internalized plans: A representation for action re­sources. In Pattie Maes, editor, Designing Autonomous Agents, pages 89–104. MIT/Elsevier, The MIT Press, Cambridge, Massachusetts, London, England, 1990.Google Scholar
  18. 18.
    David W. Payton, J. Kenneth Rosenblatt, and David M. Keirsey. Plan guided reaction. IEEE transactions on Systems, Man, and Cybernetics, 20(6):1370–1382, November/December 1990.CrossRefGoogle Scholar
  19. 19.
    F. G. Pin and Y. Watanabe. Automatic Generation of Fuzzy Rules Using the Fuzzy Behaviorist Approach: The Case of Sensor-Based Robot Navigation. Intelligent Automation and Soft Computing, 1(2): 161–178, 1995.Google Scholar
  20. 20.
    Paolo Pirjanian. Multiple Objective Action Selection & Behavior Fusion using Voting. PhD thesis, Department of Medical Informatics and Image Analysis, Institute of Electronic Systems, Aalborg University, Fredrik Bajers Vej 7, DK-9220 Aalborg, Denmark, August 1998. http://www-robotics.usc.edu/”paolo/publications.Google Scholar
  21. 21.
    Paolo Pirjanian. Multiple objective action selection in behavior-based control. In The proceedings of the 6th Symposium for Intelligent Robotic Systems, Edinburgh, Scotland, pages 83–92, July 1998.Google Scholar
  22. 22.
    Paolo Pirjanian. Satisficing action selection. In Sensor Fusion and Decentralized Control in Robotic Systems, volume 3523, pages 157–168. SPIE Conference, November 1998.Google Scholar
  23. 23.
    Paolo Pirjanian. Behavior coordination mechanisms - state-of-the-art. Technical Report IRIS-99–375, Institute of Robotics and Intelligent Systems, School of Engineering, University of Southern California, October 1999. http://iris.usc.edu/~irislib.
  24. 24.
    Jukka Riekki and Juha Roning. Reactive Task Execution by Combining Action Maps. In International Conference on Integrated Robots and Systems (IROS), pages 224–230, Grenoble, France, September 1997.Google Scholar
  25. 25.
    Julio K. Rosenblatt. DAMN: A Distributed Architecture for Mobile Navigation. In AAAI Spring Symposium on Lessons Learned from Implemented Software Architectures for Physical Agents, Stanford, CA. AAAI Press, Menlo Park, CA., March 1995.Google Scholar
  26. 26.
    Enrique H. Ruspini. On truth and utility. In R. Kruse and P. Siege, editors, Symbolic and Quantitative Approaches to Uncertainty, pages 297–304. Springer-Verlag, 1991.Google Scholar
  27. 27.
    Alessandro Saffiotti. Using Fuzzy Logic in Autonomous Robot Navigation: a catalogue raisonne. Soft Computing 1(4), 1997. Online at http://www.aass.oru.se/Living/FLAR/.
  28. 28.
    Alessandro Saffiotti, Kurt Konolige, and Enrique H. Ruspini. A multivalued logic approach to integrating planning and control. Artificial Intelligence, 76:481–526, March 1995.CrossRefGoogle Scholar
  29. 29.
    Herbert A. Simon. The New Science of Management Decision. Harper and Row, New York, 1960.CrossRefGoogle Scholar
  30. 30.
    Eddie Tunstel. Coordination of Distributed Fuzzy Behaviors in Mobile Robot Control. In IEEE International Conference on Systems, Man,and Cybernetics, pages 4009–4014, Vancouver, BC, Canada, October 1995.Google Scholar
  31. 31.
    John Yen and Nathan Pfluger. A Fuzzy Logic Based Extension to Payton and Rosenblatt’s Command Fusion Method for Mobile Robot Navigation. IEEE Transactions on Systems, Man, and Cybernetics, 25(6):971–978, June 1995.CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2001

Authors and Affiliations

  • Paolo Pirjanian
  • Maja Matarić

There are no affiliations available

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