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Dynamic Robot-Target Assignment — Dependence of Recovering from Breakdowns on the Speed of the Selection Process

  • Tomoyuki Kaga
  • Jens Starke
  • Péter Molnár
  • Michael Schanz
  • Toshio Fukuda
Chapter

Abstract

The self-organized and fault tolerant behavior of a novel control method for the dynamic assignment of robots to targets using an approach proposed by Starke and Molnar is investigated in detail. Concerning the robot-target assignments the method shows an excellent error resistivity and robustness by using only the local information of each robot. Experimental results verify the dynamic assignment of the mobile robots to the targets and the capability to cope with sudden changes like a breakdown of one of the robots. The dependence of the assignment on the speed of the target-selection dynamics is shown by both experiments and numerical simulations. The results suggest the existence of an optimal value for the speed of the target-selection dynamics.

Keywords

Mobile Robot Automatic Guide Vehicle Autonomous Mobile Robot Destination Direction Dynamic Assignment 
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.
    A. Cai: “Behavior Decision and Distributed Sensing on Cellular Robotic System” , Ph.D. thesis, Nagoya University, 1983, in JapaneseGoogle Scholar
  2. 2.
    T. Lueth, R. Dillmann, P. Dario and H. Worn: “Distributed Autonomous Robotic System 3”, Springer, 1998CrossRefGoogle Scholar
  3. 3.
    T. Fukuda and S. Nakagawa : “Approach to the dynamically reconfigurable robotic system”, Journal of Intelligent and Robotic Systems, pp. 55–72, 1988 pp. 239-248, 1991Google Scholar
  4. 4.
    T. Fukuda and T. Ueyama: “Cellular Robotics and Micro Robotic Systems”, World Scientific Series in Robotics and Automated Systems, World Scientific, 1994Google Scholar
  5. 5.
    H. Haken: “Synergetics, An Introduction”, Springer Series in Synergetics, Springer-Verlag, Heidelberg, Berlin, New York, 1983Google Scholar
  6. 6.
    H. Haken: “Advanced Synergetics”, Springer Series in Synergetics, Springer-Verlag, Heidelberg, Berlin, New York, 1983Google Scholar
  7. 7.
    P. Molnár and J. Starke: “Control of distributed autonomous robotic systems using principles of pattern formation in nature and pedestrian behaviour”, submitted, 1999Google Scholar
  8. 8.
    C. Papadimitriou and K. Steiglitz: “Combinatorial Optimization -Algorithms and Complexity”, Prentice-Hall.Englewood Cliffs, New Jersey, 1982MATHGoogle Scholar
  9. 9.
    P. Pu and J. Hughes: “Integrative AGV schedules in a scheduling system for a flexible manufacturing environment”, in Proceedings of the IEEE International Conference on Robotics and Automation, pp. 3149–3154, 1994Google Scholar
  10. 10.
    J. Starke: “Kombinatorische Optimierung auf der Basis gekoppelter Selektions-gleichungen”, Ph.D. thesis, Universität Stuttgart, Verlag Shaker, Aachen, 1997Google Scholar
  11. 11.
    J. Starke, M. Schanz and H. Haken: “Self-Organized Behaviour of Distributed Autonomous Mobile Robotic Systems by Pattern Formation Principles”, Distributed Autonomous Systems 3, Springer, pp. 90–100, 1998 and T. Fukuda:Google Scholar

Copyright information

© Springer-Verlag Tokyo 2000

Authors and Affiliations

  • Tomoyuki Kaga
    • 1
  • Jens Starke
    • 2
  • Péter Molnár
    • 3
  • Michael Schanz
    • 4
  • Toshio Fukuda
    • 5
  1. 1.Graduate School of EngineeringNagoya UniversityJapan
  2. 2.Institute of Applied MathematicsUniversity of HeidelbergGermany
  3. 3.Center for Theoretical Studies of Physical SystemsClark Atlanta UniversityUSA
  4. 4.Institute of Parallel and Distributed High Performance SystemsUniversity of StuttgartGermany
  5. 5.Center for Cooperative Research in Advanced Science and TechnologyNagoya UniversityJapan

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