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Interaction of Two Oscillator Aggregations

Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 302)

Abstract

The main feature that keeps states and structures stable can be seen in living organisms. This adjusting and adaptive features are called homeostasis. This integrated adaptive feature is achieved by the cooperation of organs in living organisms. Living organisms in nature act dynamically due to this feature. Highly adaptive behavior caused by this feature is also observed in simple living organisms that have no neural circuits such as amoebas. Based on these facts, a method of control to generate homeostasis in robotic systems is proposed by assuming a robot system is an aggregation of oscillators and each parameter in a robot system is allocated to an oscillator. Especially, interaction between two independent robots as oscillator aggregation is focused in this paper.

Keywords

A-life engineering Oscillator Homeostasis Robot 

References

  1. 1.
    Matthew M Williamson. Neural control of rhythmic arm movements. Neural Networks, 11(7):1379–1394, 1998.Google Scholar
  2. 2.
    Satoshi Ito, Hideo Yuasa, Zhi-wei Luo, Masami Ito, and Dai Yanagihara. Quadrupedal robot system adapting to environmental changes. Journal of the Robotics Society of Japan, 17(4):595–603, 1999.Google Scholar
  3. 3.
    Shinya Aoi and Kazuo Tsuchiya. Locomotion control of a biped robot using nonlinear oscillators. Autonomous Robots, 19(3):219–232, 2005.Google Scholar
  4. 4.
    Hiroshi Yokoi, Takafumi Mizuno, Masatoshi Takita, Jun Hakura, and Yukinori Kakazu. Amoeba like self-organization model using vibrating potential field. In Artificial Life V: Proceedings of the Fifth International Workshop on the Synthesis and Simulation of Living Systems, volume 5, page 51. MIT Press, 1997.Google Scholar
  5. 5.
    Sho Yamauchi, Hidenori Kawamura, and Keiji Suzuki. Extended flocking algorithm for self-parameter tuning. IEEJ Transactions on Electronics, Information and Systems, 133(6):Sec. C, 2013.Google Scholar
  6. 6.
    Sho Yamauchi, Hidenori Kawamura, and Keiji Suzuki. Observation of synchronization phenomena in structured flocking behavior. Journal of Advanced Computational Intelligence and Intelligent Informatics.Google Scholar
  7. 7.
    Tetsu Saigusa, Atsushi Tero, Toshiyuki Nakagaki, and Yoshiki Kuramoto. Amoebae anticipate periodic events. Physical Review Letters, 100(1):018101, 2008.Google Scholar
  8. 8.
    Steven H. Strogatz. SYNC: The Emerging Science of Spontaneous Order. Hyperion, 1 edition, 3 2003.Google Scholar
  9. 9.
    Axel Kleidon and Ralph Lorenz. 1 entropy production by earth system processes. In Non-equilibrium Thermodynamics and the Production of Entropy, pages 1–20. Springer, 2005.Google Scholar
  10. 10.
    Paolo Sassone-Corsi. Molecular clocks: mastering time by gene regulation. Nature, 392:871–874, 1998.Google Scholar
  11. 11.
    James J Collins and Ian N Stewart. Coupled nonlinear oscillators and the symmetries of animal gaits. Journal of Nonlinear Science, 3(1):349–392, 1993.Google Scholar
  12. 12.
    Arkady Pikovsky, Michael Rosenblum, and Juergen Kurths. Synchronization: A Universal Concept in Nonlinear Sciences (Cambridge Nonlinear Science Series). Cambridge University Press, 1 2002.Google Scholar
  13. 13.
    Arthur T Winfree. Biological rhythms and the behavior of populations of coupled oscillators. Journal of theoretical biology, 16(1):15–42, 1967.Google Scholar
  14. 14.
    John Guckenheimer. Isochrons and phaseless sets. Journal of Mathematical Biology, 1(3):259–273, 1975.Google Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.Hokkaido UniversitySapporoJapan

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