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Distributed Metamorphosis of Regular M-TRAN Structures

  • Esben H. Ostergaard
  • Kohji Tomita
  • Haruhisa Kurokawa
Conference paper

Abstract

A key issue in controlling the morphing process of a self-reconfigurable robot is how to deal with the complexity introduced by limitations in the mechanical capabilities of an actual physical system. In this paper, we explore how structural regularity can be exploited to reduce this complexity, by considering three specific structures that consist of many M-TRAN modules. Two control approaches are presented and compared for an example 2-dimensional flow motion. One approach considers programming using subroutines and local variables, and the other considers a direct mapping from the local physical state of a module to the modules’ action space. Also, we discuss the concept of sprouting, a process in which structures grow substructures.

Keywords

Flow Motion Obstacle Detector Proximity Sensor Reconfigurable Robot Intelligent Autonomous System 
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.
    Z. Butler, K. Kotay, D. Rus, and K. Tomita. Generic decentralized control for a class of self-reconfigurable robots. In Proceedings, IEEE Int. Conf. on Robotics and Automation (ICRA’02), volume 1, pages 809–815, Washington, DC, USA, 2002.Google Scholar
  2. 2.
    Z. Butler and D. Rus. Distributed locomotion algorithms for self-reconfigurable robots operating on rough terrain. In Proceedings of IEEE International Symposium on Computational Intelligence in Robotics and Automation (CIRA), pages 880–885, 2003.Google Scholar
  3. 3.
    D.J. Christensen, E. H. Ostergaard, and H. H. Lund. Metamodule control for the ATRON self-reconfigurable robotic system. In Proceedings of the The 8th Conference on Intelligent Autonomous Systems (IAS-8), pages 685–692, Amsterdam, 2004.Google Scholar
  4. 4.
    H. Kurokawa, A. Kamimura, E. Yoshida, K. Tomita, S. Kokaji, and S. Murata. M-TRAN II: Metamorphosis from a four-legged walker to a caterpillar. In Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems, pages 2454–2459, 2003.Google Scholar
  5. 5.
    H. Kurokawa, E. Yoshida, K. Tomita, A. Kamimura, S. Murata, and S. Kokaji. Deformable multi M-TRAN structure works as walker generator. In Proceedings of the The 8th Conference on Intelligent Autonomous Systems (IAS-8), pages 746–753, Amsterdam, 2004.Google Scholar
  6. 6.
    H.H. Lund, R.L. Larsen, and E.H. Østergaard. Distributed control in selfreconfigurable robots. In Proceedings of ICES, The 5th International Conference on Evolvable Systems: From Biology to Hardware, Trondheim, Norway, March 2003. Springer-Verlag.Google Scholar
  7. 7.
    H. Meinhardt. A model for pattern formation of hypostome, tentacles, and foot in hydra: how to form structures close to each other, how to form them at a distance. Developmental Biology, 157(2):321–333, June 1993.Google Scholar
  8. 8.
    S. Murata, H. Kurokawa, and S. Kokaji. Self-assembling machine. In Proceedings, IEEE Int. Conf. on Robotics & Automation (ICRA’ 94), pages 441–448, San Diego, California, USA, 1994.CrossRefGoogle Scholar
  9. 9.
    S. Murata, E. Yoshida, A. Kamimura, H. Kurokawa, K. Tomita, and S. Kokaji. M-TRAN: Self-reconfigurable modular robotic system. IEEE/ASME Transactions on Mechatronics, 7(4):431–441, 2002.CrossRefGoogle Scholar
  10. 10.
    E. H. Ostergaard and H. H. Lund. Distributed cluster walk for the ATRON selfreconfigurable robot. In Proceedings of the The 8th Conference on Intelligent Autonomous Systems (IAS-8), pages 291–298, Mar. 2004.Google Scholar
  11. 11.
    K.C. Prevas, C. Unsal, M.O. Efe, and P.K. Khosla. A hierarchical motion planning strategy for a uniform self-reconfigurable modular robotic system. In Proceedings, IEEE International Conference on Robotics and Automation (ICRA’02), volume 1, pages 787–792, Washington, DC, USA, 2002.Google Scholar
  12. 12.
    D. Rus and M. Vona. Crystalline robots: Self-reconfiguration with compressible unit modules. Autonomous Robots, 10(1):107–124, 2001.MATHCrossRefGoogle Scholar
  13. 13.
    W.-M. Shen, B. Salemi, and P. Will. Hormone-inspired adaptive communication and distributed control for conro self-reconfigurable robots. IEEE Transactions on Robotics and Automation, 18(5):700–712, Oct. 2002.Google Scholar
  14. 14.
    K. Stoy. Controlling self-reconfiguration using cellular automata and gradients. In Proceedings of the The 8th Conference on Intelligent Autonomous Systems (IAS-8), pages 693–702, Amsterdam, 2004.Google Scholar
  15. 15.
    K. Tomita, S. Murata, H. Kurokawa, E. Yoshida, and S. Kokaji. Self-assembly and self-repair method for a distributed mechanical system. IEEE Transactions on Robotics and Automation, 15(6):1035–1045, 1999.CrossRefGoogle Scholar
  16. 16.
    K. Tomita, S. Murata, E. Yoshida, H. Kurokawa, A. Kamimura, and S. Kokaji. Development of a self-reconfigurable modular robotic system. In Sensor Fusion and Decentralized Control in Robotic Systems III, Proceedings of SPIE, Vol. 4196, pages 469–476, 2000.Google Scholar
  17. 17.
    E. Yoshida, S. Murata, A. Kamimura, K. Tomita, H. Kurokawa, and S. Kokaji. A motion planning method for a self-reconfigurable modular robot. In Proceedings, IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS’01), pages 590–597, Maui, Hawaii, USA, 2001.Google Scholar
  18. 18.
    E. Yoshida, S. Murata, A. Kamimura, K. Tomita, H. Kurokawa, and S. Kokaji. A self-reconfigurable modular robot: Reconfiguration planning and experiments. The International Journal of Robotics Research, 21(10):903–916, 2002.CrossRefGoogle Scholar

Copyright information

© Springer 2007

Authors and Affiliations

  • Esben H. Ostergaard
    • 1
  • Kohji Tomita
    • 2
  • Haruhisa Kurokawa
    • 2
  1. 1.The Maersk Mc-Kinney Moller Institute for Production TechnologyUniversity of Southern DenmarkDenmark
  2. 2.National Institute of Advanced Industrial Science and Technology (AIST)Japan

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