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Autonomous Robots

, Volume 10, Issue 1, pp 41–56 | Cite as

Distributed Control for 3D Metamorphosis

  • Mark Yim
  • Ying Zhang
  • John Lamping
  • Eric Mao
Article

Abstract

In this paper, we define Proteo as a class of three-dimensional (3D) metamorphic robotic system capable of approximating arbitrary 3D shapes by utilizing repeated modules. Each Proteo module contains embedded sensors, actuators and a controller, and each resides in a 3D grid space. A module can move itself to one of its open neighbor sites under certain motion constraints. Distributed control for the self-reconfiguration of such robots is an interesting and challenging problem. We present a class of distributed control algorithms for the reconfiguration of Proteo robots based on the “goal-ordering” mechanism. Performance results are shown for experiments of these algorithms in a simulation environment, and the properties of these algorithms are analyzed.

metamorphic robots distributed control of shape reconfiguration motion constraints goal ordering 

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References

  1. Bojinov, H., Casal, A., and Hogg, T. 2000. Emergent structures in modular self-reconfigurable robots. In Proc. IEEE International Conference on Robotics and Automation, San Francisco, CA. pp. 1734–1741.Google Scholar
  2. Chirikjian, G. 1993. Metamorphic hyper-redundant manipulators. In Proc. JSME International Conference on Advanced Mechatronics, pp. 467–472.Google Scholar
  3. Chirikjian, G., Pamecha, A., and Ebert-Uphoff, I. 1996. Evaluating efficiency of self-reconfiguration in a class of modular robots. Journal of Robotic Systems, 13(5):317–338.Google Scholar
  4. Fukuda, T. and Kawauchi,Y. 1990. Cellular robotic system (CEBOT) as one of the realization of self-organizing intelligent universal manipulator. In Proc. IEEE International Conference on Robotics and Automation, pp. 662–667.Google Scholar
  5. Hamlin, G. and Sanderson, A. 1996. Tetrobot modular robotics: Prototype and experiments. In Proc. IEEE/RSJ International Symposium of Robotics Research, Osaka, Japan, pp. 390–395.Google Scholar
  6. Hosokawa, K., Tsujimori, T., Fujii, T., Kaetsu, H., Asama, H., Kuroda, Y., and Endo, I. 1998. Self-organizing collective robots with morphogenesis in a vertical plane. In Proc. IEEE International Conference on Robotics and Automation, Leuven, Belgium, pp. 2858–2863.Google Scholar
  7. Kotay, K., and Rus, D. 1999. Locomotion versatility through self-reconfiguration. Robotics and Autonomous Systems, 26(2–3):217–232.Google Scholar
  8. Latombe, J.-C. 1991. Robot Motion Planning, Kluwer: Dordrecht, Netherlands.Google Scholar
  9. Murata, S., Kurokawa, H., and Kokaji, S. 1994. Self-assembling machine. In Proc. IEEE International Conference on Robotics and Automation, pp. 441–448.Google Scholar
  10. Murata, S., Kurokawa, H., Yoshida, E., Tomita, K., and Kokaji, S. 1998. A 3D self-reconfigurable structure. In Proc. IEEE International Conference on Robotics and Automation, Leuven, Belgium, pp. 432–439.Google Scholar
  11. Nguyen, A., Guibas, L., and Yim, M. 2000. Controlled module density helps reconfiguration planning. In Workshop on the Algorithmic Foundations of Robotics, pp. TH15-TH27.Google Scholar
  12. Pamecha, A., Chiang, C., Stein, D., and Chirikjian, G.S. 1996. Design and implementation of metamorphic robots. In Proc. ASME Design Engineering Technical Conference and Computers in Engineering Conference, Irvine, California.Google Scholar
  13. Pamecha, A., Ebert-Uphoff, I., and Chirikjian, G.S. 1997. Useful metrics for modular robot motion planning. In Proc. IEEE Transactions on Robots and Automation, 13(4):531–545.Google Scholar
  14. Paredis, C. and Khosla, P. 1993. Kinematic design of serial link manipulators from task specifications. International Journal of Robotic Research, 12(3):274–287.Google Scholar
  15. Rus, D. and Vona, M. 1999. Self-reconfiguration planning with compressible unit modules. In Proc. IEEE International Conference on Robotics and Automation, Chicago, IL, pp. 2513–2520.Google Scholar
  16. Rus, D. and Vona, M. 2000. A physical implementation of the self-reconfiguring crystalline robot. In Proc. IEEE International Conference on Robotics and Automation, San Francisco, CA, pp. 1726–1733.Google Scholar
  17. Unsal, C. and Khosla, P. 2000. Mechatronic design of a modualr selfreconfiguring robotic system. In Proc. IEEE International Conference on Robotics and Automation, San Francisco, CA, pp. 1742–1747.Google Scholar
  18. Walker, I. and Cavallaro, J. 1999. Keeping the analog genie in the bottle: A case for digital robots. In Proc. IEEE International Conference on Robotics and Automation, Chicago, IL, pp. 1063–1070.Google Scholar
  19. Will, P., Castano, A., and Shen, W.-M. 1999. Robot modularity for self-reconfiguration. In SPIE International Symposium on Intelligent Systems and Advanced Manufacturing Proc., Vol. 3839, pp. 236–245.Google Scholar
  20. Yim, M. 1994. New locomotion gaits. In Proc. IEEE International Conference on Robotics and Automation, San Diego, CA, pp. 2508–2514.Google Scholar

Copyright information

© Kluwer Academic Publishers 2001

Authors and Affiliations

  • Mark Yim
    • 1
  • Ying Zhang
    • 1
  • John Lamping
    • 1
  • Eric Mao
    • 1
  1. 1.Xerox Palo Alto Research CenterPalo AltoUSA

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