Top > JRM > Acquisition of Adaptive Behavior for the SMA-Net Robot Using C ...

single-rb.php

JRM Vol.16 No.4 pp. 411-419
doi: 10.20965/jrm.2004.p0411
(2004)

Paper:

Views over last 60 days: 397

Acquisition of Adaptive Behavior for the SMA-Net Robot Using Chaotic Neural Networks

Ikuo Suzuki*, Masaru Fujii**, Keitaro Naruse**,
Hiroshi Yokoi***, and Yukinori Kakazu**

*Life Oriented Software Laboratory, Muroran Institute of Technology, 27-1 Mizumoto-cho, Muroran 050-8585, Japan

**Graduate School of Engineering, Hokkaido University, Kita-13 Nishi-8, Kita-ku, Sapporo 060-8628, Japan

***School of Engineering, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8656, Japan

Received:
January 23, 2004
Accepted:
March 4, 2004
Published:
August 20, 2004
Creative Commons License
Keywords:
adaptive behavior, SMA-Net Robot, chaotic neural network, online learning
Abstract
In this paper, we propose control for the SMA-Net Robot, a flexible structure consisting of many units and shape memory alloy (SMA) springs. To generate greater force for movement, more than one SMA spring is required. Since SMA springs are driven by thermal transition, controlling individual spring heating patterns is important in SMA-Net Robot behavior. It is a problem in controlling SMA spring that its detailed control is difficult because of the nonlinearity. We propose methodology that arranges heating and cooling as a rhythm pattern memorized by many chaotic neural networks (CNNs). To renew connecting weights in the network, we use the modified dynamic learning method (DLM) in online learning. The results of computational experiments showed that the SMA-Net Robot with the proposed control generates movement automatically.
Cite this article as:
I. Suzuki, M. Fujii, K. Naruse, H. Yokoi, and Y. Kakazu, “Acquisition of Adaptive Behavior for the SMA-Net Robot Using Chaotic Neural Networks,” J. Robot. Mechatron., Vol.16 No.4, pp. 411-419, 2004.
Data files:
References
  1. [1] M. Otake, Y. Kagami, M. Inaba, and H. Inoue, “Behavior of a mollusk-type robot made of electro active polymer gel under spatially varying electro fields,” Intelligent Autonomous systems, Vol.6, pp. 686-691, 2000.
  2. [2] S. Murata, E. Yoshida, K. Tomita, H. Kurokawa, A. Kamimura, and S. Kokaji, “Hardware Design of Modular Robotic System,” Proc. IEEE/RSJ IROS 2000, CD-ROM, 2000.
  3. [3] K. Stoy, W. M. Shen, and P. Will, “Global Locomotion from Local Interaction in Self-Reconfigurable Robots,” Intelligent Autonomous systems, Vol.7, pp. 309-316, 2002.
  4. [4] E. Yoshida, S. Murata, A. Kamimura, K. Tomita, H. Kurokawa, and S. Kokaji, “Automatic Locomotion Pattern Generation for Modular Robots,” Proc. of the 2003 ICRA, pp. 1004-1010, 2003.
  5. [5] G. Taga, “A model of the neuro-musculo-skeletal system for human locomotion II-Real-time adaptability under various constrains,” Biol. Cybern., Vol.73, pp. 113-121, 1995.
  6. [6] H. Kimura, S. Akiyama, and K. Sakurama, “Realization of Dynamic Walking and Running of the Quadruped Using Neural Oscillator,” Autonomous Robots, Vol.7, No.3, pp. 247-258, 1999.
  7. [7] T. Ebuchi, M. Tsuchiya, T. Maeno, and N. Yamazaki, “Frictional Driving Mechanism Based on Wave Propagation,” Trans. of the JSME, C, Vol.68, pp. 920-926, 2002 (in Japanese).
  8. [8] T. Nagai, H. Yokoi, and Y. Kakazu, “SMA-Net: A Deformable Morphology Robot Using Shape Memory Alloy,” Journal of Robotics and Mechatronics, Vol.14, No.3, pp. 290-297, 2002.
  9. [9] M. Asada, K. F. MacDorman, H. Ishiguro, and Y. Kuniyoshi, “Cognitive developmental robotics as a new paradigm for the design of humanoid robots,” Robotics and Autonomous Systems, Vol.37, pp. 185-193, 2001.
  10. [10] K. Aihara, T. Takabe, and M. Toyoda, “Chaotic Neural Networks,” Phys. Lett. A, 144, 6, 7, pp. 333-340, 1990.
  11. [11] M. Adachi, and K. Aihara, “Associative dynamics in a chaotic neural network,” Neural Networks, Vol.10, pp. 333-340, 1996.
  12. [12] Y. Kinoshita, H. Yokoi, and Y. Kakazu, “An On-Line Trial and Error Learning Method for Chaotic Neural Networks,” International Engineering Systems through Artificial Neural Networks, ASME Press, Vol.8, pp. 643-648, 1999.
  13. [13] M. Fujii, H. Yokoi, and Y. Kakazu, “Modeling and Movement Control of Mobile SMA-Net,” Proc. of IEEE International Symposium on CIRA2003, pp. 253-258, 2003.

Creative Commons License  This article is published under a Creative Commons Attribution-NoDerivatives 4.0 Internationa License.

Copyright© 2004 by Fuji Technology Press Ltd. and Japan Society of Mechanical Engineers. All right reserved.

*This site is desgined based on HTML5 and CSS3 for modern browsers, e.g. Chrome, Firefox, Safari, Edge, Opera.

Last updated on Apr. 22, 2024