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JACIII Vol.18 No.2 pp. 135-139
doi: 10.20965/jaciii.2014.p0135
(2014)

Paper:

Gait Motion Planning for a Six Legged Robot Based on the Associatron

Tomo Ishikawa*, Koji Makino**, Junya Imani*,
and Yasuhiro Ohyama*

*Graduate School of Bionics, Computer and Media Sciences, Tokyo University of Technology, 1404-1 Katakura, Hachioji, Tokyo 192-0982, Japan

**Graduate School of Medicine and Engineering, University of Yamanashi, 4-3-11 Takeda, Kofu, Yamanashi 400-8511, Japan

Received:
May 22, 2013
Accepted:
January 6, 2014
Published:
March 20, 2014
Keywords:
associatron, gait motion, six legged robot
Abstract

This research addresses a gait motion planning problem for a six-legged robot walking on an irregular field. In this proposal, we used a simplified neural network model called an Associatron that recalls total motion patterns sequentially frompartial information. The Associatron is used here because it is more effective and adaptable than conventional methods. Using the proposed method, the robot is expected to walk in unknown fields. After verifying planning using an Open Dynamics Engine (ODE) by using simulations, we found that memorized patterns are recalled from developed patterns. We then conducted experiments using a real developed robot. Experiment results show that, when using the proposed planning method, the robot selects suitable gait motion patterns in the presence of an obstacle.

Cite this article as:
T. Ishikawa, K. Makino, J. Imani, and <. Ohyama, “Gait Motion Planning for a Six Legged Robot Based on the Associatron,” J. Adv. Comput. Intell. Intell. Inform., Vol.18, No.2, pp. 135-139, 2014.
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References
  1. [1] M. K. Habib, “Bioinspiration and Robotics Walking and Climbing Robots,” 2007.
  2. [2] Q.J Huang and K. Nonami, “Neuro-Based Position and Force Hybrid Control of Six-Legged Walking Robot,” J. of Robotics and Mechatronics, Vol.14, No.4, pp. 324-332, 2002.
  3. [3] T. Yamaguchi, K. Watanabe, K. Izumi, and K. Kiguchi, “Obstacle Avoidance for Quadruped Robots Using a Neural Network,” J. of Advanced Computational Intelligence and Intelligent Informatics, Vol.7, No.2, pp. 115-123, 2003.
  4. [4] K. Nakano, “Associatron-A Model of Associative Memory,” IEEE Trans. on Systems, Man and Cybernetics, Vol.SMC-2, No.3, pp. 380-388, 1972.
  5. [5] M. Sakai, M. Hashimoto, T. Ishikawa, K. Makino, J. She, and Y. Ohyama, “Gait motion of a six legged real robot employing associatron,” The 18th Int. Symp. on Artificial Life and Robotics (AROB 18th ’13), pp. 420-423, 2013.
  6. [6] T. Ishikawa, Y. Ohyama, K. Makino, and J. Imani, “Associatron-Based Gait Motion Planning of a Six Legged Robot,” Proc. of the 9th China-Japan Int. Workshop on Internet Technology and Control Applications 2013 (ITCA2013), pp. 89-92, 2013.
  7. [7] M. Ishikawa, N. Mukohzaka, H. Toyoda, and Y Suzuki, “Experimental studies on learning capabilities of optical associative memory,” Optical Computing ’88, Proc. SPIE, Vol.963, pp. 527-536, 1988.
  8. [8] A. Kanagawa, H. Kawabata, and H. Takahashi, “Cellular Neural Networks with Multiple-Valued Output and Its Application,” IEICE Trans. on Fundamentals of Electronics, Communications and Computer Sciences, Vol.E79-A, No.10, pp. 1658-1663, 1996.
  9. [9] M. Kanagawa, “Entropy Based Associative Memory,” IEICE Trans. on Fundamentals of Electronics, Communications and Computer Sciences, Vol.E89-A, No.4, pp. 895-901, 2006.
  10. [10] S. Tangruamsub, A. Kawewong, M. Tsuboyama, and O. Hasegawa, “Self-Organizing Incremental Associative Memory-Based Robot Navigation,” IEICE Trans. on Information and Systems, Vol.E95.D, No.10, pp. 2415-2425, 2012.
  11. [11] M. Iwasaki, T. Hashiyama, and S. Okuma, “Self-Organizing Feature Extraction Using Associative Memory,” IEEJ Trans. on Electronics, Information and Systems, Vol.120, No.10, pp. 1467-1474, 2000 (in Japanese).

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Last updated on Nov. 15, 2018