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JACIII Vol.11 No.8 pp. 922-930
doi: 10.20965/jaciii.2007.p0922
(2007)

Paper:

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Adjustability of Neural Networks with Variant Connection Weights for Obstacle Avoidance in an Intelligent Wheelchair

Toshihiko Yasuda*, Hajime Tanaka*, Kazushi Nakamura**,
and Katsuyuki Tanaka*

*Dept. of Mechanical System Engineering, The University of Shiga Prefecture, 2500 Hassaka-cho, Hikone, Shiga 522-8533, Japan

**Dai Nippon Printing Co., Ltd., 1-1-1 Ichigaya, Kagacho, Shinjuku-ku, Tokyo 162-8001, Japan

Received:
March 14, 2007
Accepted:
June 26, 2007
Published:
October 20, 2007
Creative Commons License
Keywords:
neural network, variant connection weight, obstacle avoidance, operation assist, intelligent wheelchair
Abstract
We have been studying electrically powered wheelchair operation to make electrically powered wheelchair intelligent and to develop a mobility aid for those who find it difficult or impossible to use conventional electrically powered wheelchairs. Some of the prototypes we have developed use neural networks providing obstacle avoidance. In previous research, we found that by varying neural network connection weight based on obstacles in the wheelchair’s vicinity and its run state, obstacle avoidance is improved. In this research, we discuss the adjustability of neural networks with variant connection weight based on numerical studies.
Cite this article as:
T. Yasuda, H. Tanaka, K. Nakamura, and K. Tanaka, “Adjustability of Neural Networks with Variant Connection Weights for Obstacle Avoidance in an Intelligent Wheelchair,” J. Adv. Comput. Intell. Intell. Inform., Vol.11 No.8, pp. 922-930, 2007.
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References
  1. [1] D. P. Miller and M. G. Slack, “Design and testing of a lowcost robotic wheelchair prototype,” Autonomous Robotics, Vol.2, pp. 77-88, 1995.
  2. [2] R. C. Simpson and S. P. Levine, “Adaptive shared control of a smart wheelchair operated by voice control,” Proc. of IEEE/RSJ Int. Conf. on Intelligent Robots and Systems 1997, Vol.2, pp. 622-626, 1997.
  3. [3] H. A. Yanco and J. Gips, “Preliminary investigation of a semiautonomous robotic wheelchair directed through electrodes,” Proc. of Rehabilitation Engineering Society of North America 1997 Annual Conference, pp. 414-416, 1997.
  4. [4] N. I. Katevas, N.M. Sgouros, S. G. Tzafestas, G. Papakonstantinou, P. Beattie, J. M. Bishop, P. Tsanakas, and D. Koutsouris, “The autonomous mobile robot SENARIO: a sensor-aided intelligent navigation system for powered wheelchair,” IEEE Robotics and Automation Magazine, Vol.4, No.4, pp. 60-70, 1997.
  5. [5] T. Gomi and A. Griffith, “Developing intelligent wheelchairs for the handicapped,” Lecture Notes in AI; Assistive Technology and Artificial Intelligence, Springer-Verlag Publisher, Vol.1458, pp. 150-178, 1998.
  6. [6] A. Lankenau and T. Rofer, “A versatile and safe mobility assistant,” IEEE Robotics and Automation Magazine, pp. 29-37, 2001.
  7. [7] G. Bourhis, O. Horn, O. Habert, and A. Pruski, “An autonomous vehicle for people with motor disabilities,” IEEE Robotics and Automation Magazine, pp. 20-28, 2001.
  8. [8] H. Kitagawa, T. Kobayashi, T. Beppu, and K. Terashima, “Semi-Autonomous obstacle avoidance of omnidirectional wheelchair by joystick impedance control,” IEEE/RSJ Int. Conf. on Intelligent Robots and Systems 2001, Vol.4, pp. 2148-2153, 2001.
  9. [9] E. S. Boy, C. L. Teo, and E. Burdet, “Collaborative wheelchair assistant,” Proc. IEEE/RSJ Int. Conf. on Intelligent Robots and Systems 2002, Vol.2, pp. 1511-1516, 2002.
  10. [10] S. P. Kang and J. Katupitiya, “A hand gesture controlled semiautonomous wheelchair,” IEEE/RSJ Int. Conf. on Intelligent Robots and Systems 2004, Vol.4, pp. 3565-3570, 2004.
  11. [11] T. Hatase, K. Toda, and O. Matsumoto, “Intelligent wheelchair robot ‘TAO Aicle’,” Journal of Robotics Society of Japan Special issue “Robots at EXPO 2005,” Vol.24, part 2, pp. 18-20, 2006.
  12. [12] R. M. Murray and S. S. Sastry, “Nonholonomic motion planning: steering using sinusoids,” IEEE Trans. Automatic Control, Vol.38, No.5, pp. 700-716, 1993.
  13. [13] A. Suzuki, T. Narikiyo, H. Duong, and S. Hara, “Trajectory tracking control for non-holonomic dynamic systems with uncertainty,” Trans. of the Society of Instrument and Control Engineers, Vol.37, No.8, pp. 763-769, 2001.
  14. [14] T. Ohtsuka and A. Kodama, “Automatic code generation system for nonlinear recoding horizon control,” Trans. of the Society of Instrument and Control Engineers, Vol.38, No.7, pp. 617-623, 2002.
  15. [15] K. Kinjo, E. Uezato, K. Nakazono, and T. Yamamoto, “Regulator design of two-wheel vehicle using neurocontroller optimized by genetic algorithm,” Trans. of the Society of Instrument and Control Engineers, Vol.42, No.9, pp. 1051-1057, 2006.
  16. [16] T. Yasuda, K. Nakamura, A. Kawahara, and K. Tanaka, “Neural network with variable connection weights for autonomous obstacle avoidance on a prototype of six-wheel type intelligent wheelchair,” Int. Journal of Innovative Computing, Information &Control, Vol.2, No.5, pp. 1165-1177, 2006.

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