NDLI: NEURAL NETWORK WITH VARIABLE TYPE CONNECTION WEIGHTS FOR AUTONOMOUS OBSTACLE AVOIDANCE ON A PROTOTYPE OF SIX-WHEEL TYPE INTELLIGENT WHEELCHAIR (2006)

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NEURAL NETWORK WITH VARIABLE TYPE CONNECTION WEIGHTS FOR AUTONOMOUS OBSTACLE AVOIDANCE ON A PROTOTYPE OF SIX-WHEEL TYPE INTELLIGENT WHEELCHAIR (2006)

Content Provider	CiteSeerX
Author	Yasuda, Toshihiko Nakamura, Kazushi Kawahara, Akihiro Tanaka, Katsuyuki
Abstract	Abstract. In this paper, an assist method for human operation of electric-powered wheelchairs is studied. The purpose of this research is to make powered wheelchairs intelligent and to realize a mobility aid for people, who find it difficult or impossible to drive a conventional wheelchair. On a prototype of our group, a neural network produces an obstacle avoidance function. In this research, by the approach that connection weights of the neural network change according to the condition of obstacles in the vicinity of the wheelchair and the running state of the wheelchair, we improve the obstacle avoidance function. First, neural networks evolve by using digital computer simulator. Secondly, experiments, using a prototype with six wheels implemented neural networks whose connection weights are determined by numerical studies, demonstrate that the neural network with variable connection weights exhibits the excellent level of ability of obstacle avoidance.
File Format	PDF
Publisher Date	2006-01-01
Access Restriction	Open
Subject Keyword	Six-wheel Type Intelligent Wheelchair Obstacle Avoidance Function Connection Weight Neural Network Change Electric-powered Wheelchair Variable Connection Weight Conventional Wheelchair Mobility Aid Powered Wheelchair Intelligent Numerical Study Excellent Level Assist Method Obstacle Avoidance Human Operation Digital Computer Simulator
Content Type	Text
Resource Type	Article

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