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

Toshihiko YASUDA | Zendy; Hajime Tanaka | Zendy; Kazushi Nakamura | Zendy; Katsuyuki Tanaka | Zendy

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

Author(s) -

Toshihiko YASUDA,

Hajime Tanaka,

Kazushi Nakamura,

Katsuyuki Tanaka

Publication year - 2007

Publication title -

journal of advanced computational intelligence and intelligent informatics

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.172

H-Index - 20

eISSN - 1343-0130

pISSN - 1883-8014

DOI - 10.20965/jaciii.2007.p0922

Subject(s) - wheelchair , obstacle , obstacle avoidance , computer science , connection (principal bundle) , artificial neural network , artificial intelligence , mobile robot , engineering , robot , mechanical engineering , world wide web , political science , law

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.

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