A Study of Electric Powered Wheelchair with Operation Assist System which does not Prevent Human Operation if Unnecessary
| Project/Area Number |
16500348
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| Research Category |
Grant-in-Aid for Scientific Research (C)
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| Allocation Type | Single-year Grants |
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| Section | 一般 |
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| Research Field |
Rehabilitation science/Welfare engineering
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| Research Institution | The University of Shiga Prefecture |
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Principal Investigator |
YASUDA Toshihiko The University of Shiga Prefecture, School of Engineering, Associate Professor, 工学部, 助教授 (60157998)
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| Project Period (FY) |
2004 – 2005
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| Project Status |
Completed (Fiscal Year 2005)
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| Budget Amount *help |
¥2,500,000 (Direct Cost: ¥2,500,000)
Fiscal Year 2005: ¥1,200,000 (Direct Cost: ¥1,200,000)
Fiscal Year 2004: ¥1,300,000 (Direct Cost: ¥1,300,000)
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| Keywords | Life Support / Electric Powered Wheelchair / Operation Assist / Assist Rate / Virtual Sensor / Neural Network / Variable Type Connection Weight / Obstacle Avoidance / 福祉ロボット / PSDセンサ / 超音波センサ |
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| Research Abstract |
The purpose of this research is to make electric-powered wheelchair intelligent and to develop a mobility aid for people, who find it difficult or impossible to drive a conventional wheelchair. To do this, an assist method for human's operation of electric-powered wheelchairs has been investigated. Safe drive and easy operation of electric-powered wheelchairs has been realized by combining a function of autonomous obstacle avoidance and human operation. The goal of our research is to construct the operation assist system, which does not prevent human operation if unnecessary.
In this study, a neural network produces an obstacle avoidance function. By using an approach that connection weights of the neural network vary according to the condition of obstacles in the neighborhood of the wheelchair and the running state of wheelchair, we have improved the obstacle avoidance function. First, neural networks have evolved by using digital computer simulator. Secondly, experiments, using protot
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ypes implemented neural networks whose connection weights were given by numerical studies, have demonstrated that the neural network with variable connection weights exhibits the more excellent ability of obstacle avoidance.
To detect obstacles, 18 PSD sensors are mounted on our prototype of intelligent wheelchair. In order to compensate weak point of the PSD sensor, we have proposed a concept of the virtual sensor, which preserves previous observation data of obstacles and constructs virtually several suitable sensing systems and improves obstacle avoidance function. Finally, we have proposed new method called the assist rate, which is used in order to control unnecessary autonomous obstacle avoidance. The assist rate, which indicates necessity of the operation assist function, is calculated based on information concerned with obstacles and the running state of wheelchair. The virtual sensor system and the assist rate have been implemented on a prototype and the effectiveness of the proposed method has been demonstrated. Less
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Report
(3 results)
Research Products
(25 results)
