| Project/Area Number |
07455176
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| Research Category |
Grant-in-Aid for Scientific Research (B)
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| Allocation Type | Single-year Grants |
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| Section | 一般 |
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| Research Field |
計測・制御工学
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| Research Institution | KANAZAWA INSTITUTE OF TECHNOLOGY |
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Principal Investigator |
SUZUKI Ryoji Kanazawa Institute of Technology, Faculty of Engineering, Professor, 工学部, 教授 (80013811)
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| Co-Investigator(Kenkyū-buntansha) |
SHIMADA Yoichi Kanazawa Institute of Technology, Faculty of Engineering, Professor, 工学部, 教授 (50113155)
UNO Yoji Toyohashi University of Technology, Department of Information and Computer Scien, 情報系, 教授 (10203572)
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| Project Period (FY) |
1995 – 1996
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| Project Status |
Completed (Fiscal Year 1996)
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| Budget Amount *help |
¥7,000,000 (Direct Cost: ¥7,000,000)
Fiscal Year 1996: ¥1,500,000 (Direct Cost: ¥1,500,000)
Fiscal Year 1995: ¥5,500,000 (Direct Cost: ¥5,500,000)
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| Keywords | 把持動作 / 感覚・運動情報統合 / 砂時計型神経回路 / 筋電流 / 運動学習 / 最適制御 / ハンドロボット / 感覚運動情報統合 / 砂時計神経回路 |
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| Research Abstract |
Mechanisms of integration of visual and motor informetion and generating control signals for grasping movemetnts by human hands were investigated in the following three aspects.
1. Effects of visual information on grasping performance.
(1) Effects of visual blur on reaching time were investigated by using of image editting system which can show subjects virtual 3-dimensional objects. The results showed that the increase of blur extends reaching time.
(2) Effects of condition of object on grasping performance were investigated. Empty cup, cup filled with water and cup with water and cap were used as grasping object. The results showed visually recognized conition of object has significant effects on reaching trajectories and time.
2. Generalization ability of hour glass type neural network as a model of grasping mechanisms.
Modified hour glass type neural network model were used for integrating visula and motor information and generating control signal in grasping. The neural network are selforganized in learning phase and can generate the suitable hand shapes for grasping objects by using a relaxation computation. As learning objects, we used not only convex object such as cylinder, square pillar, ball, but concave object such as handle, gourd. Rather good ability of generalization was shown by using of an ellipsoid as a test object.
3. Neural network model which learns forwad dynamics of grasping movements. Three layred neural network model was used. Subjects are asked to grasp 5 sizes of object firmly. For each trial, 4 channel electromyograms from upper arm and 2 joint angles of thumb and index finger respectively are measured as well as grasping force. They are used in learning phase and other sets of EMG.joint angles and grasping force are used as test data. Learning was succesful and showed the possibility of estimating grasping force and finger joint torques during grasping.
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