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ANALYSIS OF GRASPING MOVEMENTS BY HUMAN HAND AND ITS APPLICATION FOR MANIPULATING HAND ROBOT

Research Project

Project/Area Number 07455176
Research Category

Grant-in-Aid for Scientific Research (B)

Allocation TypeSingle-year Grants
Section一般
Research Field 計測・制御工学
Research InstitutionKANAZAWA INSTITUTE OF TECHNOLOGY

Principal Investigator

SUZUKI Ryoji  Kanazawa Institute of Technology, Faculty of Engineering, Professor, 工学部, 教授 (80013811)

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)
Project Period (FY) 1995 – 1996
Project Status Completed (Fiscal Year 1996)
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)
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
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.

Report

(3 results)
  • 1996 Annual Research Report   Final Research Report Summary
  • 1995 Annual Research Report
  • Research Products

    (11 results)

All Other

All Publications (11 results)

  • [Publications] 福村直博: "対象物の形状に合わせて手の形を決定する神経回路モデル" システム制御情報学会論文誌. 8. 408-417 (1995)

    • Description
      「研究成果報告書概要(和文)」より
    • Related Report
      1996 Final Research Report Summary
  • [Publications] Y.Uno: "A cpmputational model for recognizing objects and planning hand shapes in grasping movements" Neural Networks. 8. 839-851 (1995)

    • Description
      「研究成果報告書概要(和文)」より
    • Related Report
      1996 Final Research Report Summary
  • [Publications] M.Dornay: "Minimum Muscle-Tension Change Trajectories Predicted by Using a 17-Muscle Model of the Monkey's Arm" J. Motor Behavior. 28. 83-100 (1996)

    • Description
      「研究成果報告書概要(和文)」より
    • Related Report
      1996 Final Research Report Summary
  • [Publications] 島田洋一: "把持動作における順ダイナミクスモデルの学習" 電子情報通信学会研究会技術資料. MBE97(印刷中). (1997)

    • Description
      「研究成果報告書概要(和文)」より
    • Related Report
      1996 Final Research Report Summary
  • [Publications] N.Fukumura: "A Neural Network Models that Designs Hand Shapes to Grasp Objects" J.Society of System, Control, Information. 8-8. 408-417 (1995)

    • Description
      「研究成果報告書概要(欧文)」より
    • Related Report
      1996 Final Research Report Summary
  • [Publications] Y.Uno: "A computational model for recognizing objects and planning hand shapes in grasping movements" Neural Networks. 8-5. 839-851 (1995)

    • Description
      「研究成果報告書概要(欧文)」より
    • Related Report
      1996 Final Research Report Summary
  • [Publications] M.Dornay: "Minimum Muscle-Tension Change Trajectories Predicted by Using a 17-Muscle Model of the Monkey's Arm." J.Motor Behavior. 28-2. 83-100 (1996)

    • Description
      「研究成果報告書概要(欧文)」より
    • Related Report
      1996 Final Research Report Summary
  • [Publications] Y.Shimada: "Learning of Forward Dynamics Model for Grasping by Hand" Technical Notes, MBE. 97(in press). (1997)

    • Description
      「研究成果報告書概要(欧文)」より
    • Related Report
      1996 Final Research Report Summary
  • [Publications] M.Dornay: "Minimum Muscle-Tension Change Trajectories Predicted by Using as 17-Muscle Model of the Monkey's Arm" Journal of Motor Behavior. 28・2. 83-100 (1996)

    • Related Report
      1996 Annual Research Report
  • [Publications] Y. Uno: "A Computational Model for Recognzing Objects and Planning Hand Shapes in Grasping Movements" Neural Networks. 8. 839-852 (1995)

    • Related Report
      1995 Annual Research Report
  • [Publications] 福村直博: "対象物体の形状に合わせて手の形を決定する神経回路モデル" システム制御情報学会論文誌. 8. 408-417 (1995)

    • Related Report
      1995 Annual Research Report

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Published: 1995-04-01   Modified: 2016-04-21  

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