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

人手抓取动作分析及其在操控手机器人中的应用

基本信息

批准号：
07455176
负责人：
SUZUKI Ryoji
金额：
$ 4.48万
依托单位：
KANAZAWA INSTITUTE OF TECHNOLOGY
依托单位国家：
日本
项目类别：
Grant-in-Aid for Scientific Research (B)
财政年份：
1995
资助国家：
日本
起止时间：
1995 至 1996
项目状态：
已结题

项目摘要

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.

本文从以下三个方面研究了视觉和运动信息的整合机制以及人手抓取动作控制信号的产生机制.视觉信息对抓取绩效的影响。(1)利用图像编辑系统，研究了视觉模糊对到达时间的影响。结果表明，模糊度的增加延长了到达时间。(2)研究了物体的状态对抓取绩效的影响。以空杯、盛水杯、盛水杯和杯盖作为抓取对象。实验结果表明，视觉识别的目标概念对到达轨迹和到达时间有显著影响.沙漏型神经网络作为抓取机构模型的泛化能力，采用改进的沙漏型神经网络模型集成视觉和运动信息，产生抓取控制信号。神经网络在学习阶段是自组织的，通过松弛计算可以生成适合抓取物体的手形。作为学习对象，我们不仅使用了圆柱体、方柱、球等凸形物体，还使用了手柄、葫芦等凹形物体。以椭球体为测试对象，显示出较好的泛化能力.学习抓取动作前向动力学的神经网络模型。采用三层神经网络模型。要求受试者牢牢抓住5个大小的物体。每次测试分别测量上臂4道肌电和拇指、食指2个关节角肌电以及抓握力。它们被用于学习阶段和其他组肌电信号。关节角度和抓握力被用作测试数据。学习是成功的，并显示了在把握力和手指关节扭矩估计的可能性。