权益分类	功能权益	普通用户	{{item.name}}会员
{{category.name}}	{{benefitItem.name}}

Neural Network Model for Voluntary Movement and Application to Robotics

自主运动神经网络模型及其在机器人中的应用

基本信息

批准号：
62490011
负责人：
SUZUKI Ryoji
金额：
$ 5.12万
依托单位：
The University of Tokyo
依托单位国家：
日本
项目类别：
Grant-in-Aid for General Scientific Research (B)
财政年份：
1987
资助国家：
日本
起止时间：
1987 至 1989
项目状态：
已结题

来源：
https://kaken.nii.ac.jp/en/grant/KAKENHI-PROJECT-62490011/
关键词：
Voluntary Arm Movement Neural Network Manipulator Robot Control Inverse Dynamics of Motor System Inverse Kinematics of Motor System Feedback-Error-Learning Optimal Trajectory Formation Minimum Torque-Change Criterion 随意運動ニューラルネットワーク誤

项目摘要

Human motor skills are not innate, but have to be acquired by training from birth. In the beginning, movements are controlled by using feedback signals through visual pathways. With increased skill, the feedback control system is replaced as the main controller by a feedforward control system which means movements can be control led unconsciously. A neural network model which can explain this process is proposed. The model is based on a rule called the feedback-error-learning. The inverse dynamics of the motor system is organized in a three layer neural network according to the back-propagation learning rule.Optimal control of human arm movement is also discussed , and a neural network model which realizes the optimal pathways based on the minimum torque change criterion is proposed. Basic ideas are (1) spatial representation of time, (2) learning of forward dynamics and kinematics model and (3) relaxation computation based on the acquired model. Operations of this network are divided into the learning phase and the pattern-generating phase. In the learning phase, this network acquires a forward model of the multi-degree-of-freedom controlled object while monitoring the actual trajectory as a teaching signal. In the pattern-generating phase, electrical coupling between neurons representing motor commands at neighboring times is activated to guarantee the minimum torque-change criterion. By computer simulation, we show that the model can produce a multi-joint arm trajectory while avoiding obstacles or passing through viapoints.

人类的运动技能不是天生的，而是必须通过与生俱来的训练获得的。最初，运动是通过视觉通路的反馈信号来控制的。随着技能的提高，反馈控制系统被前馈控制系统取代为主控制器，这意味着运动可以在无意识中被控制。提出了一个可以解释这一过程的神经网络模型。该模型基于一个称为反馈错误学习的规则。将电机系统的逆动力学按照反向传播学习规则组织成一个三层神经网络，并讨论了手臂运动的最优控制，提出了一种基于最小转矩变化准则实现最优路径的神经网络模型。基本思想是（1）时间的空间表示，（2）正向动力学和运动学模型的学习和（3）基于所获得的模型的松弛计算。该网络的操作分为学习阶段和模式生成阶段。在学习阶段，该网络获取多自由度被控对象的前向模型，同时监测作为示教信号的实际轨迹。在模式生成阶段，在相邻时间代表电机命令的神经元之间的电耦合被激活，以保证最小的扭矩变化标准。通过计算机仿真，我们表明，该模型可以产生一个多关节的手臂轨迹，同时避免障碍物或通过过境点。