人工智能
机器人
计算机科学
控制器(灌溉)
方向(向量空间)
人工神经网络
计算机视觉
运动学
对象(语法)
先验与后验
机器人学
非线性系统
机械臂
集合(抽象数据类型)
控制理论(社会学)
控制工程
控制(管理)
工程类
数学
几何学
农学
程序设计语言
哲学
物理
认识论
生物
经典力学
量子力学
出处
期刊:IEEE Transactions on Systems, Man, and Cybernetics
[Institute of Electrical and Electronics Engineers]
日期:1989-01-01
卷期号:19 (4): 825-831
被引量:214
摘要
A practical neural network-based learning control system is described that is applicable to complex robotic systems involving multiple feedback sensors and multiple command variables. In the controller, one network is used to learn to reproduce the nonlinear relationship between the sensor outputs and the system command variables over particular regions of the system state space. The learned information is used to predict the command signals required to produce desired changes in the sensor outputs. A second network is used to learn to reproduce the nonlinear relationship between the system command variables and the changes in the video sensor outputs. The learned information from this network is then used to predict the next set of video parameters, effectively compensating for the image processing delays. The results of learning experiments using a General Electric P-5 manipulator are presented. These experiments involved control of the position and orientation of an object in the field of view of a video camera mounted on the end of the robot arm, using moving objects with arbitrary orientation relative to the robot. No a priori knowledge of the robot kinematics or of the object speed of orientation relative to the robot was assumed. Image parameter uncertainty and control system tracking error in the video image were found to converge to low values within a few trials.< >
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