Controlling a robot's position using neural networks

被引：2
|
作者：
Wu, C. M. [1 ]
Jiang, B. C. [1 ]
Shiau, Y. R. [1 ]
机构：
[1] Auburn Univ, Dept Ind Engn, Auburn, AL 36849 USA
来源：
INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY | 1993年 / 8卷 / 04期
关键词：
fine motion; gross motion; modified two-layer counterpropagation network; robot learning; robot process capability;
D O I：
10.1007/BF01748631
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
In order to fully utilise the power of robots in factories, robot process capability (RPC) must be considered and improved. To improve the RPC in on-line processing by applying robot learning, the counterpropagation network was modified in this research. With two layers, the counterpropagation network was modified to control a robot's gross and fine motions. For the first layer, the network serves as a sensor-signal generator to control the gross motion. For the second layer, the network serves as a fine motion adjuster. Also, each layer can be separated functionally. By controlling both the gross and the fine motions, the RPC can then be improved. The modified two-layer counterpropagation network control scheme was validated by computer simulation and physical implementation on a RS-2200 robot system.
引用
收藏
页码：216 / 226
页数：11
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