Robot Motion Similarity Analysis Using an FNN Learning Algorithm

• Main Authors: Wang, Jyh-Kao, 王治國
• Other Authors: Kuu-Young Young
• Format: Others
• Language: zh-TW
• Published: 1996
• Online Access: http://ndltd.ncl.edu.tw/handle/86507178255021148032

碩士 === 國立交通大學 === 控制工程系 === 84 === In the application of learning control for robot motion governing, learningcontrollers are usually used as subordinates to conventional controllers, although they are considered to be capable of generalization. One reason is that when a learning controller alone is applied to govern general motions ofmulti- joint robot manipulators, the learning space encountered will be extremely complicated, due to the variations exhibited in motions corresponding to different task requirements. Hence, in this thesis, we first discuss the generalization capability in different levels to find what level command is with the bestgeneralization effect. In addition, in order to reduce the complexity of the learning space for robot learning control, we propose to perform similarity analysis for robot motions by using an FNN learning algorithm, such that robot motions can be classified according to their similarity. In the analysis,the FNN is first used to learn to govern various robot motions, and the similarity between motions is then evaluated according to the number of linguistic labels and the shape of the membership functions of the FNN under successful motion governing. Thus, groups of robot motions with high similarity can be governed by using learning controllers with reasonable sizes, because these motions correspond to similar fuzzy parameters in the FNN, implicating a simplified learning space.