Robot motion similarity analysis using an FNN learning mechanism

Learning controllers are usually subordinate to conventional controllers in governing multiple-joint robot motion, in spite of their ability to genera lize, because learning space complexity and motion variety require them to consume excessive amount of memory when they are employed as major roles in motion governing. We propose using a fuzzy neural network (FNN) to learn a nd analyze robot motions so that they can be classified according to simila rity. After classification, the learning controller can then be designed to govern robot motions according to their similarities without consuming exc essive memory resources. (C) 2001 Elsevier Science B.V. All rights reserved .