Synthesis of Fuzzy , Artificial Intelligence , Neural Networks , and Genetic Algorithm for Hierarchical Intelligent Control - Top - Down and Bottom - Up Hybrid Method -

Autonomous robots, which perform tasks without human operators, are required in many fields. The autonomous robots have to carry out tasks in various environments by themselves like human beings. They have to be intelligent to determine their own actions in unknown environments by themselves based on sensory information. In advance, human operators can give the robots their knowledge and skill to some extent in top-down manner. However, when the robots perform tasks in unknown environment, the knowledge may not be useful. In this case, the robots have to adapt to their environments and acquire new knowledge by themselves through learning. This process proceeds in bottom-up manner. This paper introduces a control scheme for autonomous robots, which this paper refers to as hierarchical intelligent control scheme (Fig. 1). The hierarchical intelligent control consists of three levels: adaptation level, skill level and learning level. This scheme has two characteristics with respect to learning process: top-down approach and bottom-up approach. To link three levels and have such characteristics, the scheme uses artificial intelligence (AI), fuzzy logic, neural networks (NN) and genetic algorithm (GA) [l 31. Each technique has advantages and disadvantages. In order to overcome the disadvantages, this paper introduces synthesis techniques of them. Those are key techniques for intelligent control of robots.