Fuzzy behavior-based control trained by module learning to acquire the adaptive behaviors of mobile robots

Intelligent control techniques for robotic systems have been used with some success in a wide variety of applications. In this paper, we construct a method for the intelligent control system of a robot using the fuzzy behavior-based control, which decomposes the control system into several elemental behaviors, and each one is realized by fuzzy reasoning. In particular, a module learning method is investigated for obtaining each representative group behavior, so that the robot can, consequently, acquire more general knowledge or fuzzy reasoning, than a central learning method. The proposed method is applied for an obstacle-avoidance problem of a mobile robot; the effectiveness of the method is illustrated through some simulations.