Self-Organization of Associative Data-Base and Application to Autonomous Mobile Intelligent Robot

Abstract

A simple and efficient method of self-organizing associative data-bases is proposed with a demonstration of application to an autonomous mobile intelligent robot. The system consists of a processing block prescribed and a memory block being blank at the initial state. For a current input signal, the processing block searches for a past input signal that is similar to the current one in the memory block, and recalls the referred information. If the information accompanied with the current input signal does not agree with the recalled one, the new information is added to a data-base in the memory block, with influence reflects on the processing at the next time. Thus, the data-base grows dependently on its states and input signals in a self-organizing way. Since carrier of the data-base organization depends on the series of inputs, each data-base acquires an individual structure. If an input series of sufficient length is given, the relation between input signals and the referred information tends to agree with that of the input series. This system is meaningful in the sense that it gives a conceptual but essential model of intuitive information processing in human's brain and, from a practical viewpoint, in the sense that this system is applicable to a learning mechanism that learns universally any input-output response desired. A camera-type autonomous mobile robot for indoor passages utilizing this learning mechanism is introduced. The brain of robot is constructed on a microcomputer of processor i80286, and behavior of the robot is controlled with a small-size robot language that is supplemented to C-language. A successful experiment result is shown.