A visual control system using image processing and fuzzy inference

A control system for an unmanned vehicle is constructed by combining dynamic image processing and fuzzy inference. The features of this system are that an object on the road can be recognized with a high speed, and the unmanned vehicle runs according to the result of recognition. First, the object to follow is extracted by the image processor from a tremendous amount of image data containing noises. The result is then transformed, using the improved Hough transform, into the information concerning the position and the orientation, which are needed in the steering of the vehicle. The information is then compared to the expertise of the vehicle steering in the fuzzy controller, and the handle variables are determined. The image processor executes the pipeline processing using the analog color extractor and the logical filtering processor with the local parallel pipeline architecture. Sixty images per second can be processed in real-time. The fuzzy inference has the fuzzy rulebase with the motion information of the vehicle as the input parameter so that the handle variables can be determined even for a moving vehicle. By an LSI implementation of the image processor, the proposed system is realized as a small-scale system which can be mounted on the vehicle (A4 size × 10 cm). In the unmanned control experiment using a vehicle with the proposed system, a high-speed processing of 100 ms on the average from the image input to the handle operation is verified. The follow-up to the series of objects within the error of 5 cm is verified, indicating the practical usefulness of the proposed system.