A visual servoing algorithm using fuzzy logics and fuzzy-neural networks

Abstract

A visual servoing algorithm is proposed for a robot with a camera in hand, where fuzzy logics and fuzzy-neural networks are employed to represent and/or learn camera motion commands to track a moving object in terms of image features and their variations. Specifically, novel image features are suggested by employing a viewing model of perspective projection to estimate relative pitching and yawing angles between the object and the camera. And, owing to the uniqueness of the proposed image features, at most two input variables are shown to be sufficient for the design of fuzzy logics and/or fuzzy-neural networks. To compensate dynamic characteristics of the robot, desired feature trajectories for the learning of visually guided line-of-sight robot motion are obtained by measuring features by the camera in hand not in the entire workspace, but on a single linear path along which the robot moves under the control of a commercially provided function of linear motion. And then, control actions of the camera are approximately found by fuzzy neural networks to follow such desired feature trajectories. To show the validity of proposed algorithm, some experimental results are illustrated, where a four axis SCARA robot with a B/W CCD camera is used.