Adaptive Visual Servoing of Contour Features

In vision-based robotic manipulation, it is usually required that the object should present a desired shape or be viewed at a specific angle to facilitate subsequent operations such as object crawling and component inspection. To implement this kind of visual servoing tasks, image features that describe shape information of objects need to be designed. This paper proposes methods based on the general contour of an object. The Bezier curve and the nonuniform rational B-spline curve are, respectively, used to fit the contour of the object. Based on contour curve parameters, image contour features are designed. Visual measurements of the image features are used to provide feedback on the shape information of the object to the eye-in-hand visual servoing system. Based on the visual feedback, the motion of the robot is controlled to make the image features converge to the desired values. In this way, it is implemented that the object presents the desired shape or is viewed at the desired angle. Moreover, by using adaptive laws to estimate curve parameters online, the proposed method does not require a priori knowledge of the 3-D position of the object. The stability of the proposed controller is analyzed by Lyapunov theory. This method has substantially expanded the application domain of not only the image feature-based visual servoing, but also the B-spline curves. The feasibility of the proposed method is validated by experiments.