Adaptive Visual Servoing of Contour Features (original) (raw)

2018, IEEE/ASME Transactions on Mechatronics

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 Non-Uniform Rational B-Spline (NURBS) 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 3D 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. Index Terms-visual shape servoing, curve image feature, Bezier curve, B-spline curve Ⅰ. INTRODUCTION Visual servoing is widely used because of the high reliability, the wide availability and the low-cost feature of visual sensors. It can be used for many practical tasks like detection, robot inspection and trajectory tracking. One of the key problems of visual servoing is the selection of image features, which can be used to describe the environment and to define control tasks. The most commonly used image features are point features such as feature points [1, 2], hole points [3], mass center [4] and other artificially marked points [5, 6]. Other geometric features, such as line features [7] and ellipse features