Uncalibrated 3D stereo image-based dynamic visual servoing for robot manipulators

This paper introduces a new comprehensive solution for the open problem of uncalibrated 3D image-based stereo visual servoing for robot manipulators. One of the main contributions of this article is a novel 3D stereo camera model to map positions in the task space to positions in a new 3D Visual Cartesian Space (a visual feature space where 3D positions are measured in pixel). This model is used to compute a full-rank Image Jacobian Matrix (J_img), which solves several common problems presented on the classical image Jacobians, e.g., image space singularities and local minima. This Jacobian is a fundamental key for the image-based control design, where uncalibrated stereo camera systems can be used to drive a robot manipulator. Furthermore, an adaptive second order sliding mode visual servo control is designed to track 3D visual motions using the 3D trajectory errors defined in the Visual Cartesian Space. The stability of the control in closed loop with a dynamic robot system is formally analyzed and proved, where exponential convergence of errors in the Visual Cartesian Space and task space without local minima are demonstrated. The complete control system is evaluated both in simulation and on a real industrial robot. The robustness of the control scheme is evaluated for cases where the extrinsic parameters of the stereo camera system change on-line and the kinematic/dynamic robot parameters are considered as unknown. This approach offers a proper solution for the common problem of visual occlusion, since the stereo system can be moved to obtain a clear view of the task at any time.