3D image-based dynamic visual servoing with uncalibrated stereo cameras

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 pixels). 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 control is designed to track 3D visual motions using the 3D trajectory errors defined in the Visual Cartesian Space, where a Torque to Position Model is designed to allow the implementation of joint torque control techniques on joint position-controlled robots. This approach has been experimentally implemented on a real industrial robot where exponential convergence of errors in the Visual Cartesian Space and Task space without local minima are demonstrated. This approach offers a proper solution for the common problem of visual occlusion, since the stereo system can be moved manually to obtain a clear view of the task at any time.