Adaptive visual servoing of robots in uncalibrated environments

Visual servoing is an approach to control motion of a robot manipulator using visual feedback signals from a vision system and has received extensive attention in recent years. Many existing methods work based on an assumption that the parameters of the vision system are accurately calibrated, while the calibration process is tedious. Furthermore, most of the controllers are designed using the kinematics relationship only, without considering the nonlinear dynamics of robots, so that they are not suitable for high performance and fast visual servoing tasks. Aiming at solving those two problems, this thesis addresses dynamic position and tracking control of robots with uncalibrated visual feedback. Both the fixed camera and eye-in-hand camera configurations are considered.;One of the major problems that obstruct the development of adaptive visual servoing is the fact that the image Jacobian or the interaction matrix cannot be linearly parameterized by the unknown parameters. To solve this problem, we propose a depth-independent interaction matrix, which is obtained by eliminating the depth in the traditional interaction matrix. Using this depth-independent interaction matrix in controller design, it is possible to make the unknown parameters appear linearly in the closed-loop dynamics. As a result, we can use an adaptive algorithm, similar to that proposed by Slotine and Li [1], to estimate the unknown parameters on-line. To guarantee the convergence of the image errors, in the parameter adaptation we combine the Slotine-Li algorithm with an on-line gradient descending minimization algorithm of the errors between the real and estimated image coordinates of the feature points. On the basis of the depth-independent interaction matrix and the new adaptive algorithm, we first propose an adaptive controller for image-based visual servoing of point features using both uncalibrated eye-in-hand and fixed cameras. Then, we extend the controller to visual servoing using line features with an eye-in-hand camera. Next, we present a dynamic controller for trajectory tracking of feature points on a robot manipulator in 3D general motion using fixed uncalibrated camera. To avoid performance decaying caused by measurement errors of the visual velocity, we also propose a new controller for dynamics visual tracking without using visual velocities. Finally, we design a new controller for locking a moving object in 3-D space at a particular position on the image plane of a camera mounted on a robot by actively moving the camera. The asymptotic stabilities of the system under the control of the proposed methods are rigorously proved by the Lyapunov theory with the nonlinear robot dynamics fully taken into account. The performances of the controllers have been verified by experiments on a 3 DOF robot manipulator.;The contribution of this thesis can be summarized as follows: First, a depth-independent interaction matrix is proposed for mapping the image errors onto the joint space. Second a new adaptive algorithm has been developed to estimate the unknown parameters. Finally, new methods to position and tracking control of robots with uncalibrated visual feedback in both eye-in-hand and fixed camera configuration are proposed.