We introduce a robust controller for kernel-based visual servoing systems. In such systems, visual features are sum of weighted-image intensities via smooth kernel functions. This information along with its derivative are input to the controller in which we have developed a sliding mode approach to generate system commands. In vision-based systems, image uncertainties affect the tracking performance and stability, and the target object may get out of the field of view. Unless considerable image uncertainties appear, such systems are able to track the object within desired precision. Hence, we have investigated the effect of the image noise as the main source of uncertainty, and encapsulated its characteristics in a proper representation. In order to fulfill the sliding condition, some bounds over image uncertainty and tracking errors are considered, and the controller gains are tuned online to keep the tracking error bounded. An application of the proposed method is experimentally tested on an industrial robot.

In this study, a new approach to design a controller for a visual servoing (VS) system is proposed. Kernel-measurement is used to track the motion of a featureless object which is defined as sum of weighted-image value through smooth kernel functions. This approach was used in kernel-based VS (KBVS). To improve the tracking error and expand the stability region, sliding mode control is integrated with kernel measurement. Proportional-integral-type sliding surface is chosen as a suitable manifold to produce the required control effort. Moreover, the stability of this algorithm is analysed via Lyapunov theory and its performance is verified experimentally by implementing the proposed method on a five degrees of freedom industrial robot. Through experimental results, it is shown that the performance of tracking error in the proposed method is more suitable than KBVS, for a wider workspace and when the object is placed near the boundary of the camera's field of view.

The performance of visual servoing systems can be enhanced through nonlinear controllers. In this paper, a sliding mode control is employed for such a purpose. The controller design is based on the outputs of a pose estimator which is implemented on the scheme of the Position-Based Visual Servoing (PBVS) approach. Accordingly, a robust estimator based on unscented Kalman observer cascading with Kalman ?lter is used to estimate the position, velocity and acceleration of the target. Therefore, a PD-type sliding surface is selected as a suitable manifold. The combination of the estimator and nonlinear controller provides a robust and stable structure in PBVS approach. The stability analysis is veri?ed through Lyapunov theory. The performance of the proposed algorithm is veri?ed experimentally through an industrial visual servoing system.

This paper presents a nonlinear controller for visual servoing system. Pose estimation is one of the fundamental issues in position-based visual servoing (PBVS) approach. A few researches have focused on controller synthesis under modeling uncertainty and measurement noise of estimated position. In this research, PD-type sliding surface is designed for tracking target. The control signal is obtained from the sliding surface and the stability of the algorithm is verified by Lyapunov theory. Moreover, a recent designed robust estimator based on unscented Kalman observer (UKO) cascading with Kalman filter (KF) is used to estimate the pose, velocity and acceleration of the target. The combination of the implemented estimator and the proposed controller provide a stable and robust structure in PBVS. The reported experimental results, verify the effectiveness of the proposed method in an industrial visual servoing system.

This paper presents a nonlinear controller for visual servoing system. Pose estimation is one of the fundamental issues in position-based visual servoing (PBVS) approach. A few researches have focused on controller synthesis under modeling uncertainty and measurement noise of estimated position. In this research, PD-type sliding surface is designed for tracking target. The control signal is obtained from the sliding surface and the stability of the algorithm is verified by Lyapunov theory. Moreover, a recent designed robust estimator based on unscented Kalman observer (UKO) cascading with Kalman filter (KF) is used to estimate the pose, velocity and acceleration of the target. The combination of the implemented estimator and the proposed controller provide a stable and robust structure in PBVS. The reported experimental results, verify the effectiveness of the proposed method in an industrial visual servoing system.

Three-dimensional (3D) pose estimation of a rigid object by only one camera has a vital role in visual servoing systems, and extended Kalman filter (EKF) is vastly used for this task in an unstructured environment. In this study, the stability of the EKF-based 3D pose estimators is analysed in detail. The most challenging issue of the state-of-the-art EKF-based 3D pose estimators is the possibility of its divergence because of the measurement and model noises. By analysing the stability of conventional EKF-based pose estimators a composite technique is proposed to guarantee the stability of the procedure. In the proposed technique, the non-linear-uncertain estimation problem is decomposed into a non-linear-certain observation in addition to a linear-uncertain estimation problem. The first part is handled using the extended Kalman observer and the second part is accomplished by a simple Kalman filter. Finally, some experimental and simulation results are given in order to verify the robustness of the method and compare the performance of the proposed method in noisy and uncertain environment to the conventional techniques.