Measurement errors in visual servoing

IEEE Transactions on Robotics, 2000

This paper concerns the stability analysis of imagebased visual servoing control laws with respect to uncertainties on the 3-D parameters needed to compute the interaction matrix for any calibrated central catadioptric camera. In the recent past, research on image-based visual servoing has been concentrated on potential problems of stability and on robustness with respect to camera-calibration errors. Only little attention, if any, has been devoted to the robustness of image-based visual servoing to estimation errors on the 3-D structure. It is generally believed that a rough approximation of the 3-D structure is sufficient to ensure the stability of the control law. In this paper, we prove that this is not always true and that an extreme care must be taken when approximating the depth distribution to ensure stability of the image-based control law. The theoretical results are obtained not only for conventional pinhole cameras but for the entire class of central catadioptric systems as well.

Proceedings 2000 ICRA. Millennium Conference. IEEE International Conference on Robotics and Automation. Symposia Proceedings (Cat. No.00CH37065)

We describe in this paper potential problems that may appear in image-based visual servoing when the initial camera position is far away from its desired position. We show by concrete examples that local minima or a singularity of the image Jacobian can be reached during the servoing. We then recall recent results obtained to avoid these drawbacks. It consists in combining visual features obtained directly from the image, and position-based features. This approach, called 2 1/2 D visual servoing, also provides supplementary advantages in function of the mean the features are combined with.

In image-based visual servoing (IBVS), the control law is based on the error between the current and desired features on the image plane. The visual servoing system is working well only when all the designed features are correctly extracted. To monitor the quality of feature extraction, in this paper, a condition monitoring scheme is developed. First, the failure scenarios of the visual servoing system caused by incorrect feature extraction are reviewed. Second, we propose a residual generator, which can be used to detect if a failure occurs, based on the Kalman filter. Finally, simulation results are given to verify the effectiveness of the proposed method.

Proceedings 2007 IEEE International Conference on Robotics and Automation, 2007

Calibration techniques allow the estimation of the intrinsic parameters of a camera. This paper describes an adaptive visual servoing scheme which employs the visual data measured during the task to determine the camera intrinsic parameters. This approach is based on the virtual visual servoing approach. However, in order to increase the robustness of the calibration several aspects have been introduced in this approach with respect to the previous developed virtual visual servoing systems. Furthermore, the system is able to determine the value of the intrinsic parameters when they vary during the task. This approach has been tested using an eye-in-hand robotic system. 1

2009

In this paper an image controller using model predictive control (MPC) for visual servoing systems is presented and its performances are analyzed. An eye-inhand type servoing architecture, composed from a 6 degree of freedom robot and a camera mounted on the gripper is considered. Modeling the visual servo open loop is composed from two stages: first the designing of a model for dynamics of the velocity controlled robot and second a model of the visual sensor. In classical approach, an image interaction matrix maps image space errors into errors in Cartesian space. For the MPC approach, using the open loop visual servo model, an image based predictor is developed. The image based predictor generates the future trajectories of a visual feature ensemble when past and future camera velocities are known. In order to obtain better performances for visual servoing systems an advanced technique is required, for this mater using the developed predictor an image based predictive controller (...

2003

Abstract In recent papers, a new class of model-free (ie, the 3-dimensional task-space model of the object is unknown) visual servoing methods was proposed that are based on the estimation of the relative camera orientation between two views of an object. By utilizing homography-based techniques, the control problem is decoupled by separating the rotation and translation components. A single controller is used to control the rotation component, and the class members consist of various translation controllers.

IEEE Transactions on Robotics, 2012

This paper addresses the problem of stabilizing a robot at a pose specified via a reference image. Specifically, this paper focuses on six degrees-of-freedom visual servoing techniques that require neither metric information of the observed object nor precise camera and/or robot calibration parameters. Not requiring them improves the flexibility and robustness of servoing tasks. However, existing techniques within the focused class need prior knowledge of the object shape and/or of the camera motion. We present a new visual servoing technique that requires none of the aforementioned information. The proposed technique directly exploits 1) the projective parameters that relate the current image with the reference one and 2) the pixel intensities to obtain these parameters. The level of versatility and accuracy of servoing tasks are, thus, further improved. We also show that the proposed nonmetric scheme allows for path planning. In this way, the domain of convergence is greatly enlarged as well. Theoretical proofs and experimental results demonstrate that visual servoing can, indeed, be highly accurate and robust, despite unknown objects and imaging conditions. This naturally encompasses the cases of color images and illumination changes.

2013 ASME Dynamic Systems and Control Conference (DSCC 2013), 2013

This paper presents a control scheme of visual servoing. Real-time vision guidance is necessary in many desirable applications of industrial manipulators. Challenge comes from the limitations of visual sensing and robot dynamics. Typical industrial machine vision systems have low sampling rate and large latency. In addition, due to the large inertia of industrial manipulators, a proper consideration of robot dynamics is important. In particular, actuator saturation may cause undesirable response. In this paper, an adaptive tracking filter is used for sensing compensation. Based on the compensated vision feedback, a two-layer controller is formulated using multi-surface sliding control. System kinematics and dynamics are decoupled and handled by the two layers of the controller respectively. Further, a constrained optimal control approach is adopted to avoid actuator saturation. Validation is conducted using a SCARA robot.

2006

This paper describes a innovative algorithm for the on-line estimation of the image jacobian based on previous movements. It has been utilized for the tracking of an uncalibrated 3 DOF joint system, using two weakly calibrated fixed cameras. The algorithm incorporates the epipolar restriction of the two cameras conveyed in the fundamental matrix, and a reliability factor has been defined for the previous movements. The tests carried out show that the proposed algorithm is more robust in presence of noise than those obtained from already existing algorithms in specialized literature.

Industrial Robotics: Programming, Simulation and Applications, 2006