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Online-estimation of Image Jacobian based on adaptive Kalman filter
... Another focus of the visual servo community has been in estimation of the Image Jacobian. [9][10][11][12][13] have showcased several analytical methods to estimate the image Jacobian matrix. More recently, the focus of this field of research has been in leveraging deep learning and statistical concepts in the Image Jacobian estimation [14,15]. ...
In industry, several operations require sheet-like materials to be transported from a loading station to the desired location. Such applications are prevalent in the aerospace and textile industry where composite prepreg sheets or fabrics are placed over a tool or fed to a machine. Using robots for sheet transport operations offers a flexible solution for such highly complex tasks. To create high-quality parts, sheets need to be accurately placed at the correct location. This paper presents automated trajectory planning and control algorithms for a robot to pick up sheets from the input station using suction grippers and, transport and place them over the tool surface. Machine vision is used at the pick location for estimating the sheet pose. Unfortunately, pick-up accuracy is not sufficiently high due to sheet movement during suction-based grasping and localization errors. We employ ideas inspired by visual servo techniques to accurately place the sheet on the tool. Our method uses an Eye-to-Hand camera configuration to align the desired image features with the reference markings on the tool. We introduce a sampling-based Jacobian estimation scheme that can reliably achieve the desired accuracy while minimizing the operation time. Experiments are performed to validate our methodology and compute the placement accuracy on an industrial tool.
Uncalibrated visual servoing control still encounters some challenges, such as calculating the interaction matrix with less cost and keeping the current image features within a camera’s field of view (FOV) in a noisy system environment. To solve these problems, we propose a new control method that uses a random vector functional link network with L21 norm regularization to calculate the interaction matrix and further estimate it with a robust information filter (RIF). L21 norm regularization can deal with the global sparsity of input weights and reduce the inherent complexity of a model. The RIF limits noise variance within a certain range to reduce the influence of uncertain noise on the servoing task. We also design a method that reacts to the control law in accordance with the coordinates of image features. It can adjust running speed in real time and keep image features within a camera’s FOV. We apply this method to a six-degrees-of-freedom eye-in-hand manipulator, and several simulations are performed. Simulation results show that the proposed algorithm performs well in the task and achieves good performance in terms of noise resistance. Image features barely escape from the camera’s FOV through the proposed constraint method.
A major challenge in solving robot visual servoing problems with unstructured environments is to obtain image Jacobian matrix, especially non-Gaussian noise always exist in the whole process. However, the standard Kalman Filter (KF) is exhausted to find the optimal value under Gaussian noise assumption. In this paper, a Kalman filter which adopted the maximum correntropy criterion (MCC) instead of the minimum mean square error (MMSE) criterion is proposed to solve the approximation issue of the image Jacobian. The simulation and experiment results using a conventional 6R manipulator are conducted to verify the effectiveness of the proposed method.
This article provides a tutorial introduction to visual servo
control of robotic manipulators. Since the topic spans many disciplines
our goal is limited to providing a basic conceptual framework. We begin
by reviewing the prerequisite topics from robotics and computer vision,
including a brief review of coordinate transformations, velocity
representation, and a description of the geometric aspects of the image
formation process. We then present a taxonomy of visual servo control
systems. The two major classes of systems, position-based and
image-based systems, are then discussed in detail. Since any visual
servo system must be capable of tracking image features in a sequence of
images, we also include an overview of feature-based and
correlation-based methods for tracking. We conclude the tutorial with a
number of observations on the current directions of the research field
of visual servo control
The classical filtering and prediction problem is re-examined using the Bode-Sliannon representation of random processes and the “state-transition” method of analysis of dynamic systems. New results are: (1) The formulation and methods of solution of the problem apply without modification to stationary and nonstationary statistics and to growing-memory and infinitememory filters. (2) A nonlinear difference (or differential) equation is derived for the covariance matrix of the optimal estimation error. From the solution of this equation the coefficients of the difference (or differential) equation of the optimal linear filter are obtained without further calculations. (3) The filtering problem is shown to be the dual of the noise-free regulator problem. The new method developed here is applied to two well-known problems, confirming and extending earlier results. The discussion is largely self-contained and proceeds from first principles; basic concepts of the theory of random processes are reviewed in the Appendix.
Tracking of two Dimensional Based On Kalman Filtering[J]
- Liu Guang-Rui
- Huang Zhen
- Han Jing-Kai
- Bi
Optimal state estimation and system identification [M]
- Zhi-Xian
- Wang
Simulation Analysis of Target Tracking Method Based on Unscented Kalman Filter [J]
- Shi Shao-Wang
- Shou-Lin
- Tan
Nonlinear Filtering Theory with Target Tracking Application [M]
- Rong-Hui Zhan
- Jun
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