Visual Servoing: A 3D Alignment Task

Author(s):  
Johan Baeten ◽  
Joris De Schutter
2019 ◽  
Vol 40 (6) ◽  
pp. 819-831
Author(s):  
Chicheng Liu ◽  
Libin Song ◽  
Ken Chen ◽  
Jing Xu

Purpose This paper aims to present an image-based visual servoing algorithm for a multiple pin-in-hole assembly. This paper also aims to avoid the matching and tracking of image features and the remaining robust against image defects. Design/methodology/approach The authors derive a novel model in the set space and design three image errors to control the 3 degrees of freedom (DOF) of a single-lug workpiece in the alignment task. Analytic computations of the interaction matrix that link the time variations of the image errors to the single-lug workpiece motions are performed. The authors introduce two approximate hypotheses so that the interaction matrix has a decoupled form, and an auto-adaptive algorithm is designed to estimate the interaction matrix. Findings Image-based visual servoing in the set space avoids the matching and tracking of image features, and these methods are not sensitive to image effects. The control law using the auto-adaptive algorithm is more efficient than that using a static interaction matrix. Simulations and real-world experiments are performed to demonstrate the effectiveness of the proposed algorithm. Originality/value This paper proposes a new visual servoing method to achieve pin-in-hole assembly tasks. The main advantage of this new approach is that it does not require tracking or matching of the image features, and its supplementary advantage is that it is not sensitive to image defects.


2012 ◽  
Vol 26 (15) ◽  
pp. 1771-1797 ◽  
Author(s):  
Loïc Cuvillon ◽  
Edouard Laroche ◽  
Jacques Gangloff ◽  
Michel de Mathelin

2021 ◽  
Vol 11 (11) ◽  
pp. 4894
Author(s):  
Anna Scius-Bertrand ◽  
Michael Jungo ◽  
Beat Wolf ◽  
Andreas Fischer ◽  
Marc Bui

The current state of the art for automatic transcription of historical manuscripts is typically limited by the requirement of human-annotated learning samples, which are are necessary to train specific machine learning models for specific languages and scripts. Transcription alignment is a simpler task that aims to find a correspondence between text in the scanned image and its existing Unicode counterpart, a correspondence which can then be used as training data. The alignment task can be approached with heuristic methods dedicated to certain types of manuscripts, or with weakly trained systems reducing the required amount of annotations. In this article, we propose a novel learning-based alignment method based on fully convolutional object detection that does not require any human annotation at all. Instead, the object detection system is initially trained on synthetic printed pages using a font and then adapted to the real manuscripts by means of self-training. On a dataset of historical Vietnamese handwriting, we demonstrate the feasibility of annotation-free alignment as well as the positive impact of self-training on the character detection accuracy, reaching a detection accuracy of 96.4% with a YOLOv5m model without using any human annotation.


Actuators ◽  
2021 ◽  
Vol 10 (5) ◽  
pp. 105
Author(s):  
Thinh Huynh ◽  
Minh-Thien Tran ◽  
Dong-Hun Lee ◽  
Soumayya Chakir ◽  
Young-Bok Kim

This paper proposes a new method to control the pose of a camera mounted on a two-axis gimbal system for visual servoing applications. In these applications, the camera should be stable while its line-of-sight points at a target located within the camera’s field of view. One of the most challenging aspects of these systems is the coupling in the gimbal kinematics as well as the imaging geometry. Such factors must be considered in the control system design process to achieve better control performances. The novelty of this study is that the couplings in both mechanism’s kinematics and imaging geometry are decoupled simultaneously by a new technique, so popular control methods can be easily implemented, and good tracking performances are obtained. The proposed control configuration includes a calculation of the gimbal’s desired motion taking into account the coupling influence, and a control law derived by the backstepping procedure. Simulation and experimental studies were conducted, and their results validate the efficiency of the proposed control system. Moreover, comparison studies are conducted between the proposed control scheme, the image-based pointing control, and the decoupled control. This proves the superiority of the proposed approach that requires fewer measurements and results in smoother transient responses.


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