Non-vector space visual servoing for multiple pin-in-hole assembly by robot

Liu, Chicheng; Xu, Jing; Zhao, Jianguo; Chen, Heping; Xi, Ning; Chen, Ken

doi:10.1109/arso.2016.7736270

Cited by 7 publications

(4 citation statements)

References 12 publications

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“…For this method, only a simple segmentation to convert gray images to binary images is required; in this case, the texture features and the tracking and matching of geometric features are not required. Moreover, compared with our previous work (Liu et al, 2016), a simple outlier rejection method is sufficient to handle image defects in the set space, which makes the proposed method robust against image defects. In addition, an auto-adaptive algorithm is also proposed to increase the efficiency.…”

Section: Introductionmentioning

confidence: 92%

Image-based visual servoing using a set for multiple pin-in-hole assembly

Liu

Song

Chen

et al. 2019

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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.

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Section: Introductionmentioning

confidence: 92%

Image-based visual servoing using a set for multiple pin-in-hole assembly

Liu

Song

Chen

et al. 2019

Self Cite

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“…1) Coaxial Error Detection: The proposed method was originally designed to solve the coaxial error detection problem in pin-in-hole assembly tasks [52]. In automated pin-in-hole assembly tasks, the coaxial error of the holes on different lugs must be measured in a timely manner.…”

Section: Applicationsmentioning

confidence: 99%

Self-Supervised Learning for Specified Latent Representation

Liu

Song

Zhang

et al. 2020

IEEE Trans. Fuzzy Syst.

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Current latent representation methods using unsupervised learning have no semantic meaning; thus, it is difficult to directly express their physical task in the real world. To this end, this paper attempts to propose a specified latent representation with physical semantic meaning. First, a few labeled samples are used to generate the framework of the latent space, and these labeled samples are mapped to framework nodes in the latent space. Second, a self-learning method using structured unlabeled samples is proposed to shape the free space between the framework nodes in the latent space. The proposed specified latent representation therefore possesses the advantages provided by both supervised and unsupervised learning. The proposed method is verified by numerical simulations and real-world experiments.

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“…Knowing spatial location of an object in a scene has several applications, particularly, in the field of robotics where visual servoing is usually employed which consists in using visual information obtained from one or more cameras to locate the end effector of a robot in a wanted position [1] and even to follow a trajectory tracking of a movable object [2]. Visual control applications at industrial level have focused on tasks of welding [3] and assembly [4], but there are also important advances in the applications found in other fields as medicine, some examples are presented in [5] and [6]. In literature, visual control diagrams are classified according the camera's location: eye-in-hand camera [7] and eye-to-hand camera [8]; but also in: image-based visual servoing (IBVS) and position-based visual servoing (PBVS) [9], in the first one, the data feedback consists of image characteristics, whereas in the second one information about the actual location of an object is required.…”

Section: Introductionmentioning

confidence: 99%

A comparison of an RGB-D cameras performance and a stereo camera in relation to object recognition and spatial position determination

Rodríguez

2021

ELCVIA

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Results of using an RGB-D camera (Kinect sensor) and a stereo camera, separately, in order to determine the 3D real position of characteristic points of a predetermined object in a scene are presented. KAZE algorithm was used to make the recognition, that algorithm exploits the nonlinear scale space through nonlinear diffusion filtering; 3D coordinates of the centroid of a predetermined object were calculated employing the camera calibration information and the depth parameter provided by a Kinect sensor and a stereo camera. Other comparisons have been made using different types of cameras similar to those used in this work, however, a conclusion of the best performance depends on the specific application, for example, it has been shown that for 3D surface reconstruction, the Intel RealSense D415 camera has higher precision than the Kinect. Experimental results of this work show it is possible to get the required coordinates with both cameras in order to locate a robot, although a balance in the distance where the sensor is placed must be guaranteed: no fewer than 0.8 m from the object to guarantee the real depth information, it is due to Kinect operating range; 0.5 m to stereo camera, but it must not be 1 m away to have a suitable rate of object recognition, however, without loss of generality it can be concluded that the Kinect presents greater precision in the distance measurements with respect to the stereo camera.

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Non-vector space visual servoing for multiple pin-in-hole assembly by robot

Cited by 7 publications

References 12 publications

Image-based visual servoing using a set for multiple pin-in-hole assembly

Image-based visual servoing using a set for multiple pin-in-hole assembly

Self-Supervised Learning for Specified Latent Representation

A comparison of an RGB-D cameras performance and a stereo camera in relation to object recognition and spatial position determination

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