Application of circular laser vision sensor (CLVS) on welded seam tracking

2008 ◽  
Vol 205 (1-3) ◽  
pp. 404-410 ◽  
Author(s):  
Peiquan Xu ◽  
Xinhua Tang ◽  
Shun Yao
Author(s):  
Taewook Kim ◽  
Seungbeom Lee ◽  
Seunghwan Baek ◽  
Kwangsuck Boo

Author(s):  
Chao Liu ◽  
Hui Wang ◽  
Yu Huang ◽  
Youmin Rong ◽  
Jie Meng ◽  
...  

Abstract Mobile welding robot with adaptive seam tracking ability can greatly improve the welding efficiency and quality, which has been extensively studied. To further improve the automation in multiple station welding, a novel intelligent mobile welding robot consists of a four-wheeled mobile platform and a collaborative manipulator is developed. Under the support of simultaneous localization and mapping (SLAM) technology, the robot is capable of automatically navigating to different stations to perform welding operation. To automatically detect the welding seam, a composite sensor system including an RGB-D camera and a laser vision sensor is creatively applied. Based on the sensor system, the multi-layer sensing strategy is performed to ensure the welding seam can be detected and tracked with high precision. By applying hybrid filter to the RGB-D camera measurement, the initial welding seam could be effectively extracted. Then a novel welding start point detection method is proposed. Meanwhile, to guarantee the tracking quality, a robust welding seam tracking algorithm based on laser vision sensor is presented to eliminate the tracking discrepancy caused by the platform parking error, through which the tracking trajectory can be corrected in real-time. The experimental results show that the robot can autonomously detect and track the welding seam effectively in different station. Also, the multiple station welding efficiency can be improved and quality can also be guaranteed.


2015 ◽  
Vol 1088 ◽  
pp. 819-823
Author(s):  
Min Ho Park ◽  
Qian Qian Wu ◽  
Cheol Kyun Park ◽  
Jong Pyo Lee ◽  
Ill Soo Kim

With the development of economy, the seam tracking technology for arc welding becomes one of the major research tasks in the manufacturing area with robots. In this study, the objectives aim to develop an intelligent and cost-effective algorithm based on the laser vision sensor for image processing in Gas Metal Arc (GMA) welding. Images of welded seam were captured from the CCD camera. These images were then processed by the algorithms in the proposed image processing. To optimize the effective image process, popular algorithms in use were verified, compared and finally selected for every step in the image processing. Moreover, owing to the simple interactive environment and abundant toolboxes, MATLAB was employed to realize those algorithms, which offer a sample for engineers to achieve the goal of algorithm developed by this new but easier approach. Finally, weld seam images obtained with different welding environments were processed to enhance the proposal validity, and it’s proved to have significant effect of getting rid of the variable noises to extract the feature points and centerline for seam tracking in GMA welding and is capable for industrial application.


Measurement ◽  
2018 ◽  
Vol 127 ◽  
pp. 489-500 ◽  
Author(s):  
Yanbiao Zou ◽  
Xiangzhi Chen ◽  
Guoji Gong ◽  
Jinchao Li

2007 ◽  
Vol 21 (10) ◽  
pp. 1720-1725 ◽  
Author(s):  
K. Park ◽  
Y. Kim ◽  
J. Byeon ◽  
K. Sung ◽  
C. Yeom ◽  
...  

Author(s):  

An algorithm for tracking of the welded seams grooving by using a Kalman filter based on six characteristic points of the profile obtained using the RF627 laser vision sensor is proposed. In order to reduce the error in weld seams control, a multilayer neural network with a backpropagation algorithm is created to compensate for errors caused by colored noise when using the Kalman filter. Experimental results show that when the algorithm is applied, the error in tracking the trajectory of weld seams is reduced. Keywords tracking of weld seams; multilayer/multi-pass welding; Kalman filter; multilayer perceptron


2004 ◽  
Vol 270-273 ◽  
pp. 2332-2337 ◽  
Author(s):  
H. Lee ◽  
K. Sung ◽  
H. Park ◽  
Se Hun Rhee

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