An initial point alignment method of narrow weld using laser vision sensor

Fan, Junfeng; Yang, Lei; Long, Teng; Tan, Min

doi:10.1007/s00170-018-3184-2

Cited by 12 publications

(5 citation statements)

References 24 publications

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“…In air, using Equation ( 11), the camera's field of view is calculated to be 173.9073 mm × 309.1704 mm. = 309.1704 mm (11) where W a and H a represent the width and height of the FOV, and w and h are the width and height of the sensor, respectively. f denotes the camera's focal length.…”

Section: Results Of the Experiments On Underwater Target Position Cal...mentioning

confidence: 99%

“…Alignment operations using vision sensors have been widely studied in various fields. For instance, Fan et al [11] proposed a laser-vision-sensor-based method for initial point alignment of narrow weld seams by utilizing the relationship between laser streak feature points and initial points. They obtain a high signal-to-noise image of the narrow weld seam using the laser vision sensor, and then calculate the 3D coordinates of the final image feature point and the initial point based on the alignment model.…”

Section: Introductionmentioning

confidence: 99%

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Automatic Alignment Method of Underwater Charging Platform Based on Monocular Vision Recognition

Wang

et al. 2023

JMSE

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To enhance the crypticity and operational efficiency of unmanned underwater vehicle (UUV) charging, we propose an automatic alignment method for an underwater charging platform based on monocular vision recognition. This method accurately identifies the UUV number and guides the charging stake to smoothly insert into the charging port of the UUV through target recognition. To decode the UUV’s identity information, even in challenging imaging conditions, an encryption encoding method containing redundant information and an ArUco code reconstruction method are proposed. To address the challenge of underwater target location determination, a target location determination method was proposed based on deep learning and the law of refraction. The method can determine the two-dimensional coordinates of the target location underwater using the UUV target spray position. To meet the real-time control requirements and the harsh underwater imaging environment, we proposed a target recognition algorithm to guide the charging platform towards the target direction. The practical underwater alignment experiments demonstrate the method’s strong real-time performance and its adaptability to underwater environments. The final alignment error is approximately 0.5548 mm, meeting the required alignment accuracy and ensuring successful alignment.

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Section: Results Of the Experiments On Underwater Target Position Cal...mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Automatic Alignment Method of Underwater Charging Platform Based on Monocular Vision Recognition

Wang

et al. 2023

JMSE

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“…Through this approach, the grouped convolution in ResNeXt enhances the model's accuracy without significantly increasing the parameter count. To further reduce the parameter count, the number of groups in the convolution is set to 4, and the number of blocks in the convolutional layers is reduced from the original [3,4,6,3] to [3,3,3,3], meaning each convolutional layer contains 3 block structures.…”

Section: Enhancements To Wipl-netmentioning

confidence: 99%

“…With the advancement of machine learning and computer vision, visual sensors are increasingly being applied in robotic automated welding [3,4]. How to make use of visual sensors and design intelligent visual algorithms for identification and localization has become a key and inevitable trend in achieving autonomous and intelligent welding robots [5,6].…”

Section: Introductionmentioning

confidence: 99%

Initial point positioning of weld seam and robot pose estimation based on binocular vision

Ye,

Yao,

Ran

et al. 2024

Meas. Sci. Technol.

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In order to rapidly identify and locate the weld seam initial point in robotic automated welding, we established a binocular vision system and proposed a weld seam initial point localization algorithm named WIPL-Net. Built upon the Fully Convolutional One-Stage (FCOS) object detection network, WIPL-Net introduces a lightweight ResNext as its backbone network and incorporates channel attention and enhanced feature fusion mechanisms to enhance feature detection and extraction capabilities. Subsequently, WIPLNet is utilized to obtain the weld seam's initial point, and its three-dimensional coordinates are determined through trigonometric measurements. To further estimate the robot's posture at the initial point, we performed sparse three-dimensional reconstruction of the local region centered on the weld seam initial point based on You Only Look At Coefficients of Tensors (YOLACT) instance segmentation and feature point matching. Finally, we conducted comparative experiments on WIPL-Net and conducted weld seam initial point localization experiments in real welding scenarios. The results demonstrate that our proposed method achieves a positioning error of less than 1.2 mm for the weld seam's initial point and a pose error of less than 10 degrees for the robot, meeting the requirement for real-time positioning of the weld seam's initial point.

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“…Later, Shao et al [ 11 ], continued to develop the previously mentioned system by using a particle filter in order to make it more robust. Fan et al [ 12 ] introduced a method based on digital image processing for initial point alignment in narrow robotic welding. This method allowed detection of the weld seam center point when a laser stripe line was projected over a junction, and it was used later by Fan et al [ 13 ] to develop a weld seam tracking system.…”

Section: Introductionmentioning

confidence: 99%

Computer Vision System for Welding Inspection of Liquefied Petroleum Gas Pressure Vessels Based on Combined Digital Image Processing and Deep Learning Techniques

Cruz

Rivas

Quiza

et al. 2020

Sensors

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One of the most important operations during the manufacturing process of a pressure vessel is welding. The result of this operation has a great impact on the vessel integrity; thus, welding inspection procedures must detect defects that could lead to an accident. This paper introduces a computer vision system based on structured light for welding inspection of liquefied petroleum gas (LPG) pressure vessels by using combined digital image processing and deep learning techniques. The inspection procedure applied prior to the welding operation was based on a convolutional neural network (CNN), and it correctly detected the misalignment of the parts to be welded in 97.7% of the cases during the method testing. The post-welding inspection procedure was based on a laser triangulation method, and it estimated the weld bead height and width, with average relative errors of 2.7% and 3.4%, respectively, during the method testing. This post-welding inspection procedure allows us to detect geometrical nonconformities that compromise the weld bead integrity. By using this system, the quality index of the process was improved from 95.0% to 99.5% during practical validation in an industrial environment, demonstrating its robustness.

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An initial point alignment method of narrow weld using laser vision sensor

Cited by 12 publications

References 24 publications

Automatic Alignment Method of Underwater Charging Platform Based on Monocular Vision Recognition

Automatic Alignment Method of Underwater Charging Platform Based on Monocular Vision Recognition

Initial point positioning of weld seam and robot pose estimation based on binocular vision

Computer Vision System for Welding Inspection of Liquefied Petroleum Gas Pressure Vessels Based on Combined Digital Image Processing and Deep Learning Techniques

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