Stereo vision for path correction in off-line programmed robot welding

Ryberg, Anders; Ericsson, Mikael; Christiansson, Anna-Karin; Eriksson, Kenneth; Nilsson, Jonas; Larsson, Martin

doi:10.1109/icit.2010.5472442

Cited by 25 publications

(9 citation statements)

References 10 publications

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“…Identifying the actual processing trajectory and performing calibration in accordance with the default processing trajectory [10]:…”

Section: Automatic Calibration Technologymentioning

confidence: 99%

Intelligent Automation Module-Based Gear Edge Grinding System

Huang¹,

Guu²,

Yang³

et al. 2020

JMEA

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As industry progresses toward intelligent production and development, on-site workers that perform repetitive tasks will be replaced by intelligent machines. Currently, automation applications still have the following problems: (1) on-site personnel are required to line up the workpieces before a robot arm can pick it up; (2) the trajectory generated by offline programming software must be adjusted by on-site personnel in accordance with the processing results; and (3) because of workpiece positioning errors and tool wear, achieving acceptable processing results is difficult. This study developed intelligent application modules that solve the aforementioned automation application problems. These modules predict processing quality, generate trajectory, enable robot arms to load and unload randomly positioned workpieces, and automatically calibrate the system. An automatic gear edge grinding system was developed by integrating each module; the system increases the processing efficiency and solves the current problem of manual grinding being required after gear processing.

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“…Identifying the actual processing trajectory and performing calibration in accordance with the default processing trajectory [10]:…”

Section: Automatic Calibration Technologymentioning

confidence: 99%

Intelligent Automation Module-Based Gear Edge Grinding System

Huang¹,

Guu²,

Yang³

et al. 2020

JMEA

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“…Stereo vision for path correction in off-line programmed robot welding was proposed by Ryberg et al (2010). The system uses one camera and a weld tool mounted on the robot hand to grab the images of the weld joint from different positions and orientations.…”

Section: Related Workmentioning

confidence: 99%

A Review Paper on Vision Based Identification, Detection and Tracking of Weld Seams Path in Welding Robot Environment

Shah¹,

Sulaiman²,

Shukor³

et al. 2016

MAS

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<p><span style="font-size: 10.5pt; font-family: 'Times New Roman','serif'; mso-bidi-font-size: 10.0pt; mso-fareast-font-family: 宋体; mso-font-kerning: 1.0pt; mso-ansi-language: EN-US; mso-fareast-language: ZH-CN; mso-bidi-language: AR-SA;" lang="EN-US">Welding is an important technology especially for joining between two metals, fabricated and repairing metals products in manufacturing industries such as in automotive industries. To increase the productivity and lower cost, today the welding operation in industries introduces the welding robot. The main challenges to using welding robot is time taken to program robot path for a new job in low to medium volume manufacturing industries. This paper begins with the review of identified, detected and tracked the weld seams path with different type of welding in welding environment. Next, a review of analysis an identified and detect the weld seams path approaches is included with advantages, drawback and limitation. This paper is concluded by a comprehensive summary which discussed the disadvantages and limitation of a robust approach in each stage. The improvement of a new approach in each stage depends on the lack, limitation and the results which are expected from the work.</span></p>

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“…The pixel intensities in the local window are used to define a threshold for segmenting the weldment from the background. Similarly in [8] the ROI is centred in the image to ignore background objects. The experimental results show that these methods perform well, however it is not always possible to centre the weld seam in the image for robotic applications due to occlusions introduced from tooling and space limitations.…”

Section: Introductionmentioning

confidence: 99%

Weld seam detection using computer vision for robotic Arc Welding

Dinham

2012

2012 IEEE International Conference on Automation Science and Engineering (CASE)

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Visual identification of narrow welding seams is difficult to achieve in robotic systems especially for ferrous materials. This paper describes an algorithm for detecting the weld seam in a butt-joint configuration for robotic Arc Welding of mild steel materials using computer vision. The proposed method can automatically subtract the background from the images obtained from a robot mounted camera system. Reliable seam identification can then be achieved for welding of ferrous materials. Experimental results have shown that this method is capable of detecting both straight and curved welding seams without prior knowledge of the location of the seam. It is shown that this method is robust to be used in an industrial setting.

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Stereo vision for path correction in off-line programmed robot welding

Cited by 25 publications

References 10 publications

Intelligent Automation Module-Based Gear Edge Grinding System

Intelligent Automation Module-Based Gear Edge Grinding System

A Review Paper on Vision Based Identification, Detection and Tracking of Weld Seams Path in Welding Robot Environment

Weld seam detection using computer vision for robotic Arc Welding

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