Robust vision-based joint tracking for laser welding of curved closed-square-butt joints

Nilsen, Morgan; Sikström, Fredrik; Christiansson, Anna-Karin; Ancona, Antonio

doi:10.1007/s00170-018-3044-0

Cited by 10 publications

(6 citation statements)

References 16 publications

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“…Os trabalhos de [57], [58] e [59] utilizam sistemas com câmeras para captura de imagens da junta e definição de pontos da trajetória. [60], [61] e [62] vão além e, a partir da captura de imagens e definição de pontos da trajetória, realizam a eliminação de pontos indesejáveis.…”

Section: Detecção De Juntas E Desenvolvimento De Trajetórias De Soldaunclassified

Evolução dos Processos de Automação em Células de Soldagem: Uma Revisão da Literatura

2022

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Resumo: O processo de soldagem é um método de união de materiais metálicos por meio do aquecimento até uma temperatura adequada. Esse processo é crucial na manufatura de diversos componentes, desde a microeletrônica até componentes estruturais de máquinas pesadas. Com o advento da automação industrial, houve o início da utilização de robôs de solda na indústria, visando uma maior produtividade, qualidade e acuracidade do processo. A partir do século XXI, com a chegada da Indústria 4.0, é possível enxergar novas oportunidades de melhoria no processo de solda, tais como a introdução do sistema CPS (Cyber Physical System), a realidade aumentada e a realidade virtual. Tais técnicas permitem não só a automatização da operação de soldagem, mas também um maior controle do processo, cruzando informações do método planejado com a operação real de modo dinâmico e adaptativo. Nesse contexto de diversificado portifólio de tecnologias usadas no processo de soldagem, o presente trabalho se propõe a realizar uma revisão da literatura com o objetivo de mapear as principais evoluções tecnológicas da automação dos processos de soldagem a arco nos últimos anos.

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Section: Detecção De Juntas E Desenvolvimento De Trajetórias De Soldaunclassified

Evolução dos Processos de Automação em Células de Soldagem: Uma Revisão da Literatura

2022

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“…Moreover, the use of machine vision systems in robotic applications with an industrial perspective has been extensively carried out, for it gives the robot controller feedback about its environment, providing accuracy and flexibility. On the one hand, some applications use eye-in-hand configurations (i.e., camera attached to the robot end-effector), as in [19], where computer vision is used to improve the accuracy of a welding operation, or in [20], where the tool of the robot is automatically changed thanks to an image-based visual servoing. On the other hand, other applications use eye-tohand configurations, i.e., the vision system is attached to some rigid structure and observing the robot workspace, so as to localize pieces to perform operations such as pick and place [21], or to keep track of the environment of the robot for safety purposes [22].…”

Section: B Literature Reviewmentioning

confidence: 99%

“…The visual data can be obtained using 2D cameras, estimating depth by processing the images [19]- [21], or can be directly read from 3D sensors [22], such as Microsoft Kinect.…”

Section: B Literature Reviewmentioning

confidence: 99%

Human-Robot Cooperation for Surface Repair Combining Automatic and Manual Modes

et al. 2020

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This article develops a human-robot cooperation to carry out treatments such as sanding, polishing, etc. on the surface of a known rigid object. For this purpose, a vision system is considered to get the object location to ensure not only the perpendicularity of the robot tool to the object surface but also a smooth approach of the tool to the surface. In order to add flexibility, the proposal includes the simultaneous combination of automatic and manual modes of operation. Thus, the human user can guide the robot tool to treat arbitrary areas (manual mode) and, when the operator releases the tool, the robot goes into the automatic mode to treat prior established areas. The method uses a task prioritization framework and three types of controllers: an admittance controller for the tool guidance; a hybrid controller to modify the tool orientation and, in the automatic mode, the tool position; and a sliding mode controller to limit the velocity at which the tool approaches the object surface. The applicability and efficacy of the proposed method is demonstrated experimentally using a conventional 6R robot arm. INDEX TERMS Cooperative sanding, robot cooperation, vision system I. INTRODUCTION A. MOTIVATION

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“…Joint features were extracted from the image by Hough transform. then the position of the joint was estimated by kalman filter [2] . Wang WX uses a hybrid optimization process based on template matching, effectively detects the weld gap by boundary detection method, and analyzes the variable gap detected in real-time tracking to ensure high welding quality [3] .…”

Section: Introductionmentioning

confidence: 99%

Vision based weld seam localization method for axle housing automatic welding

Zhang

Zhang²,

Zhang³

et al. 2022

Thirteenth International Conference on Information Optics and Photonics (CIOP 2022)

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Clamping error during automatic welding of axle housing will lead to weld deviation. To that end, a method for axle housing weld seam localization based on monocular vision is proposed in this paper. Firstly, a method of extracting weld seam centerline based on template matching and iterated least squares fitting is proposed according to the grayscale of axle housing image. Then, the end points of weld seam are searched through the gray feature of axle housing on the weld seam centerline. Finally, the mapping relationship between image coordinate system and robot coordinate system is established by calibration. According to the deviation between actual position and standard position of weld under image coordinate system, the actual three-dimensional coordinate of weld in robot coordinate system is found by particle swarm optimization algorithm. The experimental results show that the maximum error of the weld seam endpoints obtained by this method in the x, y axis is less than 0.5mm, and the maximum error in the z axis is less than 0.9mm, which meets the requirements of axle housing automatic welding. This method only uses one monocular camera, and has a low cost while ensuring the positioning accuracy through calibration and optimization algorithms.

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Robust vision-based joint tracking for laser welding of curved closed-square-butt joints

Cited by 10 publications

References 16 publications

Evolução dos Processos de Automação em Células de Soldagem: Uma Revisão da Literatura

Evolução dos Processos de Automação em Células de Soldagem: Uma Revisão da Literatura

Human-Robot Cooperation for Surface Repair Combining Automatic and Manual Modes

Vision based weld seam localization method for axle housing automatic welding

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