Theory research of seam recognition and welding torch pose control based on machine vision

Long, Qiang; Zhai, Peng; Liu, Miao; He, Kai; Wang, Chunyang

doi:10.1063/1.4977356

Cited by 2 publications

(1 citation statement)

References 8 publications

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“…In 1993, MATLAB introduced image processing [16], which is widely used in automatic welding [17][18][19][20], welding temperature detection [21], and copper steel laser welding [22]. It offers powerful data processing capabilities, which can realize positioning at the center of the end face of a rebar bundle and the centers of rebar heads.…”

Section: Introductionmentioning

confidence: 99%

Automatic Label Welding Robot System for Bundled Rebars

et al. 2021

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By considering the application requirements of automatic welding of labels on the heads of bundled rebars in the iron and steel industry, an automatic label welding robot system is developed based on machine vision and image processing. The system consists of six modules: pickup unit, image unit, control unit, welding stud unit, label unit, and rebar unit. It can automatically pick up welding studs, pick up labels, perform welding, detect dropped labels, and detect dropped welding studs. The development process of label welding mainly includes the following steps. First, the Cascade RCNN object detection algorithm is used to detect the rebar head, so that the number of rebars and the pixel coordinates of the rebar head centers can be obtained. The detection accuracy of the number of rebars can reach 100%, and the Cascade RCNN average score is 0.9801. Then, an algorithm to identify the center of bundled rebar heads based on the variable-scale method (Davidon-Fletcher-Powell formula (DFP)) is proposed. In addition, limiting conditions, and a process for selection of weldable points for double-label welding are provided. And the coordinate conversion equations of weldable points in different coordinate systems are derived. Finally, a serious of functional verification tests of the robot system were carried out on an actual rebar production line, which verified the accuracy of the weldable point image recognition process, and the usability of the robot system. The results indicate that the accuracy of weldable point recognition can reach 98.99%.

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Section: Introductionmentioning

confidence: 99%

Automatic Label Welding Robot System for Bundled Rebars

et al. 2021

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Manipulator Trajectory Planning Based on Clustering Curve Discretization and B‐Spline

Peng,

Tang,

Huang

et al. 2024

Journal of Field Robotics

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In addressing the welding trajectory planning problem for a six‐axis robot under visual guidance, a method based on clustering and isometric image trajectory curve discretization is proposed to enhance welding trajectory precision and improve welding quality. Firstly, the kinematic model of the six‐axis robot is established using the D‐H parameter method. Subsequently, the discrete processing of the welding trajectory map recognized by an industrial camera and chord error calculation are performed. The discrete trajectory is then interpolated using fifth‐order non‐uniform B‐spline interpolation, with welding speed planning based on an S‐shaped speed planner. Finally, the planned trajectory is verified by welding simulation in MATLAB and V‐REP simulation. The simulation results show that the end trajectory of the manipulator is smooth and stable. Compared with the equal arc length discretization method, the discretization accuracy of the proposed method is improved by 19% (plane curve) and 10.7% (space curve), indicating that the proposed discretization method improves the end tracking accuracy of the six‐axis robot.

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Theory research of seam recognition and welding torch pose control based on machine vision

Cited by 2 publications

References 8 publications

Automatic Label Welding Robot System for Bundled Rebars

Automatic Label Welding Robot System for Bundled Rebars

Manipulator Trajectory Planning Based on Clustering Curve Discretization and B‐Spline

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