Path Planning With Automatic Seam Extraction Over Point Cloud Models for Robotic Arc Welding

Zhou, Peng; Peng, Rui; Xu, Maggie; Wu, Victor; Navarro-Alarcon, David

doi:10.1109/lra.2021.3070828

Cited by 83 publications

(7 citation statements)

References 20 publications

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“…The workflow depicted in Fig. 3 describes the following steps: Before the process begins, (1) the RGB-D sensor captures a depth image of the welding area to produce a point cloud of the workpiece; (2) A depth-based localization algorithm segments the area of interest and computes the welding seam/path [22], [23]. During welding, (3) the HDR camera captures greyscale images of the process and traces the center of the electric arc in real-time, where (4) all visual information is registered into a 2D image and streamed to a webpage; (5) The VR headset access the 2D live streaming video and the 3D model via Wi-Fi.…”

Section: A System Overviewmentioning

confidence: 99%

A Multisensor Interface to Improve the Learning Experience in Arc Welding Training Tasks

Lee

Zhou

Duan

et al. 2023

IEEE Trans. Human-Mach. Syst.

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This paper presents the development of a multisensor user interface to facilitate the instruction of arc welding tasks. Traditional methods to acquire hand-eye coordination skills are typically conducted through one-to-one instruction where trainees must wear protective helmets and conduct several tests. This approach is inefficient as the harmful light emitted from the electric arc impedes the close monitoring of the process; Practitioners can only observe a small bright spot. To tackle these problems, recent training approaches have leveraged virtual reality to safely simulate the process and visualize the geometry of the workpieces. However, the synthetic nature of these types of simulation platforms reduces their effectiveness as they fail to comprise actual welding interactions with the environment, which hinders the trainees' learning process. To provide users with a real welding experience, we have developed a new multi-sensor extended reality platform for arc welding training. Our system is composed of: (1) An HDR camera, monitoring the real welding spot in real-time; (2) A depth sensor, capturing the 3D geometry of the scene; and (3) A head-mounted VR display, visualizing the process safely. Our innovative platform provides users with a "bot trainer", virtual cues of the seam geometry, automatic spot tracking, and performance scores. To validate the platform's feasibility, we conduct extensive experiments with several welding training tasks. We show that compared with the traditional training practice and recent virtual reality approaches, our automated multi-sensor method achieves better performances in terms of accuracy, learning curve, and effectiveness.

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Section: A System Overviewmentioning

confidence: 99%

A Multisensor Interface to Improve the Learning Experience in Arc Welding Training Tasks

Lee

Zhou

Duan

et al. 2023

IEEE Trans. Human-Mach. Syst.

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“…Once the system starts, we obtain color and images from the RGB-D camera and continuously receive a grayscale image from the HDR camera. A point cloud is formed with the RGB-D matrix and a groove detection algorithm is applied to segment the welding groove on the workpiece [19], [20]. A seam localization algorithm is implemented to automatically determine the welding seam.…”

Section: A System Overviewmentioning

confidence: 99%

“…The RGB-D camera captures one image that covers the whole welding area of the workpiece to locate the groove. Together, the color and depth images form a point cloud P. The proposed detection algorithm uses the difference in edge intensity to automatically find the path of the groove, where local neighborhoods in P are computed to segment the groove's approximated location [19], [20]. In Fig.…”

Section: B Seam Localizationmentioning

confidence: 99%

A Multi-Sensor Interface to Improve the Teaching and Learning Experience in Arc Welding Training Tasks

Lee¹,

Zhou²,

Duan³

et al. 2021

Preprint

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“…Currently, the traditional working mode of teaching-playback [3] or offline programming prevails in robotic MAG welding processes [4]. As these conventional practices are error-prone and tedious, many advanced techniques such as seam detection and tracking are gradually receiving increasing attention [5].…”

Section: Introductionmentioning

confidence: 99%

A Novel 3D Complex Welding Seam Tracking Method in Symmetrical Robotic MAG Welding Process Using a Laser Vision Sensing

Zhang

Huang

et al. 2023

Symmetry

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The welding seam tracking operation ensures that the welding torch of the welding robot can go with the welding seam during the whole symmetrical robotic welding procession. To achieve three-dimensional complex welding seams tracking, a four-step welding seam tracking system is suggested based on segmented scanning, combined filtering, feature-point extraction, and welding path planning. From using the laser sensor installed at the end of welding robot, the welding seam data was continuously collected in multiple segments by segmented scanning. For the purpose of improving seam tracking accuracy, a combined filtering technique was used to correct the data to reduce the effects of burrs, data distortion, and noise on the surface of the weldment. Then, the feature points were collected so that the coordinate system will be calibrated to identify the welding points. Finally, a spatial welding path was obtained by welding path planning. Experimental investigations of the two-dimensional (2D) symmetrical S-shaped and three-dimensional (3D) curved welding seams were conducted. The obtained results demonstrate the proposed method can form a complete welding path. The average errors of the two weldments are about 0.296 mm and 0.292 mm, respectively. This shows that the proposed tracking method is effective and can provide a reference for the research of high-precision seam tracking and automatic welding.

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Path Planning With Automatic Seam Extraction Over Point Cloud Models for Robotic Arc Welding

Cited by 83 publications

References 20 publications

A Multisensor Interface to Improve the Learning Experience in Arc Welding Training Tasks

A Multisensor Interface to Improve the Learning Experience in Arc Welding Training Tasks

A Multi-Sensor Interface to Improve the Teaching and Learning Experience in Arc Welding Training Tasks

A Novel 3D Complex Welding Seam Tracking Method in Symmetrical Robotic MAG Welding Process Using a Laser Vision Sensing

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