面向三維復雜焊縫的焊接機器人焊縫跟蹤方法

曹學鵬; 張弓; 楊根; 吳月玉; 陶浩; 王傳璽

doi:10.13374/j.issn2095-9389.2021.09.02.001

面向三維復雜焊縫的焊接機器人焊縫跟蹤方法

doi: 10.13374/j.issn2095-9389.2021.09.02.001

曹學鵬¹,
張弓^{2, 3, ,},
楊根²,
吳月玉²,
陶浩²,
王傳璽¹

1.
長安大學，西安 710064
2.
廣州先進技術研究所，廣州 511458
3.
中國科學院大學，北京 100049

基金項目: 國家自然科學基金資助項目（62073092）；廣東省自然科學基金資助項目（2021A1515012638）；陜西省重點研發計劃資助項目（2021ZDLGY09-02）；廣州市基礎研究計劃資助項目（202201011619, 202102080650, 202002030320）

詳細信息

通訊作者:
E-mail: gong.zhang@giat.ac.cn

中圖分類號: TP242.2
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- 文章訪問數: 753
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- 被引次數: 0
出版歷程
- 收稿日期: 2021-09-02
- 網絡出版日期: 2021-10-27
- 刊出日期: 2023-02-01

Welding seam tracking method of welding robot oriented to three-dimensional complex welding seam

1.
Chang’an University, Xi’an 710064, China
2.
Guangzhou Institute of Advanced Technology, Guangzhou 511458, China
3.
University of Chinese Academy of Sciences, Beijing 100049, China

More Information

Corresponding author: E-mail: gong.zhang@giat.ac.cn

摘要

摘要: 機器人焊接技術具有質量穩定、效率高等特點，為實現空間內的三維復雜焊縫跟蹤，提出基于分段掃描、濾波、特征點采集、路徑規劃的焊縫四步跟蹤方法。通過安裝于焊接機器人末端的激光傳感器，以分段掃描方式連續多段采集焊縫數據；為提高跟蹤精度，采用組合濾波的方式修正數據，有效降低焊件表面毛刺、數據失真和噪聲等影響；通過特征點采集與坐標系標定確定焊接點；最后結合焊接機器人路徑規劃獲得空間焊接路徑。對二維S型焊縫與三維復雜焊縫進行了實驗研究，結果表明提出的四步焊縫跟蹤方法可形成完整的焊接路徑，兩種焊件平均跟蹤誤差約為0.296 mm和0.292 mm，滿足機器人焊接跟蹤誤差低于0.5 mm的精度要求。表明所提出焊接跟蹤方法的有效性，可為復雜焊縫的高精度跟蹤和自動焊接研究提供有益參考。
- 焊縫跟蹤 /
- 三維復雜焊縫 /
- 分段掃描 /
- 特征點提取 /
- 路徑規劃
Abstract: Welding robot is widely used in many kinds and working conditions of welding production in the current machinery manufacturing industry. It plays an essential role in the machinery manufacturing industry. At the moment, in most industries, welding robots still work by teaching and payback. When the welding object or conditions change, the robot cannot make corresponding adjustments in time, which makes the welding gun deviate from the weld center, resulting in the decline of welding quality. The realization of automatic and intelligent welding is the inevitable development trend in the future. The application of machine vision in the welding field will promote the transformation of welding technology from rigid welding automation to flexible welding intelligence. Welding automation and intelligence are intended to improve the working conditions and environment, reduce labor costs, and improve product quality. Robotic welding technology is known for its great efficiency and consistent quality. A four-step welding seam tracking system is suggested based on segmented scanning, filtering, feature points extraction, and path planning. Through the laser sensor installed at the end of the welding robot, the welding seam data is continuously collected in multiple segments in a segmented scanning manner. To improve the tracking accuracy, a combined filtering method is 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 are collected, and the coordinate system is calibrated in order to identify the welding points. Finally, the spatial welding path is obtained by path planning. Two-dimensional type S and three-dimensional complex welding experimental investigations are carried out. The results show that 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, which are close enough to fulfill the required accuracy of 0.5 mm. It shows that the proposed tracking method is effective and can provide a reference for the research of high-precision tracking and automatic welding.
- seam tracking /
- 3D complex welding /
- segmented scanning /
- feature points extraction /
- path planning

HTML全文

圖 1 焊縫跟蹤系統構成圖

Figure 1. Structure diagram of seam tracking system

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圖 2 焊縫跟蹤流程圖

Figure 2. Flow chart of welding seam tracking

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圖 3 分段掃描原理.(a)實物掃描圖; (b)原始數據圖像; (c) 點云圖; (d) 偽彩圖

Figure 3. Segmented scanning: (a) weldment scanning; (b) raw data; (c) point cloud; (d) pseudo-color picture

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圖 4 濾波效果. (a) Lowess濾波; (b) 限幅、高斯濾波

Figure 4. Filter effect: (a) Lowess filtering; (b) limiting, Gaussian filtering

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圖 5 特征點提取

Figure 5. Feature points extraction

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圖 6 機器人工作站坐標系

Figure 6. Robot workstation coordinate system

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圖 7 焊接實驗系統組成

Figure 7. Welding experimental system composition

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圖 8 實驗對象. (a) S型焊縫; (b) 三維復雜焊縫

Figure 8. Test subject: (a) type S; (b) three-dimensional (3D) curve

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圖 9 原始數據圖. (a) S型焊縫; (b) 三維復雜焊縫

Figure 9. Raw data graph: (a) type S; (b) 3D curve

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圖 10 焊接路徑. (a) S型焊縫; (b) S型焊縫點云圖; (c) 三維復雜焊縫; (d) 三維復雜焊縫點云圖

Figure 10. Welding path: (a) type S; (b) point cloud of type S; (c) 3D curve; (d) point cloud of 3D curve

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圖 11 誤差分析. (a) S型焊縫; (b) 三維復雜焊縫

Figure 11. Error analysis: (a) type S; (b) 3D curve

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表 1 誤差分析

Table 1. Error analysis

Welding type Mean error/mm Standard deviation

Type S 0.296 0.0779
3D curve 0.292 0.1129

下載: 導出CSV

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