In the field of welding robotics, visual sensors, which are mainly composed of a camera and a laser, have proven to be promising devices because of their high precision, good stability, and high safety factor. In real welding environments, there are various kinds of weld joints due to the diversity of the workpieces. The location algorithms for different weld joint types are different, and the welding parameters applied in welding are also different. It is very inefficient to manually change the image processing algorithm and welding parameters according to the weld joint type before each welding task. Therefore, it will greatly improve the efficiency and automation of the welding system if a visual sensor can automatically identify the weld joint before welding. However, there are few studies regarding these problems and the accuracy and applicability of existing methods are not strong. Therefore, a weld joint identification method for visual sensor based on image features and support vector machine (SVM) is proposed in this paper. The deformation of laser around a weld joint is taken as recognition information. Two kinds of features are extracted as feature vectors to enrich the identification information. Subsequently, based on the extracted feature vectors, the optimal SVM model for weld joint type identification is established. A comparative study of proposed and conventional strategies for weld joint identification is carried out via a contrast experiment and a robustness testing experiment. The experimental results show that the identification accuracy rate achieves 98.4%. The validity and robustness of the proposed method are verified.

中文翻译：

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基于图像特征和支持向量机的视觉传感器焊接接头类型识别方法。

在焊接机器人领域，主要由照相机和激光组成的视觉传感器由于其高精度，良好的稳定性和高安全系数而被证明是很有前途的设备。在实际的焊接环境中，由于工件的多样性，焊接接头种类繁多。不同焊接接头类型的定位算法不同，并且焊接中应用的焊接参数也不同。在每次焊接任务之前，根据焊接接头类型手动更改图像处理算法和焊接参数效率非常低下。因此，如果视觉传感器能够在焊接前自动识别焊接接头，则将大大提高焊接系统的效率和自动化程度。然而，关于这些问题的研究很少，现有方法的准确性和适用性也不强。因此，本文提出了一种基于图像特征和支持向量机的视觉传感器焊接接头识别方法。焊接点周围的激光变形被视为识别信息。提取两种特征作为特征向量以丰富识别信息。随后，基于提取的特征向量，建立用于焊接接头类型识别的最优SVM模型。通过对比实验和鲁棒性测试实验对提议和常规焊缝识别策略进行了比较研究。实验结果表明，识别准确率达到98.4％。