Key Laboratory of Automobile Materials, School of Materials Science and Engineering, Jilin University, Changchun 130025, China.
School of Mechanical and Aerospace Engineering, Jilin University, Changchun 130025, China.
Sensors (Basel). 2022 Nov 6;22(21):8546. doi: 10.3390/s22218546.
Real-time tracking welding with the assistance of structured light vision enhances the intelligence of robotic welding, which significantly shortens teaching time and guarantees accuracy for user-customized product welding. However, the robustness of most image processing algorithms is deficient during welding practice, and the security regime for tracking welding is not considered in most trajectory recognition and control algorithms. For these two problems, an adaptive feature extraction algorithm was proposed, which can accurately extract the seam center from the continuous, discontinuous or fluctuating laser stripes identified and located by the CNN model, while the prior model can quickly remove a large amount of noise and interference except the stripes, greatly improving the extraction accuracy and processing speed of the algorithm. Additionally, the embedded Pauta criterion was used to segmentally process the center point data stream and to cyclically eliminate outliers and further ensure the accuracy of the welding reference point. Experimental results showed that under the guarantee of the above-mentioned seam center point extraction and correction algorithms, the tracking average error was 0.1 mm, and even if abnormal trajectory points existed, they did not cause welding torch shaking, system interruption or other accidents.
借助结构光视觉的实时跟踪焊接提高了机器人焊接的智能化程度，显著缩短了用户定制产品焊接的教学时间，并保证了准确性。然而，在焊接实践中，大多数图像处理算法的鲁棒性不足，并且在大多数轨迹识别和控制算法中都没有考虑跟踪焊接的安全机制。针对这两个问题，提出了一种自适应特征提取算法，该算法可以从 CNN 模型识别和定位的连续、不连续或波动的激光条纹中准确地提取焊缝中心，而先验模型可以快速去除除条纹以外的大量噪声和干扰，从而大大提高了算法的提取精度和处理速度。此外，嵌入式 Pauta 准则用于分段处理中心点数据流，并循环消除异常值，进一步保证焊接参考点的准确性。实验结果表明，在上述焊缝中心点提取和校正算法的保证下，跟踪平均误差为 0.1 毫米，即使存在异常轨迹点，也不会导致焊枪晃动、系统中断或其他事故。
