一种基于CNN深度学习的焊接机器人视觉模型

为了准确地识别复杂环境下的焊缝目标，建立了一种基于深度学习的焊接机器人视觉模型，该模型采用局部联接和全联接相结合的CNN卷积神经网络结构，局部联接由3个卷积层和子采样层交替组成，用于焊接目标的特征提取，全连接层由输入层、隐层和输出层组成，作为分类器用于焊缝目标识别. 采样了一千多幅焊接目标图像样本用于CNN的网络训练，分析了不同CNN网络结构参数对模型的影响. 结果表明，该视觉模型对焊接目标的平移、旋转和比例缩放表现出良好的鲁棒性，可以应用到焊接机器人的视觉导航.

A visual model of welding robot based on CNN deep learning

In order to accurately recognize the weld target in complex environment, a visual model of welding robot based on deep learning was established. The model adoped a convolutional neural network (CNN) combining local connection and full connection. The local connection was composed of 3 convolution layers (C) and 3 subsampling layers (S) with C-S alternating mode for feature extraction of welding target. The full connection layer was composed of input layer, hidden layer and output layer as a classifier for weld target recognition. More than 1 000 image samples of welding targets were sampled for CNN network training, and the influence of different CNN structure parameters on the model was analyzed. The test results show that the visual model was robust to the translation, rotation and scaling of welding targets, and could be applied to the visual navigation of welding robots.