卷积神经网络算法在工件抓取中的应用

为提高机械手臂夹取物件的准确率,提出基于深度学习法的3D视觉辨识与抓取系统。该视觉系统结合GPU和深度影像Open CV等函数库,分别进行影像拾取、深度数据运算、坐标转换、影像轮廓搜寻和卷积类神经网络模型训练等。采用YOLOv2算法判别目标物体的种类和中心点,并利用轮廓搜寻方法找出物体的角度信息,作为机械手臂操作目标点;通过坐标转换将相机坐标转为机械手臂坐标,由TCP/IP通信传至运动控制系统进行物件夹取。实验结果表明:不同位置的所有零件辨识准确率均在82%以上,抓取误差在1～4 mm内,符合工业生产的要求。

Application of Convolutional Neural Network Algorithm in Artifact Grabbing

In order to improve the accuracy of the robotic arm grabbing up objects, a 3D visual identification and grabbing system was proposed based on deep learning. In the visual system, GPU and deep image Open CV function library were combined to perform image pickup, depth data calculation, coordinate transformation, image contour search and convolutional neural network model training respectively.The YOLOv2 algorithm was used to discriminate the type and center point of the target object, and the contour search method was used to find out the angle information of the object as the robot arm operation target point; the camera coordinates were converted to the robotic arm coordinates by using coordinate transformation, and then transferred to the motion control system for object grabbing by TCP/IP communication. The experimental results show that the identification accuracy of all parts in different positions is above 82%, and the grabbing error is within 1~4 mm, which meet the requirements of industrial production.