Weld identification and robot path planning algorithm based on stereo vision and YOLO deep learning framework
马佳玮 1孙菁伯 1迟关心 2张广军 1李鑫磊1
扫码查看
点击上方二维码区域，可以放大扫码查看
作者信息
1. 哈尔滨工业大学,材料结构精密焊接与连接全国重点实验室,哈尔滨,150001
2. 哈尔滨工业大学,机电工程学院,哈尔滨,150001
折叠
摘要
为了实现机器人焊接的免示教路径规划,结合深度学习与点云处理技术,开发了一种高效、稳定的焊缝智能识别算法.首先,采用ETH(Eye-to-hand)构型的工业级 3D相机获取焊件周围的二维图像和 3D点云模型,利用预先训练的YOLOv8目标检测模型识别焊件所在的ROI区域(region of interest,ROI),模型识别精度为 99.5%,从而实现快速剔除背景点云,并基于RANSAC平面拟合、欧式聚类等点云处理算法,对ROI区域的三维点云进行焊缝空间位置的精细识别;最后根据手眼标定结果转化为机器人用户坐标系下的焊接轨迹.结果表明,文中所开发的算法可实现随机摆放的焊缝自动识别和焊接机器人路径规划,生成的轨迹与人工示教轨迹效果相当,偏差在0.5 mm以内.
Abstract
In order to realize the teaching-free path planning of robot welding,an efficient and stable weld intelligent identification algorithm was developed by combining deep learning and point cloud processing technology.Firstly,an industrial-grade 3D camera with an eye-to-hand identification was used to obtain the two-dimensional image and 3D point cloud model around the weldment,and the pre-trained YOLOv8 target detection model was used to identify the ROI(Region of Interest)area where the weldment is located.The model identification accuracy is 99.5%,thereby realizing the rapid removal of background point clouds.Based on point cloud processing algorithms such as RANSAC plane fitting and Euclidean clustering,the three-dimensional point cloud in the ROI area was finely identified for the spatial position of the weld seam.Finally,the trajectory was converted into welding trajectories in the robotic user coordinate system with the coordinate transformation matrix.The verification experimental results show that the algorithm developed in this paper can realize the automatic identification of randomly placed weld seams and welding robot path planning,and the generated trajectory has a similar effect to the manual teaching trajectory,with a deviation of within 0.5 mm.
关键词
焊缝智能识别/机器人路径规划/立体视觉/YOLO深度学习/点云处理
Key words
intelligent weld identification/robot path planning/stereo vision/YOLO deep learning model/cloud point processing
维普
万方数据