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机构地区:[1]江苏大学材料科学与工程学院,江苏镇江212013 [2]上海交通大学焊接工程研究所,上海200030
出 处:《焊接学报》2009年第4期17-20,共4页Transactions of The China Welding Institution
基 金:国家自然科学基金资助项目(50575144);江苏大学高级人才启动基金资助项目(07JDG085);江苏大学创新团队基金资助项目(CLXY03)
摘 要:起始焊接位置的识别和定位是实现机器人智能化焊接的第一步,在分析焊缝接头类型的基础上给出了起始焊位的定义.采用CCD摄像机在自然光条件下获得待焊工件图像,由起始焊位的定义分析了起始焊位识别的方法.提出一种局部范围内由粗到细的两步精确定位方法.首先求取焊缝和工件边界交点作为初始值.第二步以该点为中心建立一个小窗口,在此局部范围内检测角点.根据设定的判断标准,可充分利用原图像的丰富信息和边缘提取后的结构化信息.结果表明,对规则焊缝,初始值与实际初始焊位能很好吻合,而对不规则的焊缝,初始值偏离实际位置.文中提出的两步定位方法则能准确的识别和定位具有明显边界的规则和不规则焊缝的初始焊接位.The recognition and positioning of start welding position(SWP) is the first step to realize intelligent robot welding.The definition of SWP is given based on the analysis of seam type.The macro-scopical images of workpieces to be welded are snapped by CCD camera in a relativly large range without additional light.The recognizing methods of SWP are analyzed according to its definition.A two-step method that from coarse to fine is proposed to recognize the SWP accurately.The first step is to solve the curve functions of seam and workpieces boundaries by fitting,and their intersection points are regarded as the initial value of SWP.The second step is to establish a small window that takes the initial values as the centre.And the SWP becomes exact by the corner detection in the window.Both the abundant information of original images and the structured information of recognized images are used according to the given judge rules,which take full advantage of the image information and improve the recognized precision.The detected results show that the actual position of SWP and recognized initial value by the first step are identical for the normal seam,but the recognized result by the first step is different from the actual position for the un-normal curve seam.The exact results can be obtained by the presented two-step method for both normal and un-normal curve seams.
分 类 号:TG115.28[金属学及工艺—物理冶金]
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