Study of image processing for V-shape groove and robotic weld seam tracking based on laser vision

Single-stripe laser was applied to acquire V-shape groove contour information. Most of arc light and splash noise was removed and stripe laser image was kept by wavelet transform. Half-threshold algorithm was used for image segmentation and stripe laser image was gotten after refining. Weld seam center position was identified and extracted by extreme curvature corner detection method. The location of torch was detected to accord the frequency of computer program with robot program and serial communication program. The tracking experiments of sidelong, reflex and curve weld line show that the system can meet the demand of the tracking precision under normal welding conditions.

Determination of Surface Roughness in Wire and Arc Additive Manufacturing Based on Laser Vision Sensing

Wire and arc additive manufacturing(WAAM) shows a great promise for fabricating fully dense metal parts by means of melting materials in layers using a welding heat source. However, due to a large layer height produced in WAAM, an unsatisfactory surface roughness of parts processed by this technology has been a key issue. A methodology based on laser vision sensing is proposed to quantitatively calculate the surface roughness of parts deposited by WAAM.Calibrations for a camera and a laser plane of the optical system are presented. The reconstruction precision of the laser vision system is verified by a standard workpiece. Additionally, this determination approach is utilized to calculate the surface roughness of a multi-layer single-pass thin-walled part. The results indicate that the optical measurement approach based on the laser vision sensing is a simple and effective way to characterize the surface roughness of parts deposited by WAAM. The maximum absolute error is less than 0.15 mm. The proposed research provides the foundation for surface roughness optimization with different process parameters.

Development of an automatic post-weld inspection system based on laser vision

In order to overcome the limitations of manual post-weld visual inspection approach, an automated inspection system is developed which uses three-dimensioual laser vision system based on the principle of optical triangulation. The system hardware consists of a modular development kit （MDK）, a computer, an actuating mechanism and so on. In image processing algorithms, extraction accuracy of centric line of laser stripe is the critical factor that determines the system performance. So according to the features of laser stripe image, a novel algorithm is developed to detect the central line of laser stripe fast and accurately. Experiments have demonstrated that this system can be used in various weld features inspection of both butt and fillet types of weld. Compared with traditional manual inspection method, this method has obvious dominance. The three-dimensional reconstruction result shows that this system has high accuracy and reliability.

Study on the image processing of laser vision seam tracking system

Seam image processing is the basis of the realization of automatic laser vision seam tracking system, and it has become one of the important research directions. Adding windows processing, gray processing, fast median filtering, binary processing and image edge extraction are used to pretreat the seam image. In the post-processing of seam image, the feature points of the target image are succesfully detected by using center line extraction and feature points detection algorithm based on slope analysis. The whole processing time is less than 150 ms, and the real-time processing of seam image can be implemented.

Laser vision based adaptive fill control system for TIG welding

The variation of joint groove size during tungsten inert gas （TIG） welding will result in the non-uniform fill of deposited metal. To solve this problem, an adaptive fill control system was developed based on laser vision sensing. The system hardware consists of a modular development kit （MDK） as the real-time image capturing system, a computer as the controller, a D/A conversion card as the interface of controlled variable output, and a DC TIG welding system as the controlled device. The system software is developed and the developed feature extraction algorithm and control strategy are of good accuracy and robustness. Experimental results show that the system can implement adaptive fill of melting metal with high stability, reliability and accuracy. The groove is filled well and the quality of the weld formation satisfies the relevant industry criteria.

Vision Sensing-Based Online Correction System for Robotic Weld Grinding

The service cycle and dynamic performance of structural parts are afected by the weld grinding accuracy and surface consistency. Because of reasons such as assembly errors and thermal deformation, the actual track of the robot does not coincide with the theoretical track when the weld is ground ofine, resulting in poor workpiece surface quality. Considering these problems, in this study, a vision sensing-based online correction system for robotic weld grinding was developed. The system mainly included three subsystems: weld feature extraction, grinding, and robot real-time control. The grinding equipment was frst set as a substation for the robot using the WorkVisual software. The input/output (I/O) ports for communication between the robot and the grinding equipment were confgured via the I/O mapping function to enable the robot to control the grinding equipment (start, stop, and speed control). Subsequently, the Ethernet KRL software package was used to write the data interaction structure to realize realtime communication between the robot and the laser vision system. To correct the measurement error caused by the bending deformation of the workpiece, we established a surface profle model of the base material in the weld area using a polynomial ftting algorithm to compensate for the measurement data. The corrected extracted weld width and height errors were reduced by 2.01% and 9.3%, respectively. Online weld seam extraction and correction experiments verifed the efectiveness of the system’s correction function, and the system could control the grinding trajectory error within 0.2 mm. The reliability of the system was verifed through actual weld grinding experiments. The roughness, Ra, could reach 0.504 µm and the average residual height was within 0.21 mm. In this study, we developed a vision sensing-based online correction system for robotic weld grinding with a good correction efect and high robustness.

Structured Light 3D Vision Based on Laser Scanning Space-encoding and Its Synchro Control

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Global Calibration Method of Multi-sensor Vision System Using Skew Laser Lines

Multi-sensor vision system plays an important role in the 3D measurement of large objects.However,due to the widely distribution of sensors,the problem of lacking common fields of view（FOV） arises frequently,which makes the global calibration of the vision system quite difficult.The primary existing solution relies on large-scale surveying equipments,which is ponderous and inconvenient for field calibrations.In this paper,a global calibration method of multi-sensor vision system is proposed and investigated.The proposed method utilizes pairs of skew laser lines,which are generated by a group of laser pointers,as the calibration objects.Each pair of skew laser lines provides a unique coordinate system in space which can be reconstructed in certain vision sensor＇s coordinates by using a planar pattern.Then the geometries of sensors are computed under rigid transformation constrains by taking coordinates of each skew lines pair as the intermediary.The method is applied on both visual cameras with synthetic data and a real two-camera vision system;results show the validity and good performance.The prime contribution of this paper is taking skew laser lines as the global calibration objects,which makes the method simple and flexible.The method need no expensive equipments and can be used in large-scale calibration.

基于线结构光的厚板焊缝特征点提取算法

针对厚板结构件在使用激光视觉系统时图像特征区域提取难和工件表面加工不均匀漫反射等导致特征点提取困难的问题,提出基于线结构光的厚板焊缝特征点提取算法。首先,激光视觉系统获取焊缝图像并使用YOLOv4算法进行预训练,利用训练获取的权重文件自动检测并获取焊缝特征感兴趣区域(ROI);其次,对ROI进行降噪、二值化等处理,通过逐行(列)搜索法得到焊缝中心线;最后,根据不同焊缝类型,基于最小二乘法使用距离法和直线段聚类的方法来提取特征点。实验结果表明:该方法可有效提取不同类型的焊缝特征点,具有鲁棒性强、识别误差小等特点。

Above-average defocus curves in photopic and mesopic vision with multifocal intraocular lenses after laser assisted in situ keratomileusis

I am Dr. David P Pifiero from the Department of Optics, Pharmacology and Anatomy of the University of Alicante and from the Department of Ophthalmology of Vithas Medimar （Oftalmar） and Vithas Virgen del Carmen （Qvision） hospitals in Spain.

Wavelet Moment Invariants Extraction of Underwater Laser Vision Image

Wavelet moment invariants are constructed for object recognition based on the global feature and local feature of target, which are brought for the simple background of the underwater objects, complex structure, similar form etc. These invariant features realize the multi-dimension feature extraction of local topology and in- variant transform. Considering translation and scale invariant characteristics were ignored by conventional wavelet moments, some improvements were done in this paper. The cubic B-spline wavelets which are optimally localized in space-frequency and close to the forms of Li's(or Zernike's) polynomial moments were applied for calculating the wavelet moments. To testify superiority of the wavelet moments mentioned in this paper, generalized regres- sion neural network(GRNN) was used to calculate the recognition rates based on wavelet invariant moments and conventional invariant moments respectively. Wavelet moments obtained 100% recognition rate for every object and the conventional moments obtained less classification rate. The result shows that wavelet moment has the ability to identify many types of objects and is suitable for laser image recognition.

激光视觉引导下的工业机器人步态运动学参数辨识

工业机器人在多个领域都有广泛应用,而离线编程则成为其重要的发展方向。由于离线编程对机器人自身的绝对定位精度有较高要求,为提升绝对定位精度,设计一种激光视觉引导下的工业机器人步态运动学参数辨识方法。基于线结构激光测量技术,设计由工业智能相机、感光元件、滤光片、线激光器构成的激光视觉传感器,使用该传感器测量工业机器人,以获取其三维信息。通过粗引导与精引导两个步骤对激光视觉传感器实施激光视觉引导,以提高效率,降低瞄准误差。工业机器人步态运动学参数误差的辨识模型,通过最小二乘法对模型实施迭代求解,以获取精确的方程组解,从而完成步态运动学参数辨识。测试结果表明,该方法的步态运动学参数辨识误差低于0.02 mm,在30次迭代次数以内即可完成求解。

应用激光跟踪仪和单目视觉检测三维水模体定位精度方法研究

针对三维水模体检测方法较少、检测过程复杂等问题,提出基于激光跟踪仪和单目视觉的三维水模体定位精度检测方法。激光跟踪仪三维水模体定位精度检测对比实验验证三维水模体在有水和无水的情况下定位精度一致。单目视觉三维水模体定位精度检测实验结果表明:X轴、Y轴、Z轴和空间对角线的定位误差均在0.050 mm以内,重复性分别为0.030 mm,0.064 mm,0.056 mm,0.140 mm,X轴和Y轴的垂直度为0.800 mm,Y轴和Z轴的垂直度为0.761 mm,X轴和Z轴的垂直度为0.503 mm,与激光跟踪仪的测量精度在一个量级。最后,经不确定度分析得到相邻点距离测量误差的标准差为0.0187 mm,满足检测要求。两种方法都能实现非接触测量,简化冗杂的检测过程,可满足医院对医用直线加速器的日常检测需求。

雷珠单抗联合激光光凝治疗视网膜静脉阻塞继发黄斑水肿患者的效果

目的探讨视网膜静脉阻塞(RVO)继发黄斑水肿(ME)患者采用雷珠单抗联合激光光凝治疗对视力的影响。方法选取2020年1月至2022年12月南昌市第一医院收治的84例(84只眼)RVO继发ME患者作为研究对象,按照随机数字表法分为对照组(42例)与观察组(42例),对照组采用激光光凝治疗,观察组采用雷珠单抗与激光光凝联合治疗。比较两组患者的最佳矫正视力(BCVA)、黄斑中心凹视网膜厚度(CMT)、眼压、血管内皮生长因子(VEGF)、眼底视网膜出血治疗效果及并发症。结果观察组治疗后的BCVA、CMT及VEGF低于对照组,眼底视网膜出血治疗总有效率(92.86%)高于对照组(76.19%),差异有统计学意义(P<0.05);两组患者治疗后的眼压比较,差异无统计学意义(P>0.05)。所有患者均未出现视网膜脱离、玻璃体积血及眼内感染等并发症,仅观察组出现1例一过性眼压升高。结论RVO继发ME患者采用雷珠单抗与激光光凝联合治疗有利于降低CMT、VEGF水平,改善患者的视力及眼底视网膜出血,且对患者眼压影响小,并发症并未增加,临床应用安全性高。

基于单激光束信息的掘锚装备视觉定位方法研究

煤矿井下掘锚装备智能化是改善行业采掘失衡问题的关键,而掘锚装备的精确定位是实现其智能化的前提。与其他传统定位方法相比,基于视觉的位姿测量方法以其无接触、无累计误差的优势在煤矿井下得到了初步的应用。针对目前煤矿井下掘进工作面掘锚装备视觉定位方法存在的合作标靶结构复杂、标定繁琐的问题,结合掘进工作面原有激光指向仪特征,提出一种基于单激光束信息的掘锚装备视觉定位方法。该方法通过分析激光指向仪光斑及光束图像特征,提出了一种基于二维反正切函数拟合的激光光斑中心提取方法和基于Hough直线检测的激光束中心线提取方法,构建了基于点线特征的双目视觉位姿解算模型,得出了掘锚装备在巷道中的实时位姿。最后,为了验证提出的特征提取方法和视觉定位方法的可行性和准确性,在实验室模拟掘进工作面工况环境搭建平台进行了试验。结果表明:基于矿用激光指向仪信息的掘锚装备视觉定位方法具有较高的位姿测量精度。在50 m的测试范围内,机身位置在巷道坐标系下沿X轴、Y轴和Z轴的平均测量误差分别为25.44、58.64、31.08 mm,其最大误差分别为55.16、127.39、63.57 mm;机身姿态在巷道坐标系下的俯仰角、偏航角和横滚角的平均测量误差分别为0.22°、0.22°、0.41°,其最大误差分别为0.29°、0.37°、0.58°。满足煤矿井下巷道施工的定位精度要求。

Design of Robot Welding Seam Tracking System with Structured Light Vision

Robot welding is an important developing direction of welding automation and intelligentization, and automatic seam tracking technology is one of principal research domains. Nowadays, seam tracking system with structured light vision becomes a hot research. Structured light vision seam tracking products abroad are generally very expensive and can only be applied on special occasions. In China, the research of structured light vision seam tracking system is still just on the stage of experiments. A robot real-time seam tracking system with line structured light vision is designed. The hardware system is set up, a filtering method for line structure seam image is improved, and compared with common filtering, it has better effect and characteristic of real time. Two methods, fast template matching and fast Hough transform, to recognize the image coordinates of seam center are improved. Two new image recognition methods, structure element matching and comer detecting, are proposed. The comparison of seam image recognition shows that fast template matching and comer detecting are more precise and stable than the other two methods, and comer detecting is the best in real time. A simultaneous calibration for camera parameters and robot hand-eye is also proposed, and calculation shows that the calibration is effective and feasible. The robot seam tracking tests for linear and folded lap-joint are performed, which are based on the above four image recognition methods, and the results indicate that four image recognition methods are all applicable to real-time seam tracking, and the whole system sufficed for the requirements of real-time seam tracking. Automatic seam tracking with line structured light vision is feasible and has good versatility.

基于误差修正的光面塞规直径高精度自动检定

针对航空制造企业光面塞规大批量检定中存在的检定精度及效率低、人工操作烦琐等不足,提出一种基于机器视觉和激光测径相结合的光面塞规直径测量方法。由激光测径仪测量塞规直径,机器视觉模块同步检测塞规在测径仪光幕当中的姿态,并对测径仪测量值进行修正。同时,利用专用工装配合气浮运动机构实现多个塞规的自动切换及单个塞规的多位置自动测量。试验结果表明,该系统的重复测量标准差为0.16μm,与立光计测量值相比的绝对测量误差≤0.5μm,塞规单端测量时间≤45 s,能够满足生产中的常用光面塞规快速高精度检定要求。

应用多传感器激光视觉的焊接机器人变质心补偿控制

制造、安装、机械传动和温度变化等诸多方面的误差较大,产生补偿机器人变质心偏移度,任务执行途中失去期望位姿,为了提高焊接机器人工作效率,提出应用多传感器激光视觉的焊接机器人变质心补偿控制方法。建立焊接机器人变质心模型,应用激光传感器扫描焊接机器人实际轨迹和位姿的几何特征,获取激光视觉图像标定管道焊缝,结合PLC控制器,调整焊接机器人轨迹和位姿,消除惯性力对变质心位移的干扰,实现焊接机器人变质心补偿控制。实验结果表明,在X-Y坐标面、Z-X坐标面和Z-Y坐标面上的变质心补偿轨迹均与期望轨迹误差5 cm左右,高度重合;控制后,焊接机器人惯性位移在0.1 cm之内,近似于零,惯性力不再引导焊接机器人移动,可以稳定停靠在目标位置,变质心控制性能好。

基于激光视觉的光照变化图像分割研究

光照变化图像在分割过程中,若不能有效抑制图像中的噪声,会直接影响图像的分割精度,为提升图像的分割效果,提出基于激光视觉的光照变化图像分割方法。将图像输入至激光视觉系统中实施去噪、目标增强等处理,过程中通过该系统中的控制传感器,严密跟踪图像处理流程,提升图像的处理效果;获取图像直方图,根据直方图的平滑结果确定光照变换图像灰度直方图阈值选取范围,对图像实施区域划分获取对应的阈值,根据确定的阈值完成光照图像的精准分割。实验结果表明,使用该方法开展图像分割时,有效去除了图像噪声,分割效果较好,能够实现对图像目标的高效、准确分割。

激光清洗效果评价体系及系统研究

为判断激光清洗效果,优化激光清洗工艺参数,对激光清洗效果进行评价就显出重要性。利用1000 W级激光清洗机进行激光清洗实验,结合实验数据,运用机器视觉技术及表面性能测试技术从宏观和微观两个方面对激光清洗后的效果进行评价,提出清洁度、粗糙度、清洗效率3种评价指标;根据评价指标制定激光清洗效果等级评价体系“Lcq”,基于此体系开发激光清洗效果智能评估系统。测试结果表明:该激光清洗效果等级评价体系客观准确,系统运行稳定,可指导激光清洗设备现场作业,从而更快地调整激光清洗机参数,提高清洗效果。