基于视觉跟踪的大型容器焊接机器人的研制

研制了一种基于 CCD视觉焊接轨迹跟踪技术的无导轨焊接机器人。该机器人由新颖的 CCD视觉传感器来实时检测机器人行走机构与焊枪的跟踪位置偏差量 ,并据此由微机控制系统实现对机器人行走机构与焊枪的二级自动跟踪。该机器人采用永磁式柔性磁轮机构作为支持行走单元 ,使焊接机器人具有了节省导轨、轨线适应性强的优点。焊接工艺评定试验结果表明 ,此焊接机器人自动跟踪精度高 ,焊缝质量好 ,工作稳定可靠。     

基于机器视觉的焊接轨迹跟踪技术

本文针对海上风电装备高焊接质量和可靠性要求,提出了基于机器视觉的焊接轨迹跟踪技术,本方案主要由五部分组成:视觉传感系统、焊接机器人、控制系统、送丝系统、图像处理系统,其中视觉传感器系统是整个焊缝跟踪系统的核心部分,包括工业CCD摄像机、激光发生器和窄带通滤光片三个重要元件,通过对比实验确定了视觉传感器与工件、焊枪之间的位置关系,并设计了视觉传感器夹具,对工业CCD摄像机标定,结果表明:本文所依托项目采集的图像质量较优,满足焊缝跟踪的图像处理需求,图像处理算法能有效地凸显焊缝图像中激光带信息和去除无关信息,实现强干扰复杂环境下焊缝特征点的准确识别。     

基于图像处理的焊缝实时跟踪技术研究

为了提高焊接产品质量及生产效率,改善工人恶劣的劳动条件,运用视觉传感器采集焊缝图像,实时传送至计算机中进行图像处理。提出了焊缝实时跟踪系统的原理,设计了合理的焊接机构。选用ABB IRB1410弧焊机器人,用RobotStudio软件建立了机器人焊接工作站。通过灰度线性拉伸法处理图像后,焊缝轮廓清晰,对比度明显提高。利用图像骨架法准确提取到焊缝中心线,设计了焊缝实时处理系统,可以快速地处理图像。在机器人焊接时,控制焊枪始终与焊缝中心对齐,即可以实现焊缝实时跟踪焊接。实验结果表明,得到的图像处理流程可靠,方法准确且适应性强,设计的焊缝实时跟踪系统应用广泛。     

基于机器视觉的焊接跟踪技术的应用研究

在传统接触式埋弧焊焊缝跟踪系统的基础上,通过摄像机进行焊接坡口复合跟踪。焊接前,对焊接坡口进行数据采集,运用计算机进行图像处理,生成虚拟焊缝识别系统。焊接时,虚拟焊缝识别系统控制焊枪,实现焊缝自动跟踪,从而达到实时和精确．保证了焊接质量。     

基于GMR的二维焊缝检测传感器的设计与实现

简述了基于GMR的二维焊缝检测传感器。为解决焊接加工自动化过程中焊缝的跟踪检测，将GMR焊枪悬浮高度传感器、焊缝检测传感器以及相应的微处理器有机集成，从而使传感器实现小型化智能化，减少对工件检测的死区范围。该传感器与自动焊接机组成的焊缝跟踪系统，焊缝跟踪精度高、响应速度块，减少了焊缝轨迹空间捕捉算法的复杂挫。     

深度视觉技术在焊接轨迹识别中的研究综述

机器视觉在机器人自动焊接领域得到广泛应用。图像识别可以对不同的焊接对象进行有效识别,而识别及跟踪焊缝轨迹是实现自动焊缝的关键问题。分析了焊接轨迹跟踪传感器的发展历程,讨论了视觉传感器在焊缝识别跟踪上的应用,对焊缝图像处理方法进行了分析,总结了视觉焊缝跟踪技术现状。分析结果表明,TOF相机更适合于焊缝跟踪应用,研究鲁棒性强的图像去噪方法和基于深度学习的焊缝图像分割方法是未来的发展方向,同时提出了稳定性好、精度高,效率好的深度视觉焊缝跟踪控制系统思路。     

焊缝跟踪应用的线激光视觉伺服控制系统

设计了一套由三轴直角坐标机器人、线激光传感器和工业计算机组成的焊缝跟踪系统。研究了该系统所涉及的测量原理、特征点测量方法和基于模糊自适应的控制方法。通过高斯核相关算法(KCF)在焊接过程中实时检测焊缝特征点,并根据测量原理计算获得特征点相对于相机坐标系的三维坐标值。设计了一种自适应模糊控制器,通过自适应模糊控制器计算坐标的偏差值和偏差变化率得到焊枪末端运动轨迹的控制量,同时对模糊控制器的输入输出论域、模糊规则和隶属函数进行实时动态更新。实施了焊缝跟踪实验。结果显示:采用最大焊接电流为350 A的惰性气体保护焊(MIG),在强烈弧光和飞溅的干扰下,该系统能实时跟踪焊接工件,跟踪精度为0.325 3mm,传感器测量频率为20Hz。焊接过程中焊枪末端运行平稳,焊缝轨迹跟踪准确,且抗干扰能力,能满足焊接应用要求。     

客车底盘骨架对接焊缝的视觉跟踪系统

根据客车底盘骨架对接焊缝的特点,设计了基于激光视觉传感的焊缝跟踪系统。针对图像处理运算量大的特点,为减少图像处理时间,提出了加窗处理算法。视觉传感系统采集并分析焊缝图像得出焊缝偏差,控制系统根据焊缝偏差指导机器人移动焊枪进行实时纠偏控制。实验结果表明,所设计的焊缝跟踪系统满足焊缝跟踪的精度要求,可以用于底盘骨架的焊接。     

视觉传感螺旋钢管埋弧焊内焊焊缝自动跟踪系统

针对螺旋钢管埋弧焊内焊,研制一种基于视觉传感的焊缝自动跟踪系统。以视频摄像机为传感器,在焊接点前方300 mm处检测递送边钝边棱边。对视频信号进行放大、整形和二值化处理,获得对应于钝边棱边的脉冲信号,以帧中心为基准,利用单片机进行偏离方向判断及偏差值计算,并将偏差信号以脉冲方式送入交流伺服电动机驱动器,由交流伺服电动机控制焊枪滑移台移动,确保焊丝实时对准焊缝。试验结果表明：采用视频摄像机识别钝边棱边实现焊缝自动跟踪的方法是可行的。系统响应周期小于50 ms,跟踪精度达±0.3 mm。     

基于EKF的GMAW实时焊缝跟踪研究

熔化极气体保护电弧焊（gas metal arc welding,GMAW）焊接过程存在惯性大、时滞大等非线性特征以及不确定的干扰因素。为了提高焊接质量,文章提出了一种基于扩展卡尔曼滤波器（extended kalman filter,EKF）算法的焊缝实时跟踪技术,采用霍尔电流传感器来捕捉实时焊接电流。为了实现焊枪摆动中心始终对准焊缝中心,文章通过有限长单位冲激响应滤波器（finite impulse response,FIR）对采集的电流进行滤波,并建立提取焊枪高度和水平偏差的数学模型,采用EKF实现控制焊枪。通过在有垂直和水平方向偏差的焊缝上进行实验验证,结果跟踪精度可达到±0.25 mm,所以该方法可满足机器人实时跟踪的要求。     

Development of a real-time laser-based machine vision system to monitor and control welding processes

In this study, a laser-based machine vision system is developed and implemented to monitor and control welding processes. The system consists of three main modules: a laser-based vision sensor module, an image processing module, and a multi-axis motion control module. The laser-based vision sensor is designed and fabricated based on the principle of laser triangulation. By developing and implementing a new image processing algorithm on the platform of LabVIEW, the image processing module is capable of processing the images captured by the vision sensor, identifying the different types of weld joints, and detecting the feature points. Based on the detected feature points, the position information and geometrical features of the weld joint such as its depth, width, plates mismatch, and cross-sectional area can be obtained and monitored in real time. Meanwhile, by feeding these data into the multi-axis motion control module, a non-contact seam tracking is achieved by adaptively adjusting the position of the welding torch with respect to the depth and width variations of the weld joint. A 3D profile of the weld joint is also obtained in real time for the purposes of in-process weld joint monitoring and post-weld quality inspection. The results indicate that the developed laser-based machine vision system can be well suited for the measurement of weld joint geometrical features, seam tracking, and 3D profiling.     

用于焊缝位置识别的视觉模型设计与试验

理论与试验研究用于焊缝位置识别的视觉模型,该模型主要由弹性梯度下降训练法BP神经网络组成.在焊接工艺条件下,使用视觉传感器获取焊接区熔池图像,并选取特定区域进行中值滤波与图像灰度变换处理以增强被测对象的特征.在此基础上,计算和处理熔池特性参量(熔池图像质心差值、质心位移、质心移动速度)以及相对应的焊缝与电弧之间的偏差值,将其输入所设计的神经网络进行网络权值参数训练和推理学习,从而建立基于BP神经网络、具有一定认知和环境适应能力的焊缝位置识别视觉模型.对该模型进行通用性检验,试验结果表明该模型通过熔池特性参量可以较精确地识别焊缝位置.     

采用微型排水罩的药H焊丝水下焊接焊缝自动跟踪系统

针对水下焊接图像噪声大、清晰度差的特殊情况,设计并研制成功一套药芯焊丝水下焊接的视觉传感焊缝自动跟踪系统.该系统由视觉传感、图像预处理及偏差识别系统,控制系统和执行机构等组成.采用小波多尺度变换进行水下焊缝图像边缘提取,结果保留大多数的细节,得到有利于焊缝位置特征提取的预处理图像,在一种基于二值图像的焊缝中心位置识别算法计算后,得出焊缝偏差.在理论分析视觉传感焊缝自动跟踪系统模型的基础上,设计满足跟踪精度要求的规则自调整模糊算法.对影响跟踪系统精度的因素进行分析,并采用相应的措施以提高系统的精度.通过药芯焊丝湿法水下焊接焊缝自动跟踪试验表明,采用规则自调整模糊控制能够达到较高的焊缝跟踪精度.在斜线、折线和曲线三种不同形状待焊焊缝的跟踪试验中,系统都能满足药芯焊丝水下自动焊接的要求.经过改进微型排水罩,进行微型排水罩局部干法药芯焊丝水下焊接焊缝跟踪试验,采用规则自调整模糊控制,不仅得到满意的焊缝跟踪效果,还改善焊缝成形质量.     

Visual Tracking System for Welding Seams

To track the narrow butt welding seams in container manufacture, a visual tracking system based on smart camera is proposed in this paper. A smart camera is used as the sensor to detect the welding seam. The feature extraction algorithm is designed with the consideration of the characteristics of the smart camera, which is used to compute the error between the welding torch and the welding seam. Visual control system based on image is presented, which employs a programmable controller to control a stepper motor to eliminate the tracking error detected by the smart camera. Experiments are conducted to demonstrate the effectiveness of the vision system.     

微间隙焊缝磁光成像卡尔曼滤波跟踪算法

在激光对接焊过程中,实时控制激光束准确对中焊缝是保证焊接质量的前提。针对紧密对接激光焊,研究一种用于微间隙(不大于0.1 mm)焊缝位置识别及跟踪的磁光成像卡尔曼滤波算法。采用磁光传感器实时获取焊接区域的微间隙焊缝磁光图像序列,利用微间隙焊缝磁光图像的灰度梯度特征提取焊缝位置坐标。以焊缝位置及位移量构成状态矢量,建立描述焊缝位置的状态方程和测量方程。同时,假设系统动态噪声和测量噪声为零均值随机分布的高斯白噪声,建立噪声环境下的卡尔曼滤波跟踪算法,计算最小均方差条件下焊缝中心最优预测值,减小系统噪声和过程噪声对焊缝位置测量的影响。试验结果显示,该方法能有效实现激光对接焊微间隙焊缝位置的识别与跟踪。     

Autonomous seam acquisition and tracking system for multi-pass welding based on vision sensor

Automatic welding technology is a solution to increase welding productivity and improve welding quality, especially in thick plate welding. In order to obtain high-quality multi-pass welds, it is necessary to maintain a stable welding bead in each pass. In the multi-pass welding, it is difficult to obtain a stable weld bead by using a traditional teaching and playback arc welding robot. To overcome these traditional limitations, an automatic welding tracking system of arc welding robot is proposed for multi-pass welding. The developed system includes an image acquisition module, an image processing module, a tracking control unit, and their software interfaces. The vision sensor, which includes a CCD camera, is mounted on the welding torch. In order to minimize the inevitable misalignment between the center line of welding seam and the welding torch for each welding pass, a robust algorithm of welding image processing is proposed, which was proved to be suitable for the root pass, filling passes, and the cap passes. In order to accurately track the welding seam, a Fuzzy-P controller is designed to control the arc welding robot to adjust the torch. The Microsoft Visual C++6.0 software is used to develop the application programs and user interface. The welding experiments are carried out to verify the validity of the multi-pass welding tracking system.     

焊缝位姿及焊枪位姿的模型

针对机器人弧焊条件下的特殊要求,建立了焊缝位姿和焊枪位姿模型,以坐标系的形式定量描述焊缝及焊枪的位置和方向,确定了能够准确且严格地描述焊缝的焊接位置和焊枪姿态的参数：焊缝倾角、焊缝转角及焊枪工作角和行走角,并给出了计算方法。上述参数不但计算简便,而且可以直接代表焊接位置和焊枪姿态对焊接质量的影响因素。模型对于焊接工艺的建模与仿真以及机器人焊接的建模与离线编程具有非常重要的意义。     

弧焊机器人焊炬姿态规划系统的研究

为提高弧焊机器人的适应性和自动规划功能，研究了一种空间全位置焊接条件下的焊炬姿态自动规划系统。该系统借助于相应的摄像机标定软件，计算测试软件和焊炬姿态规划软件，协调计算机视觉，信号采集，图像处理等模块检测焊缝的空间信息，然4后查询焊炬姿态优化数据库，运行离线偏程等离子模块，实现自动规划弧焊机器人的焊炬姿态，采用研制的爆炬姿态规划系统在空间全位置条件下进行焊接实验，结果表明，该系统能保证爆接中焊缝的成形良好。     

基于视觉的脉冲TIG焊接熔宽模糊控制系统

利用视觉传感和计算机图象处理技术从试件下面检测焊缝熔宽信息,设计了一套脉冲TIG焊接熔宽模糊控制系统。根据脉冲TIG焊接电弧的弧光特性及其对视觉检测的影响,提出了一种独特的象采集和控制方法。试验结果表明,该系统具有良好的控制效果和自适应能力。     

Multi sensor data fusion for improving performance and reliability of fully automatic welding system

This paper presents a new generation of system for pressure vessel and shipbuilding. Typical pressure vessel and ship building weld joint preparations are either traditional V, butt, fillet grooves or have narrow or semi narrow gap profiles. The fillet and U groove are prevalently used in heavy industries and shipbuilding to melt and join the parts. Since the wall thickness can be up to 6 in. or greater, welds must be made in many layers, each layer containing several passes. However, the welding time for the conventional processes such as submerged arc welding (SAW) and flux cored arc welding (FCAW) can be many hours. Although SAW and FCAW are normally mechanized processes, pressure vessel and ship structures welding up to now have usually been controlled by a full time operator. The operator has typically been responsible for positioning each individual weld run, for setting weld process parameters, for maintaining flux and wire levels, for removing slag and so on. The aim of the system is to develop a high speed welding system with multi-torch for increasing the production speed on the line and to remove the need for the operator so that the system can run automatically for the complete multi-torch multi-layer weld. To achieve this, a laser vision sensor and a special image processing algorithm have been made. Also, the multi-torch welding system can be applicable for fine grained steel because of the high welding speed and lower heat input compared to a conventional welding process.