色噪声下卡尔曼滤波焊缝跟踪算法与试验研究

精确控制激光束使其始终对中并跟踪焊缝是保证激光焊接质量的前提．以大功率光纤激光焊接Type304不锈钢为试验对象,研究一种有色噪声环境下应用卡尔曼滤波最优状态估计预测激光束与焊缝路径偏差的方法．使用高速红外视觉传感器摄取焊接区红外热像,提取焊缝位置参数并构成状态向量,建立基于焊缝位置参数的系统状态方程和焊缝位置测量方程．针对系统动态噪声为有色噪声,通过扩展状态变量的方法建立有色噪声环境下的卡尔曼滤波算法,对焊缝位置进行最优状态估计并得到最小均方差条件下的焊缝偏差最优预测值,消除系统噪声对焊缝偏差测量的影响．焊接试验结果表明新方法可有效抑制有色噪声干扰并提高焊缝跟踪精度．     

基于视觉与电弧多信息传感的焊缝跟踪算法

随着焊接传感技术以及信号处理技术的快速发展,人们越来越注重对焊接过程质量控制的研究,而多传感器信息融合技术是焊接质量控制的一种重要的方法,采用该技术实现熔化极气体保护焊(MIG)脉冲焊接质量的自动控制。该技术采用了视觉传感器和电弧传感器将采集的不同的描述信息进行了有效的特征提取和传输,并运用多传感器信息融合算法进行焊缝的跟踪。视觉传感器利用工业电荷耦合元件(CCD)获取图像信息控制焊炬的横向偏差信息,电弧传感器利用数据采集卡获取电流信息控制焊炬的高度互补偏差信息和横向的冗余信息,将两种传感器得到的冗余信息和互补信息在特征层下进行融合实现焊接过程横向和高度的纠偏控制。冗余信息的融合可以实现视觉传感的图像去噪,而互补信息的融合可以进一步提高焊缝跟踪的精度。实验结果表明,所提算法能够较好地提高焊接的质量,从而也验证了算法的有效性和合理性。     

经济型焊缝跟踪视觉传感器的研制

利用普通的焊接护目镜玻璃制作了熔池图像视觉传感器,在304#不锈钢等离子弧焊缝跟踪试验中获得的图像是足够清晰的,能够清楚地看到接缝和焊接熔池.在工厂车间里进行的试验证明:这种低成本的视觉传感器可以用于304#不锈钢的等离子弧焊接过程的实时焊缝跟踪和控制.     

超声波传感器在直角焊缝自动焊接中的应用

机器人进行船体格子型焊缝焊接过程中,完全依靠电弧传感器无法完全得到其周围环境信息,造成了自动化焊接的困难。设计了一种直角焊缝自动焊接的方法,利用超声波传感器代替机械式传感器,获得周围障碍信息,用电弧传感器获得焊缝的偏差信息,通过对机器人合理的控制,完成了直角焊缝的自动跟踪焊接。     

基于激光视觉检测的焊缝自动跟踪系统研究

旨在提高焊缝跟踪精度和焊接质量,提出一种基于激光检测自动焊缝跟踪系统。该系统由激光视觉部件、控制器、步进电机和十字滑架组成。当系统工作时,由激光视觉部件检测焊缝的当前位置与目标位置之间的偏差,控制器基于该偏差确定纠偏量,步进电机驱动十字滑架以纠正焊枪横向与纵向位置偏差。搭建出系统物理样机,进行了焊缝跟踪试验。试验结果表明,基于激光视觉检测的焊缝跟踪误差可控制在0. 5 mm 之内,其在精密焊接领域具有较大应用前景。     

基于宏微机器人的焊缝跟踪研究

提出了一种基于宏微运动机器人的焊缝跟踪方法．首先,通过若干点的简单示教获得焊缝位置信息,并通过拟合建立焊缝模型．在该模型的基础上,对机器人的宏动进行运动规划．采用激光结构光视觉测量焊缝坐标,并根据焊缝图像偏差控制机器人的微动．结合机器人的宏动规划运动和微动自动调整,实现大范围、高精度的焊缝跟踪．利用宏微运动平台进行了焊缝跟踪实验,实验结果验证了所提出方法的有效性．     

焊接机器人曲线焊缝的自动跟踪控制

为实现焊接机器人对曲线焊缝的自动跟踪,提出一种简便的位姿实时调整策略和协调视觉跟踪与机器人运动的视觉伺服控制方法。建立了曲线焊缝视觉跟踪过程中焊接机器人期望位姿的数学模型;设计了一种上下层结构的模糊视觉伺服控制器,通过建立焊缝特征点像素坐标偏差与末端轴旋转角度之间的关系模型,动态确定模糊论域的大小,在机器人期望位姿的基础上仅仅通过调整末端轴的旋转量来保证图像特征点始终存在于相机视场内。通过模拟焊接机器人自动跟踪曲线焊缝的实验,验证了所提策略与方法的有效性。     

基于CCD焊缝自动跟踪系统的设计

本文所研究的是为了保证焊接的质量而进行的焊缝自动跟踪系统,采用面阵CCD视觉传感器来摄取焊接点前焊缝图像,通过采集卡把图像采集到内存,进行图像处理,获得焊缝与焊枪的偏差量,再经由上位机向PLC发送控制信号,来达到精确焊缝跟踪的目的.     

不连续焊缝特征点检测方法研究

船舶制造中,由于船舱底部排水孔的存在,形成不连续焊缝,影响了焊接速度.为了提取排水孔特征点信号,通过对旋转电弧传感器提取焊接电流信号和激光传感器提取焊缝图像信息进行实验研究,结果表明2种传感器满足要求.最终选用旋转电弧传感器检测排水孔起始位置,视觉传感器控制机器人位姿与检测排水孔终点位置.     

基于双模控制的焊接机器人焊缝自动跟踪系统

该文提出了一种基于双模控制的焊接机器人焊缝自动跟踪系统.系统中应用新一代激光焊缝传感器测量焊缝的位置,并采用Fuzzy-P双模分段控制进行焊缝的纠偏.系统中采用DSP作为核心控制器产生控制信号,驱动焊枪横向步进电机和纵向步进电机动作,实现焊接机器人焊枪对焊缝的实时自动跟踪.实验证明,基于双模控制的焊缝自动跟踪系统可以实现焊接机器人焊枪对焊缝的实时自动跟踪.该系统完全满足实际焊接工程的需要.     

基于图像处理的模糊控制埋弧焊缝跟踪研究

焊接工艺由于其本身加工过程及环境的复杂性,使得焊缝难以精确跟踪.针对埋弧焊系统,采用视觉传感器进行焊缝跟踪,得到误差信号后,使用分段拼接控制的方法,使误差快速收敛,且减小超调.首先对焊缝图像进行预处理,使用自动化的阈值进行分割得到焊缝区域,应用边缘检测和Hough变换得到焊缝边缘,计算得到焊缝中心位置、焊矩和焊缝偏差;然后采用模糊增量式分段PI控制处理误差,当误差较大时采用增量式PI控制消除误差,当误差较小时应用模糊控制,设计了隶属度表以及模糊推理机制,采用小误差的模糊控制有效地抑制了超调,使误差快速收敛.最终仿真结果显示,使用PI混合模糊控制后,超调量被控制在0.2个单位以内,调节时间小于1 s,基本实现了对焊缝的快速准确跟踪.     

Research on the Real-time Tracking Information of Three-dimension Welding Seam in Robotic GTAW Process Based on Composite Sensor Technology

Aiming at the shortcomings of teaching-playback robot that can??t track the three-dimensional welding seam in real time during GTAW process, this paper designed a set of composite sensor system for tracking the three-dimensional welding seam based on visual sensor and arc sensor technology, which can effectively acquire three-dimensional welding seam information, such as clear images of seam and pool and stable arc voltage signals. The characteristic values of weld image and arc voltage signals were accurately extracted by using proper processing algorithm, and the experiments have been done to verify the precision of processing algorithms. The results demonstrate that the error is very small, which is accurate enough to meet the requirements of the subsequent real-time tracking and controlling during the welding robot GTAW process.     

基于结构光的机器人弧焊混合视觉伺服控制

A novel hybrid visual servoing control method based on structured light vision is proposed for robotic arc welding with a general six degrees of freedom robot. It consists of a position control inner-loop in Cartesian space and two outer-loops. One is position-based visual control in Cartesian space for moving in the direction of weld seam, i.e., weld seam tracking, another is image-based visual control in image space for adjustment to eliminate the errors in the process of tracking. A new Jacobian matrix from image space of the feature point on structured light stripe to Cartesian space is provided for differential movement of the end-effector. The control system model is simplified and its stability is discussed. An experiment of arc welding protected by gas CO_2 for verifying is well conducted.     

双队列控制的激光视觉焊缝跟踪系统研究

设计与实现了一套激光视觉引导的焊缝自动跟踪系统,包括系统的整体硬件构成和关键算法。在工业机器人末端安装激光视觉传感器构成焊缝跟踪系统的硬件部分。采用小波变换滤除焊缝图像噪声,采用改进的Steger算法和Hough变换方法提取激光条纹中心直线,进一步提取出焊缝位置。提出一种双队列控制策略,在采集的焊缝特征点的基础上进一步插值,从而实现激光视觉引导的焊缝自动与平滑跟踪。实验结果表明,该系统具有较好的跟踪精度,能满足工业实际需求。     

Computer vision technology for seam tracking in robotic GTAW and GMAW

Due to ever increasing demand in precision in robotic welding automation and its inherent technical difficulties, seam tracking has become the research hotspot. This paper introduces the research in application of computer vision technology for real-time seam tracking in robotic gas tungsten arc welding (GTAW) and gas metal arc welding (GMAW). The key aspect in using vision techniques to track welding seams is to acquire clear real-time weld images and to process them accurately. This is directly related to the precision of seam tracking. In order to further improve the accuracy of seam tracking, in this paper, a set of special vision system has been designed firstly, which can acquire clear and steady real-time weld images. By analyzing the features of weld images, a new and improved edge detection algorithm was proposed to detect the edges in weld images, and more accurately extract the seam and pool characteristic parameters. The image processing precision was verified through the experiments. Results showed that the precision of this vision based tracking technology can be controlled to be within ±0.17 mm and ±0.3 mm in robotic GTAW and GMAW, respectively.     

Autonomous Detection of Weld Seam Profiles via a Model of Saliency-Based Visual Attention for Robotic Arc Welding

This paper presents a method of autonomously detecting weld seam profiles from molten pool background in metal active gas (MAG) arc welding using a novel model of saliency-based visual attention. First, a vision sensor based on structured light is employed to capture laser stripes and molten pools simultaneously in the same frame. Second, to effectively detect the weld seam profile from molten pool background for next autonomous guidance of initial welding positions and seam tracking, a model of visual attention based on saliency is proposed. With respect to the enhanced effect of saliency, the proposed model is much better than the classic models in the field. According to the comprehensive saliency map created by the proposed model, the weld seam profile can be extracted after threshold segmentation and clustering are applied to it in turn. Third, different weld seam images are used to demonstrate the robustness of the proposed methodology and last, to evaluate the performance of the proposed method, a measure called profile extraction rate (PER) is computed, which shows that the extracted weld seam profile can basically meet the requirements of seam tracking and the guidance of welding torches.