一种基于双相机焊缝跟踪的球形储罐爬壁机器人定位方法

针对具有很少甚至没有地标和缺乏良好照明条件的大型液化石油气球形储罐环境下,仅依靠里程计以及惯性测量单元的爬壁机器人定位精度较低,且存在较为明显的累积误差的问题,提出了一种新的解决方案,通过跟踪球形储罐表面焊缝相对于机器人的运动来改善机器人定位精度。首先,通过配备辅助光源,利用安装在机器人两侧的CCD相机进行图像采集,并实时对图像进行二值化处理,识别出焊缝特征区域并输出相应的检测信号,通过相邻时刻检测信号增量计算来估计机器人的相对位置;接着,提出了一种改进加权融合算法,用以实现各传感器之间的数据融合,该算法结合了自适应加权融合算法以及基于最小二乘原理的加权融合算法的优点,以对各加权因子重新分配权值的思想来对各个传感器数据进行最终融合,提高了测量精度以及动态适应性。最后,在实际环境以及虚拟环境下对该方法进行了实验评估,验证了该方法的有效性。实验结果表明,在没有良好照明条件及地标的球形储罐环境下,该方法可以保证爬壁机器人定位的准确性。     

爬壁机器人焊缝高效修形技术研究

针对大型容器建造过程中焊缝余高打磨效率低、质量不稳定、自动化程度低等问题，首先应用力学原理探讨了加工力大小及方向对爬壁式焊缝修形机器人影响，优化了爬壁机器人焊缝修形刀具姿态等条件。在此基础上进一步通过切削反力及修形效率的建模分析，提出了一种适用于爬壁机器人的焊缝修形“锉铣”新技术，设计并研制了相应的“锉铣”刃具和焊缝修形爬壁机器人系统；实验结果表明，机器人系统能对焊缝余高进行高效切削，且加工质量优于国家和行业标准的相关规定，同时还较传统加工方法显著改善了生产环境。     

基于智能PID算法的爬壁机器人位置伺服控制方法

针对爬壁机器人实际与理想作业位置偏差过大的问题,提出了基于智能PID算法的爬壁机器人位置伺服控制方法。通过分析爬壁机器人的运动状态,构建爬壁机器人的运动学模型,获取爬壁机器人的运动规律和其位姿变化特征。运用智能PID算法对爬壁机器人的位置实施伺服控制,利用遗传算法不断调节机器人控制参数,得到最佳适应度函数,生成全局最优的PID控制参数。根据爬壁机器人运动状态得出控制终止条件,获取最终的位置伺服控制结果,实现爬壁机器人位置伺服控制。实验结果表明,所提方法的位置伺服控制稳定性较好,能够有效提高位置伺服控制精度,增强抗干扰性。     

基于MEMS微型爬壁机器人自主控制系统设计

介绍了一种基于MEMS永磁轮式微型爬壁机器人。研究多种因素限制条件下微型爬壁机器人自主控制系统硬件和软件的设计方法。控制硬件主要包括微控制器、电磁微马达驱动器、无线收发器、电源管理、微型摄像头等模块。设计基于嵌入式实时操作系μC/OS-II的模块化控制程序,对爬壁微机器人不同行为的控制方法进行了研究,包括运动控制、能源监测、图像采集、无线传输等。制作了相应的原理样机,通过实验证明了所设计控制系统的可行性。     

基于工业机器人弧焊的焊缝识别研究

从视频或图像序列中准确有效提取出焊缝位置信息是实现工业机器人自主弧焊的基础和研究关键。根据主动视觉识别技术,利用CCD摄像机和激光视觉传感器搭建硬件系统,进行焊缝图像采集,提出了一种基于图像预处理与后处理相结合的焊缝检测方法,以适应于复杂焊接环境情况下的焊缝识别。对原始采集图像做图像滤波等预处理工作滤除噪声和提高焊缝图像清晰度,通过改进图像分割和孤点滤波滤等图像后处理提取出焊缝感兴趣区域,最后通过几何分析法和斜率法提取出焊缝中心线和特征点,得到准确的焊缝位置特征信息,从而完成图像信息到焊缝位置信息转换。实验仿真结果表明,所提出算法在搭建硬件系统上的焊缝识别率达到92%,能够把误检率控制在11%左右,能够在复杂背景情景下准确提取出焊缝位置信息,满足工程应用要求。     

基于SLAM的爬壁机器人自主移动研究

目前在研的爬壁机器人基本采用遥控模式进行控制，尚不具备全自主移动功能。针对大型钢结构外表面巡检、涂装等作业，提出了一种基于SLAM(Simultaneous Localization And Mapping)的自主移动模式。通过建立机器人相关模型，在完成SLAM建立环境地图的基础上，实现基于路径规划算法的自主移动，并通过试验表明在室内环境下，Cartographer算法得到的地图边缘和障碍物细节更加完整，建图准确度更高，其相对误差绝对值的最大值为2.67%，算法可信度高。自主移动试验中，爬壁机器人基本能够到达目的地，符合机器人进行巡检等工作的需要，表明基于SLAM的爬壁机器人应用于大型钢结构自主移动具有可行性。     

四足爬壁机器人的航向偏差感知及步态调整

针对GIS管道内部故障缺陷检测，研究设计了一种四足爬壁机器人，可完成在水平和垂直管道内壁的整周爬行和轴线方向爬行。为解决管道四足爬壁机器人长时间运动产生的严重偏航问题，提出了一种不依赖外部传感器的姿态自感知与调整算法，实现了机器人姿态的自调整，保证运动的准确性。通过使用所研究的爬壁机器人在管道内进行爬行实验，包括姿态调整与爬壁运动两项运动验证，机器人实现了在水平和垂直管道内的整周爬行和轴线方向的爬壁运动，爬行运动过程中稳定性高，且在出现偏航超过设置状态时，可自发进行调整，验证了所规划的基本运动步态和姿态感知与调整算法的有效性。     

高机动性小型清洁爬壁机器人的研究

基于负压吸附原理,设计一种新型高机动性小型清洁爬壁机器人.描述机器人机构,对机器人爬壁稳定性和可靠性进行详细分析,给出其控制系统.该爬壁机器人具有小巧灵活、机动性好的特点,且有良好的应用前景.     

爬壁机器人双目视觉障碍检测系统

针对壁面障碍物的不确定性,设计了一种爬壁机器人双目视觉障碍检测系统。具体包括搭建双目平行视觉系统,根据双目视觉理论对摄像机进行标定,获取相机标定参数;通过标定参数和极线约束对双目图像进行校正,解决图像畸变不共面问题;利用块搜索模型和相似度函数获取视差,保证视差获取的快速性与鲁棒性;最后提出一种障碍物检测算法:建立壁面检测模型约束检测范围,引入面积阈值,过滤干扰并实现障碍物提取,提出一种障碍物定位算法,通过宽度、深度与偏距三个方面对障碍物进行定位,同时,通过线性插值解决障碍物中心视差丢失问题。实验结果表明:在保证实时性的基础上,该系统能够有效检测前方障碍物且准确提取率能够达到95.9%,定位误差为4.91%,满足爬壁机器人检测要求。     

基于视觉特征的厚板机器人焊接焊缝轮廓的有效提取

基于结构光的主动视觉传感技术是用于厚板机器人自动焊接中检测焊缝信息的一种有效途径。针对焊接过程中采集的焊缝图像包含或不包含电弧区域的两种情形,提出了一种基于视觉特征的适用于典型接头的激光条纹提取的通用算法。该算法利用Gabor滤波生成方向特征图,阈值分割后利用最近邻聚类算法对激光条纹和干扰数据进行聚类,根据聚类结果获得各类数据点在图像中的空间跨度,且只保留跨度值大于平均跨度的类,实现激光条纹的粗略提取;在从粗略提取结果中获取首个激光条纹分段的前提下,利用后续待识别的类与前一次已提取的激光条纹分段的欧式距离和类的自身跨度,设计了一个能提取激光条纹后续分段的竞争策略,从而实现激光条纹主体轮廓的准确提取;根据激光条纹视觉投射特点,设计了一个提取主体轮廓各分段之间的激光条纹细节的有效规则,实现激光条纹细节的提取。利用厚板T形接头和对接接头焊接中采集的焊缝图像验证了文中激光条纹提取算法的有效性,为厚板机器人自主焊接智能系统面向多种接头焊接应用的集成提供了可能。     

Robot welding seam tracking method based on passive vision for thin plate closed-gap butt welding

A passive vision sensor is added to a teaching and playback welding robot for pulsed gas tungsten arc welding, which is normally used in the welding of thin plate closed-gap butts. This paper presents a seam tracking method based on this sensor through period visual measurement of the offset between the torch and the seam center. A robust image processing algorithm is developed to extract the seam center. A kind of ARX(auto-regressive with exogenous input) model is studied to describe the relationship between the rectifying voltage and the offset. The fuzzy PID(proportional-integral-derivative) seam tracking controller is analyzed and designed in consideration of various offsets based on the ARX model. The experimental results on straight line weld display a good seam tracking capability of the proposed method.     

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.     

Arc welding robot system with seam tracking and weld pool control based on passive vision

The automatic control in square-wave alternating current (AC) gas tungsten arc welding (GTAW) is significant for a “teach and playback” robot to overcome the variation of the seam trajectory and the seam gap in the welding process. This paper presents a welding robot system based on the real-time visual measurement in the different levels of the welding current. The primary objective is to measure the offset of the torch to the seam center and the size of seam gap by passive vision, track the seam and control the weld pool in real time. A novel visual image analysis algorithm was developed for seam tracking and seam gap measuring, free from robot calibration. The control algorithm based on the knowledge base was established to control the weld formation by regulating the welding current and wire feed rate. The welding practice for the rocket storage tank demonstrates the efficiency of the proposed system.     

Passive vision based seam tracking system for pulse-MAG welding

Welding robots have been widely used in manufacturing process to substitute for human welders. However, most of them are rigid and cannot adjust to variations in the weld seam positions caused by natural welding environmental factors. To address this problem, this paper presents a passive vision-based robotic welding system, which can realize the seam tracking function for pulse-MAG welding. In this paper, the light spectrum of the welding process is analyzed to determine the optical filters used during the image capture. Then, a robust image processing method is proposed to extract the offset from the image which contains much noise. The transformation formula is calibrated to obtain the relationship between the image coordinate system and the robot coordinate system. The tracking strategy is designed to improve the tracking precision and the stability of the welding process. Finally, experiments are conducted on straight line and curved line seam to verify the feasibility of the developed system.     

一种用于球罐焊接机器人焊缝实时跟踪的线阵CCD传感器

根据球罐焊接机器人焊缝跟踪的要求和特点,设计了一种基于线阵CCD的视觉传感器。传感器所用线阵CCD的分辨率高,光电处理系统均由硬件实现。传感器与焊接机器人组成的焊缝跟踪系统焊缝偏差检测精度好、实时性好,并且可以避免焊接时弧光的干扰,能够满足球罐制造时多层多道焊接焊缝跟踪的工艺要求。     

Real-time image processing for vision-based weld seam tracking in robotic GMAW

Image capturing and processing is important in using vision sensor to effectively track the weld seam and control the weld quality in robotic gas metal arc welding (GMAW). Using vision techniques to track weld seam, the key is to acquire clear weld images and process them accurately. In this paper, a method for real-time image capturing and processing is presented for the application in robotic seam tracking. By analyzing the characteristic of robotic GMAW, the real-time weld images are captured clearly by the passive vision sensor. Utilizing the main characteristics of the gray gradient in the weld image, a new improved Canny edge detection algorithm was proposed to detect the edges of weld image and extract the seam and pool characteristic parameters. The image processing precision was further verified by using the random welding experiments. Results showed that the precision range of the image processing can be controlled to be within ±0.3 mm in robotic GMAW, which can meet the requirement of real-time seam tracking.     

An automated weld seam tracking system for thick plate using cross mark structured light

This paper presents a weld seam tracking system using cross mark structured light. The hardware of the proposed system consists of a two degrees of freedom (DOF) welding robot, a camera with cross mark structured light, and two computers. The system has two parts namely visual sensing and motion control. In the visual sensing part, the cross mark of the structured light is utilized to set a region of interest (ROI). In the ROI, an adapted line fitting algorithm is employed to estimate the lines. Then, intersections of the lines are computed and used as the pin points for templates creating. During the matching process, a modified template matching is used to detect the edges of V-groove weld seam. By using this technique, a huge computational cost in image processing can be reduced, and therefore the tracking can be made in real time. The position based visual servoing with proportional-derivative(PD) and velocity feedback controller is designed for seam tracking. The experimental results show that the proposed method performs the real-time tracking efficiently with sufficient accuracy.     

A visual seam tracking system for robotic arc welding

A novel circular laser three-dimensional (3D) scanner is developed instead of a one-dimensional laser spot scanner (1D) and two-dimensional (2D) striped laser light. Using the proposed circular laser scanner, a seam tracking system is constructed, and architecture based on the Visual C++ and RAPID languages which determines the cooperation among image processing modules is carried out. Finally, real seam tracking experiments are investigated and the error analysis is carried out. The results show that: (1) the proposed system can realize seam tracking; (2) the precision of this system is affected by the light scanning system, calibration results, image processing, and the welding seam 3D algorithm—the tracking accuracy is satisfied for robotic arc welding; (3) this system can be used in a welding robot system as a weld seam finder, flatness detector, and weld seam tracker.     

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.