Seam Tracking and Visual Control for Robotic Arc Welding Based on Structured Light Stereovision

A real-time arc welding robot visual control system based on a local network with a multi-level hierarchy is developed in this paper. It consists of an intelligence and human-machine interface level, a motion planning level, a motion control level and a servo control level. The last three levels form a local real-time open robot controller, which realizes motion planning and motion control of a robot. A camera calibration method based on the relative movement of the end-effector connected to a robot is proposed and a method for tracking weld seam based on the structured light stereovision is provided. Combining the parameters of the cameras and laser plane, three groups of position values in Cartesian space are obtained for each feature point in a stripe projected on the weld seam. The accurate three-dimensional position of the edge points in the weld seam can be calculated from the obtained parameters with an information fusion algorithm. By calculating the weld seam parameter from position and image data, the movement parameters of the robot used for tracking can be determined. A swing welding experiment of type V groove weld is successfully conducted, the results of which show that the system has high resolution seam tracking in real-time, and works stably and efficiently.

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.

Seam tracking control for mobile welding robot based on vision sensor

To solve the seam tracking problem of mobile welding robot,a new controller based on the dynamics of mobile welding robot was designed using the method of backstepping kinematics into dynamics.A self-turning fuzzy controller and a fuzzy-Gaussian neural network(FGNN) controller were designed to complete coordinately controlling of cross-slider and wheels.The fuzzy-neural control algorithm was described by applying the Gaussian function and back propagation(BP) learning rule was used to tune the membership function in real time by applying the FGNN controller.To make the tracking more quickly and smoothly,the neural network controller based on dynamic model was designed,which utilized self-learning and self-adaptive ability of the neural network to deal with the partial uncertainty and the disturbances of the parameters of the robot dynamic model and real-time compensate the dynamics coupling.The results show that the selected control input torques make the system globally and asymptotically stable based on the Lyapunov function selected out;the accuracy of the proposed controller tracing is within ±0.4 mm and can satisfy the requirements of practical welding project.

Seam-tracking based on dynamic trajectory planning for a mobile welding robot

A new seam-tracking method based on dynamic trajectory planning for a mobile welding robot is proposed in order to improve the response lag of the mobile robot and the high frequency oscillation in seam-tracking.By using a front-placed laser-based vision sensor to dynamically extract the location of the weld seam in front of torch,the trend and direction of the weld line is roughly obtained.The robot system autonomously and dynamically performs trajectory planning based on the isometric approximation model.Arc sensor technology is applied to detect the offset during welding process in real time.The dynamic compensation of the weld path is done in combination with the control of the mobile robot and the executive body installed on it.Simulated and experimental results demonstrate that the method effectively increases the stability of welding speed and smoothness of the weld track,and hence the weld formation in curves and corners is improved.

Hybrid Visual Servoing Control for Robotic Arc Welding Based on Structured Light Vision

A novel hybrid visual servoing control method based on structured light vision is pro-posed for robotic arc welding with a general six degrees of freedom robot. It consists of a positioncontrol inner-loop in Cartesian space and two outer-loops. One is position-based visual control inCartesian 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 Cartesianspace is provided for dierential movement of the end-e?ector. The control system model is simplifiedand its stability is discussed. An experiment of arc welding protected by gas CO2 for verifying iswell conducted.

VISION-BASED RECOGNITION AND GUIDING OF INITIAL WELDING POSITION FOR ARC-WELDING ROBOT

A method is put forward to realize the recognition and guiding of initial welding position. The weld seams are marked with black lines, which simplify the computational complexity of image processing greatly. A two-time template matching method has been advanced to search for the target point, which is simple and has higher calculation speed. According to the depth computing principle with the special point matching using binocular stereovision, the initial welding position can be confirmed by calculating the middle point of the perpendicular line of two radials in the space. Taking the welding of propellant fuel container for example, good results are obtained with the algorithms. Finally, similar method for terminating welding position is also advanced.

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.

Study on intelligent welding mobile robot with the function of auto-searching weld line

The development of welding robots suitable for specially unstructured working enviroments has been become an important development direction of industrial robot application because large-scale welding structures have been used more and more widely in modern industry. In this paper, an intelligent mobile robot for welding of ship deck with the function of autosearching weld line was presented. A wheeled motion mechanism and a cross adjustment slider are used for the welding robot body. A sensing system based on laser-PSD （position sensitive detector） displacement sensor was developed to obtain two dimensional deviation signals during seam tracking. A full-digital control system based on DSP and CPLD has also been realized to implement complex and high-performance control algorithms. Furthermore, the system has still the function of auto-searching weld line according to the characteristics information of weld groove and adjusting posture itself to the desired status preparing for welding. The experiment of auto-searching welding line shows that the robot has high tracing accuracy, and can satisfy the requirement of practical welding project.

Dynamic modeling and simulation for nonholonomic welding mobile robot

Based on the Newton-Euler method, the dynamic behaviors of the left and right driving wheels and the robot body for the welding mobile robot were derived. In order to realize the combination control of body turning and slider adjustment, the dynamic behaviors of sliders were also investigated. As a result, a systematic and complete dynamic model for the welding mobile robot was constructed. In order to verify the effectiveness of the above model, a sliding mode tracking control method was proposed and simulated, the lateral error stabilizes between -0.2 mm and ＋0.2 mm, and the total distance of travel for the slider is consistently within 4-2 ram. The simulation results verify the effectiveness of the established dynamic model and also show that the seam tracking controller based on the dynamic model has excellent performance in terms of stability and robustness. Furthermore, the model is found to be very suitable for practical applications of the welding mobile robot.

A machine vision approach to seam tracking in real-time in PAW of large-diameter stainless steel tube

Manual monitoring and seam tracking through watching weld pool images in real-time, by naked eyes or by industrial TV, are experience-depended, subjective, labor intensive, and sometimes biased. So it is necessary to realize the automation of computer-aided seam tracking. A PAW (plasma arc welding) seam tracking system was developed, which senses the molten pool and the seam in one frame by a vision sensor, and then detects the seam deviation to adjust the work piece motion adaptively to the seam position sensed by vision sensor. A novel molten pool area image-processing algorithm based on machine vision was proposed. The algorithm processes each image at the speed of 20 frames/second in real-time to extract three feature variables to get the seam deviation. It is proved experimentally that the algorithm is very fast and effective. Issues related to the algorithm are also discussed.

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。

基于改进T-S模糊模型的弧焊机器人轨迹实时跟踪

针对弧焊机器人在实际作业中受到诸多不确定因素的影响,导致弧焊机器人焊接轨迹偏离预期轨迹的问题,提出一种基于改进T-S(Takagi-Sugeno)模糊模型的弧焊机器人轨迹实时跟踪方法。采用图像识别技术识别并定位焊缝,通过坐标转换,确定焊缝中心线在机器人坐标系中的位置坐标。利用蚁群算法根据焊缝中心线在机器人坐标系中的坐标,搜索从起始位置到目标位置的最优焊接路径。设计了一种改进区间二型T-S模糊模型,通过视觉传感器实时获取新的焊缝信息,计算轨迹误差,以此为输入,利用改进T-S模糊模型对机器人的运动轨迹进行实时调整。实验结果表明,采用改进T-S模糊模型后,机器人轨迹跟踪离散度均降至1 mm以下,弧焊机器人轨迹与预期轨迹高度重合,比传统方法跟踪离散度大大降低。

机器人焊接技术在管道焊接中的应用现状

为介绍管道焊接中机器人焊接技术的应用情况,按照技术出现的顺序对油气管道焊接技术进行概括,分析了不同自动化焊接设备的工作原理,同时对工程中常用的设备进行了举例。文中介绍了管道铺设工程中相关的管道焊接检测技术以及管道接头腐蚀防护技术,最后介绍了焊接机器人的发展趋势,包括基于运动仿真的越障焊接、扩大应用场景与功能、引入新的传感器与原理。

船舱格子型角焊缝移动焊接机器人系统设计

针对大型船舱格子型角焊缝空间狭小,大型自动化设备与工人难以高质量完成焊接作业的现状,设计了一种可进行连贯直角角焊缝焊接的移动焊接机器人系统。该机器人由机械结构与电路控制系统组成,其中机械结构包括双轴后驱车体、可伸缩接触式传感器、机器人升降旋转复合机构、焊枪位姿调整机构;电控系统硬件包括PLC控制器、行程限位开关、步进电机与配套驱动器、直流减速电机和配套的运动状态调控板。另外,还建立了机器人在直角段角焊缝焊接过程的运动学模型,获取了该段焊缝在均分为10段后每段焊枪的伸缩速度与机器人旋转角速度的数值,为直角型角焊缝焊接的跟踪控制过程提供了PLC编程的数据基础;并基于可伸缩接触式传感器,实现了直线型角焊缝的路径跟踪。实验证明,该机器人能实现小空间直角型角焊缝与直线型角焊缝的连贯焊接,焊缝成型质量好,无焊穿、气孔、焊瘤、咬边、裂纹等不良状况。

基于视觉特征的机车钢结构焊接机器人焊缝跟踪方法研究

机车钢结构在智能焊接过程中,受到热输入、材料变形等因素的影响,焊缝的实际位置和形状会发生变化,在焊缝跟踪时,当前视觉帧间差方法容易忽略位置信息和焊缝轨迹位置偏差,导致跟踪误差大。对此,提出基于视觉特征的机车钢结构焊接机器人焊缝跟踪方法。选取以“棋盘格&条纹”混合图像作为基础,标定机车钢结构焊接机器人立体视觉位置,对焊接机器人激光条纹的中心线特征进行捕获;通过Roberts算子提取焊缝边缘信息,引入Hough变换确定焊缝中心线,实现焊缝识别,并将焊缝坐标展开转换,将其映射至机车钢结构焊接机器人的基础坐标系,在三维空间中构建焊缝轨迹;基于焊枪当前的位置信息和焊缝轨迹计算位置偏差,通过三次分均匀有理B样条对焊缝轨迹数据进行实时修正,实现机车钢结构焊接机器人焊缝实时跟踪。试验结果表明,所提方法可以有效提升机车钢结构焊接机器人焊缝跟踪精度。

工业机器人寻位及电弧追踪对自动化MAG焊质量的影响

在实际生产应用中,90%的自动化MAG焊缺陷是由寻位及电弧追踪异常导致。通过详述自动化MAG焊缺陷与寻位及电弧追踪的机理关系,解析寻位及电弧追踪的关键影响因子,旨在为自动化焊接质量控制提供理论及案例支撑。

焊接机器人在钢结构制造中的研究与应用

传统人工焊接存在自动化程度低、效率低下、质量参差不一等问题,而焊接机器人能大幅提高焊接效率,节省人力及改善焊接质量,保障同类焊缝的一致性,可被大规模应用于钢结构生产制造,因此,制造行业正逐渐向无人化、智能化方向转型。以油类垃圾箱为研究对象,采用Solidworks构建三维模型并进行静力学校核计算,利用Robot Stutio建立焊接工作站模拟仿真,对焊接轨迹进行离线编程,采用龙门式焊接机器人进行现场示教,调试激光纠偏系统,完成对油类垃圾箱的单片焊接,为焊接机器人在钢结构行业提供了应用依据。最后探讨了先进焊缝追踪技术和智能化焊接机器人,明确了焊接机器人未来应用的方向和趋势。

An adaptive thresholding for weld seam image recognition in real time

Image prooessing of wehl seam in real time is an importunity to make welding rohot be able to track weld seam. The algorithm described in this paper combines some image technologies, such as modified Sobel edge detector and Hough transformation function, and especially the thresholds for image processing are ore aled adaptively by Ineans of a neural network. aests proved that this algorithm has a high reliability and rapidity in distinguishing the position of weld seam even with noises. The algorithm can be used ac the basic program .for robot to track welding seam and furthermore for calculating 3 dimensional information plan robot movement automatically.

激光跟踪技术在工程机械焊接中的应用研究

针对现有推土机台车架机器人焊接质量及生产效率的问题,开展基于激光视觉传感的焊缝跟踪技术研究,并成功引入到工程机械焊接领域。通过三维仿真分析确定传感设备的选型,提出一种实用性强的焊缝实时跟踪方法,并通过十点标定法及多变量参数的调整等全工作流程的测试。通过前后定量数据对比分析,焊接机器人设备的有效稼动率OEE平均提升21.3%,生产节拍提升8.07%,焊接质量及焊达率均有明显优化。结果完全适用于工程机械大型复杂结构件上的焊接要求。

基于激光视觉焊缝跟踪技术的机器人焊接智能产线设计

由于人工焊接工作环境恶劣、效率低,且在焊接过程中容易发生焊接热变形所带来的焊缝位置不可控问题,焊接的加工质量无法得到保证。随着人工智能技术的发展,焊接智能化生产成为了可能。以半挂车车架机器人焊接智能生产线设计为主要研究方向,详细介绍机器人焊缝跟踪技术,焊缝识别与跟踪技术作为实现焊接自动化的关键,在焊缝的视觉识别过程中,需要借助视觉传感器对焊缝的图像进行采集、分析与处理,完成焊缝信息的提取,根据提取的焊缝信息,引导机器人完成自动焊接任务。