Acquisition of Weld Seam Dimensional Position Information for Arc Welding Robot Based on Vision Computing

Recognition and identification of weld environment and seam dimensional position by computer vision is a key technology for developing advanced autonomous welding robot. Aiming at requirements for recognition of weld seam image characteristics, this paper first presents an improved algorithm of subpixel edge detection based on Zernike moments. Comparing with the Ghosal’s original algorithm, the improved algorithm deals with mask effect and first derivative model on edge gradient direction so that it has the strong robust to noise, self-thinning ability and higher locating precision. An algorithm based on ZMs to extract line is also proposed, the comparative results with SHT and RHT show the method has the highest calculation speed and accuracy. The stereovision technology is developed to identify dimensional position of weld seam by computing dimensional coordinates of the weld seam. According to characteristics of weld seam, view field scope model and stereovision model based on baseline are studied and a stereo matching method is presented. In order to evaluate the algorithms and models presented in this paper, a welding robot systems with single camera fixed on the weld torch end-effector has been established for the robot to identify the dimensional position of typical weld seam by one-item and two-position method. The experiment results on S-shape and saddle-shape weld seams show that the vision computing method developed in this paper can be used for acquiring weld seam dimensional position information in welding robot system. Thus the welding path is mapped before the welding operation is executed.     

超大范围坐标测量系统的现场测试基准及其误差分析

CCD立体视觉坐标测量系统已有广泛应用,但其有效测量范围通常在十数米之内.超大范围坐标测量系统是一种适于野外环境的坐标测量系统,其测量范围可覆盖直径达数百米的圆形区域.文章从分析测量系统数学模型入手,指出了建立现场测试基准的必要性,随后给出一种既能保障测量结果正确可靠又可简化测量模型的基准建立方法,并分析了基准建立过程中各引入误差对系统误差的影响.实验数据证明了文中测量原理及测试基准建立方法的可行性.     

智能车双目视觉辅助GNSS定位方法研究

具有精确、稳定的定位结果以及合理的价格是未来的智能车辆导航系统的发展趋势。为了达到这个目标,人们建立了多种组合导航模型（GNSS/DR,GNSS/INS,GNSS/MM）。尽管这些模型已在多种不同环境中成功应用,但它们仍有许多缺陷,尤其是在全球卫星导航系统（GNSS）定位精度受到威胁的区域。研究了一种通过双目视觉利用路标的地理位置信息对GNSS定位精度进行局部改良的方法。随机霍夫变换用于路标检测,SIFT算法与K均值算法将用于路标的匹配识别。双目视差计算智能车与路标之间的向量,从而建立辅助定位模型计算车辆的位置。利用实验车在一处复杂环境区域进行实时数据采集,通过计算出的双目视觉定位误差与GNSS定位误差对比分析,验证了该方法在路标可见范围内对GNSS定位结果有明显改善。     

无人系统多模态视觉感知单元测试与评估方法的研究

提出一种通用的基于视觉传感器的无人系统多模态视觉感知平台的测试与评估的方法,研究了可见光波段和红外光波段下的动态测距实验与复杂光变测距实验. 整个过程采用基于北斗卫星实时授时的时间同步技术和真值采集设备,输出带有时间戳的无人系统多模态感知单元的测量数据和真值数据,并保证了不同设备之间的时间同步误差小于10 ms. 最后,对比测量数据和真值数据,给出测试设备的性能与功能的量化评估. 实验结果表明,该测试评估方法有效地获取了真值,并进行了时间同步,满足实际的需求,具备推广性.     

新型立体视觉平台跟踪系统的研制

研制了一种新型立体视觉平台跟踪系统。硬件平台设计采用了霍尔姆茨结构，这种平行的机械结构解决了电机互为负载的问题。根据视觉跟踪特性采用了基于位置控制的闭环控制结构。在跟踪算法方面，为提高系统实时性和抗干扰能力，提出了一种新的HSV颜色空间，同时对投影搜索算法进行了改进。实验结果表明，该跟踪系统具有很好的实时性。     

An iterative adaptive multi-modal stereo-vision method using mutual information

We propose a method for computing disparity maps from a multi-modal stereo-vision system composed of an infrared–visible camera pair. The method uses mutual information (MI) as the basic similarity measure where a segment-based adaptive windowing mechanism is proposed along with a novel MI computation surface with joint prior probabilities incorporated. The computed cost confidences are aggregated using a novel adaptive cost aggregation method, and the resultant minimum cost disparities in segments are plane-fitted in their respective segments which are iteratively refined by merging and splitting segments reducing dependency to initial segmentation. Finally, the estimated disparities are iteratively refined by repeating all the steps. On an artificially-modified version of the Middlebury dataset and a Kinect dataset that we created in this study, we show that (i) our proposal improves the quality of existing MI formulation, and (ii) our method can provide depth comparable to the quality of Kinect depth data.     

Model-Based Stereo-Tracking of Non-Polyhedral Objects for Automatic Disassembly Experiments

Automatic disassembly tasks in the engine compartment of a used car constitute a challenge for control of a disassembly robot by machine vision. Experience in exploratory experiments under such conditions forced us to abandon data-driven aggregation of edge elements into straight-line data segments in favor of a direct association of individual edge elements with model segments obtained from scene domain models of tools and workpieces. In addition, we had to switch from a conventional single camera hand-eye configuration to a movable stereo-configuration mounted on a separate observer robot. A generalisation of our model-based tracking includes the parameters, which characterize the relative pose of one camera with respect to the other one of the stereo-camera set-up, into the set of parameters to be re-estimated for each new stereo image pair. This results in a continuous re-calibration during a relative movement between stereo-camera set-up and tracked objects. Our approach had to be extended further in order to cope with non-polyhedral objects.The methodological improvements of machine vision in the course of this research are treated in detail. We discuss, moreover, the systematic trading-off of computational resources for increased robustness which is vital for visual control of automatic disassembly robots.     

Enhancing Design of Visual-Servo Delayed System

A robust adaptive predictor is proposed to solve the time-varying and delay control problem of an overhead crane system with a stereo-vision servo. The predictor is based on the use of a recurrent neural network (RNN) with tapped delays, and is used to supply the real-time signal of the swing angle. There are two types of discrete-time controllers under investigation, i.e., the proportional-integral-derivative (PID) controller and the sliding controller. Firstly, a design principle of the neural predictor is developed to guarantee the convergence of its swing angle estimation. Then, an improved version of the particle swarm optimization algorithm, the parallel particle swarm optimization (PPSO) method is used to optimize the control parameters of these two types of controllers. Finally, a homemade overhead crane system equipped with the Kinect sensor for the visual servo is used to verify the proposed scheme. Experimental results demonstrate the effectiveness of the approach, which also show the parameter convergence in the predictor.     

基于三坐标测量机的大尺寸非接触测量

以立体视觉测量原理为依据,提出了一种基于三坐标测量机的非接触测量方法.将三坐标测量机与视觉测量系统相结合,使测量系统在700mm×600mm×400mm的范围内测量精度达到20μm.该方法既具有三坐标测量机测量的高精度,又有立体视觉测量的非接触性.最后给出了对典型物体的测量结果.对测量结果的分析表明此方案具有简单可行、测量精度高的特点.