基于机器视觉的机械手装配系统设计

为了解决工业生产过程中料板的自动装配问题,提出了一种基于机器视觉的机械手定位装配检测系统,建立了实验平台。系统通过工业相机对料盘进行图像采集,将所采集的信息传送给工控机,借助图像处理程序,采用图像预处理实现了对图像的降噪,结合BLOB分析对图像特征进行了提取,获得了料盘上装配孔的位置信息,通过控制机械手动作,移动到装配位置,旋转相应角度,实现料板的自动定位装配。实验结果表明,该系统装配准确度高,误差小,满足了工业上的要求。     

Process knowledge based multi-class support vector classification (PK-MSVM) approach for surface defects in hot rolling

Random surface defects occur during the hot bar rolling of steels and are identified either by manual or by automated inspection techniques. Manual inspection techniques are purely based on the process knowledge of the inspector such as the location, type and kind of defects, and the primary sources of these defects. The automated techniques, to identify and classify the defects, rely on machine vision technologies and image processing algorithms based on support vector machines, wavelets, image processing and statistical inference. Both these approaches have their own advantages and limitations. To improve the accuracy of classification of these defects a process knowledge based support vector classification scheme is proposed (called PK-MSVM) which combines feature extraction task of automated inspection with the process knowledge. The defect observation data from the imaging sensor is transformed to include this process knowledge. Three attributes of the defects – length to width ratio, longitudinal location and transverse location- are used for this transformation are they are closely related to the thermo-mechanics of the rolling process. Different formulations of the multi-class support vector machines (MSVMs) are compared for this classification with or without process knowledge based transformation: one-against-one, one-against-all and Hastie’s algorithm of multi class SVM. It is found that the new approach (PK-MSVM) performs better than traditional MSVM for all the three formulations. For the best case, the performance sees a jump of more than 100%. Thus incorporating process knowledge in identification and classification does increase the reliability of inspection considerably.     

基于ROS的机器视觉系统实现

Machine vision is used in industrial robot applications for industrial inspection, flaw detection, precision measurement and control, automatic production lines, medicine and various hazardous applications. Based on the open source robot operating sys- tem ROS, this paper uses Kinect binocular vision sensor to obtain 3D stereo vision data, and combines with OpenCV, OpenNI and other open source machine vision libraries to design a low-cost machine vision system using the auxiliary machine vision tools pro- vided by ROS. The experimental test is carried on the prototype.     

Quantitative automated inspection of standard parts using machine vision

On line automated visual inspection for quality and process control is becoming a very important requirement in an automated manufacturing environment. This paper examines the possibility of real-time inspection of standard part using machine vision.     

Industrial application of machine-in-the-loop-learning for a medical robot vision system - Concept and comprehensive field study

The availability of increasingly powerful sensors and processing hardware provides the means for performing more and more complex signal processing and recognition tasks. In particular, vision systems have been the subject of intensive research and successful application during the last two decades, whereby they have often been augmented by auxiliary sensors. However, several fundamental challenges concerning the design and application of such systems remain. From the plethora of off-the-shelf-hardware, sensors and algorithms which is available a selection must be made which results in a feasible and well performing system which at the same time is capable of meeting constraints such as cost and power consumption, for instance. Currently, the design process is undergoing a paradigm shift from expert driven to automated design. Learning and optimisation techniques have proved to be more than able to compete with expert based solutions in many cases. As soon as a developed solution is deployed to more than one installation site and operated for an extended period of time under significantly varying environmental conditions, the initial solution will no longer be applicable and adjustment or fine-tuning will be required. This paper presents a conceptual solution to this challenge for the application of a medical robot vision system for the task of tube type detection. Laboratories employ this robot system for semi-automated or automated analysis of medical samples at numerous sites. The varying environmental conditions and variations in the classification task from situation to situation require reconfiguration capabilities. In our approach, these capabilities are realised in the form of a holistic training concept, designated as machine-in-the-loop-learning (MILL). The current state of concept implementation in the robot vision system with MILL support has allowed for its successful implementation in more than 250 machines at 150 sites worldwide with more than 5000 classifications per day and machine. An error rate of 0.06% was reported in a long-term study of three months duration involving two representative installations. Future work will focus on the refinement of the system architecture and incorporation of recent advances in optimisation, e.g., particle swarm optimisation (PSO) and related methods.     

The control of a quick turnaround cell — An integrated CAD and CAM system

Quick turnaround Cell (QTC) is an integrated software/hardware system for rapid product prototyping. A QTC consists of some engineering workstations, one of a few machine tools, and a vision inspection system. The major objective of the project is to study the integration of CAD, process planning, cell control, and vision inspection. Currently a prototype QTC has been implemented at Purdue Uniersity. It is capable of producing prismatic parts. After a part is designed, it can be machined and inspected automatically. There is no human decision necessary for the planning, part programming, and cell control. The cell controller plays an extremely important role on coordinating all the system functions and providing user and machine interfaces. In this paper we will address the issues encountered in designing the cell control. The structure and the functionality of the specific design will be discussed.     

Automated visual inspection: A tutorial

This article introduces the topic of automated visual inspection (AVI) as a subset of machine vision. The components of a generic AVI system will be presented and current applications of AVI systems will be discussed. Specifically, the applications of automated dimension verification, bare printed circuit board inspection, stuffed printed circuit board inspection and component identification using color technology will be covered in some detail.     

Object oriented concepts and mechanisms for feature-based computer integrated inspection

Computer vision can provide fast, accurate and reliable inspection. In this paper is described a knowledge-based vision inspection planning system for manufactured components based on their CAD design models. An object oriented methodology is developed to capture the knowledge such as the geometric entities, their relationships, sensors and plans. Reasoning mechanisms are developed to automatically determine various attributes of the different features of an object to be inspected, and to generate the alternative strategies for inspection of each attribute. Mechanisms for automated generation of sensor settings and optimization of the inspection plan are also developed. Sensor constraints are considered in determining the camera parameters. This inspection methodology is flexible, and it is easily integrated with the design and process planning aspects of an integrated manufacturing system.     

Integration of vision and walking skills together with high-level strategies for quadruped robots to play soccer

Legged robots taking part in real multi-agent activities represent a very innovative challenge. This domain of research requires developments in three main areas. First, without any feedback information from the environment, there is no way for robots to achieve some tasks autonomously. Fortunately, the quadruped 'Sony' prototypes on which all experiments are carried out are equipped with an enhanced vision system; thanks to its CCD camera located in its head, the robot can obtain color images of the scene around it. Extracting relevant information from the images captured is not easy since it must be done onboard in real time. Moreover, image treatment procedures should have high process rates for the robot to react quickly in front of unexpected events. A special vision module composed of three parts has been designed for these purposes. The second point to focus on is the walking ability of the robot. Quadrupeds are designed to move efficiently and rapidly on flat ground. The objective of the walking module is to generate appropriate walking patterns allowing the machine to walk in the desired direction. Walking gaits are produced like reflexes by the robot itself to adapt to the situation. With regard to the design of these gaits, emphasis has been put on increasing speed and mastering transitions. Finally, the machine should be given a minimum of intelligence since it has to manage vision information and its walking gaits by itself. When involved in situations of cooperation or competition or both, like in a soccer game, a high-level supervision task is welcome. This paper presents detailed developments of these three points and describes how they are implemented on a real robot.     

自适应环境的机器人视觉伺服控制方法

机器人和机器视觉的迅速发展,使得基于视觉的智能机器人得到更加广泛的应用。机器视觉提高了机器人控制系统对环境的适应程度,但其适应程度也受到周围环境对机器视觉算法的影响。针对这一情况,本文对机器人视觉伺服控制系统中的机器视觉算法进行了改进,提出了一种基于帧间差分法的自适应环境的机器人视觉伺服控制方法,从而提高了控制系统对环境的自适应程度,提高了对机器人控制的精确度,实验和理论分析证明,该方法具有较大的应用前景和实用价值。     

一种输电线路巡检机器人越障规划方法

针对输电线路巡检机器人的局部自主与远程控制方式,提出了一种基于有限状态机的越障规划方法.将巡检机器人的越障运动过程分解成若干离散化的关键状态,建立了运动规划的有限状态机模型.采用一种基于模糊方法的产生式系统用于推理越障模式,并产生运动序列.仿真与现场实验验证了越障规划方法的正确性和有效性,该方法可应用于输电线路巡检机器人运动控制.     

Design and application of industrial machine vision systems

In this paper, the role and importance of the machine vision systems in the industrial applications are described. First understanding of the vision in terms of a universal concept is explained. System design methodology is discussed and a generic machine vision model is reported. Such a machine includes systems and sub-systems, which of course depend on the type of applications and required tasks. In general, expected functions from a vision machine are the exploitation and imposition of the environmental constraint of a scene, the capturing of the images, analysis of those captured images, recognition of certain objects and features within each image, and the initiation of subsequent actions in order to accept or reject the corresponding objects. After a vision system performs all these stages, the task in hand is almost completed. Here, the sequence and proper functioning of each system and sub-systems in terms of high-quality images is explained. In operation, there is a scene with some constraint, first step for the machine is the image acquisition, pre-processing of image, segmentation, feature extraction, classification, inspection, and finally actuation, which is an interaction with the scene under study. At the end of this report, industrial image vision applications are explained in detail. Such applications include the area of automated visual inspection (AVI), process control, parts identification, and important role in the robotic guidance and control. Vision developments in manufacturing that can result in improvements in the reliability, in the product quality, and enabling technology for a new production process are presented. The key points in design and applications of a machine vision system are also presented. Such considerations can be generally classified into the six different categories such as the scene constraints, image acquisition, image pre-processing, image processing, machine vision justification, and finally the systematic considerations. Each aspect of such processes is described here and the proper condition for an optimal design is reported.     

基于反向P-M扩散的钢轨表面缺陷视觉检测

研制了一种基于反向P-M（Perona-Malik）扩散的钢轨表面缺陷视觉检测装置,该装置可 自动获取钢轨表面图像,并实现实时检测与定位钢轨表面缺陷. 钢轨图像具有光 照变化、反射不均、特征少等特点,为了在运动过程中 从复杂的钢轨表面图像提取缺陷,首先将图像进行反向P-M扩散,然后将扩散后的图像与原图像进 行差分,从而减小了上述因素的影响,最后将差分图像进行二值化操作,根据 缺陷边缘特性和面积进行滤波,分割出缺陷图像. 实验仿真和现场测试结果表明,该方法能很好地识别块状缺陷和线状缺陷,并且检测速度、精度、识别 率和误检率都能很好地满足要求.     

基于结构约束的架空输电线路巡线机器人障碍识别

巡线机器人沿相线行走时必须探测识别各种障碍，并根据障碍类型规划越障行为．针对220 kV架空输电线路的结构特点，利用视觉传感器，设计了基于结构约束的障碍识别算法．算法利用图像的边缘信息，采用改进的基于存在概率图的圆/椭圆检测方法和分层决策机制，以减少自然环境中光线变化和机器人运动对识别质量的影响，满足了巡线机器人的实时越障要求．实验室模拟线路和实际线路实验结果表明，算法能可靠地识别出复杂背景中的防震锤、悬垂线夹和耐张线夹等障碍物．     

机器视觉在印刷缺陷在线检测中的应用与研究

为了提高印刷缺陷检测准确性与效率,满足缺陷检测的实时性,提出了机器视觉技术应用于印刷缺陷在线检测并研究检测过程中的相关问题.设计印刷缺陷在线检测的方案及以数字信号处理器DSP为核心的视觉在线检测系统的硬件结构组成.研究图像预处理、图像匹配与缺陷检测的算法并加以改进使其满足缺陷检测的实时性.开发印刷品缺陷视觉检测软件,对缺陷检测流程进行仿真,验证检测方案与图像处理算法的可行性.     

基于视觉导航的轮式移动机器人横向最优控制

近年来，基于机器视觉导航的轮式移动机器人在农业领域中被广泛研究．本文运用 最优控制算法对其进行横向控制，但是，农田非结构化自然环境的机器视觉识别耗时导致反 馈通道传输延迟明显，最优控制器的性能因之而下降．因此，本文又根据轮式移动机器人运 动学模型预测其行为，修正了滞后的反馈信息．仿真结果表明，该算法能实现轮式移动机器 人精确可靠的横向控制，具有较强的纵向速度自适应能力．     

基于机器视觉的喷涂机器人轨迹规划与涂装质量检测研究综述

随着智能喷涂技术的快速发展,机器视觉在喷涂机器人系统中的研究和应用引起广泛关注,合理的喷涂轨迹能保障油漆厚度均匀、减少漆膜缺陷产生,并且融合涂装质量检测技术形成闭环的喷涂系统.鉴于此,针对机器视觉在喷涂机器人轨迹规划与涂装质量检测中的研究进行综述.首先,对喷涂系统在现代产品制造中的快速发展所面临的机遇、挑战和机器视觉技术进行介绍;然后,综述基于机器视觉技术的喷涂机器人轨迹规划和涂装质量检测的研究成果,对基于机器视觉的喷涂机器人轨迹规划方法,包括待喷涂工件的三维重建、基于点云数据的喷涂轨迹自动规划和基于视觉伺服的喷涂轨迹补偿进行分析和讨论,并重点介绍机器视觉在涂装质量检测中的应用与研究现状,从数据增强和模型选择两个方面,对不同任务中基于深度学习的涂装质量检测算法性能的改善提供潜在解决方案;最后,总结与展望机器视觉技术在喷涂机器人轨迹规划与涂装质量检测中的研究方法和思路,为喷涂系统朝着智能化、柔性化的方向发展提供参考.     

基于机器视觉的锯条自动化装盒系统设计

以VS2010为软件平台,以Staubli TP80机器人,康耐视摄像机和传送带为硬件基础,对锯条进行识别,定位和动态抓取,搭建了一个基于机器视觉的工业机器人智能抓取系统,其中Staubli-TP80机器人为操作手臂,利用康耐视摄像机和工控机搭建视觉检测平台,通过抓取系统的参数化建模实现图像坐标到机器人坐标的转换,利用OpenCV函数库实现锯条的图像处理和模式匹配,最后控制机器人对动态目标进行抓取。     

Automatic recognition vision system guided for apple harvesting robot

In apple harvesting robot, the first key part is the machine vision system, which is used to recognize and locate the apples. In this paper, the procedure on how to develop an automatic recognition vision system guided for apple harvesting robot, is proposed. We first use a color charge coupled device camera to capture apple images, and then utilize an industrial computer to process images for recognising fruit. Meanwhile, the vector median filter is applied to remove the color images noise of apple, and images segmentation method based on region growing and color feature is investigated. After that the color feature and shape feature of image are extract, a new classification algorithm based on support vector machine for apple recognition is introduced to improve recognition accuracy and efficiency. Finally, these procedures proposed have been tested on apple harvesting robot under natural conditions in September 2009, and showed a recognition success rate of approximately 89% and average recognition time of 352 ms.