基于ROS的智能工业机器人系统开发平台

目前,工业机器人对于机器视觉、自主路径规划等智能化功能需求日益增长.然而在传统工业机器人系统中添加智能化功能模块时需要修改大量的源码,浪费了人力和成本.本文提出的基于ROS的易扩展机器人系统开发平台,能为开发者开发智能工业机器人系统提供了方便.本平台分为服务器端和机器人端.将机器人端作为一级节点,与安装ROS的PC服务器端进行通信.机器人一级节点由二级功能节点与功能模块组成.根据此平台开发实现的JPB06六自由度工业机器人系统具有机器视觉、自主定位、语音控制等智能化功能,可以满足工业机器人对于智能化和实时控制的需求.     

排爆机器人双目立体视觉系统的研究和开发

为了辅助公安人员更好地完成排爆工作，设计和开发了一个带双目立体视觉系统的排爆机器人。该视觉系统使用Matlab7.0和机器视觉软件EVision的EasyMatch库进行开发，能实时捕获排爆机器人周围的图像信息、进行摄像机标定、图像预处理、立体图像匹配、求取可疑目标物的特征点的立体坐标，并把图像实时显示在控制台，控制排爆机器人准确地抓取可疑目标物。该机器人视觉系统成功地抓取实验，表明了它在精度上能够满足排爆机器人的项目要求。     

863计划对中国机器人发展的巨大作用和深远影响

一般认为 ,现代工业机器人的概念起源于５０年代初期。１９５４年 ,Ｇ．Ｃ．Ｄｅｖｏｌ申请了一种机器的美国专利 ,这种机器基于示教再现的控制方式与数控实现技术。工业机器人的另一个技术源头来自遥操作 ,即所谓主从操作手。国际上第一台主从操作手开发于１９４８年。智能机器人则更进一步涉及到人工智能、计算机视觉与现代控制理论等。１９６５年 ,美国ＭＩＴ的Ｒｏｂｅｒｔｓ演示了第一个具有视觉传感器的、能识别与定位简单积木的机器人系统。我国机器人的研究和开发可追溯到六十年代。但直到七十年代中期 ,全国大约２４０多家单位中的…     

视觉图像技术与机器人工件抓取的协作应用

工业机器人在抓取环节如果配置机器视觉装置,应用图像处理技术,在抓取效率和准确度方面就有优势。该文介绍机器人功能实训台的工件抓取视觉系统的组成,侧重工件轮廓的图像处理技术,以图像处理作为机器人视觉系统处理的核心目标,将视觉图像技术与机器人工件抓取技术协作应用于工业机器人实训平台的抓取环节,利用工业机器人运动过程中坐标的转换,通过相机的正确标定与抓取动作的共同控制,实现工件的精确定位,达到可靠抓取的目的。总结图像处理技术在工件抓取中的协作应用效果,期待便捷的视觉图像技术与机器人工件抓取的协作应用。     

基于机器视觉的电流互感器定位系统

为解决电流互感器自动化检测线抓取模块精确定位互感器的问题,提出基于机器视觉的互感器位置测量方案.该系统的软件采用visual studio 2008平台加上OPENCV库开发,应用单视测量技术对CMOS工业摄像机拍摄下来的电流互感器的照片进行图像处理与分析,得到互感器的空间位置信息并传递给机器人控制器,从而实现对机器人的控制.实验结果表明,在电流互感器的各向位移不超过4 cm、旋转角度不超过10°的条件下,系统误差精度能够满足电流互感器定位的要求,机器人可以顺利抓取空间位置在此范围内变化的电流互感器.该系统简单可行,检测效率高,性能可靠,且通过简单修改程序就可应用于不同的基于机器视觉的位置测量领域.     

卷烟条盒包装质量检测机器视觉系统设计

为了避免人眼对产品外观质量检测时,人眼视觉疲劳所产生的产品错检、漏检,提出了基于机器视觉的烟支条盒包装质量检测系统的设计。该系统由光源、相机、图像采集卡和工业控制计算机组成视觉系统处理单元,应用定位配准、边缘检测和斑点分析等图像处理算法完成对产品缺陷的检测。该视觉检测系统已经在烟厂得到了实际应用,对今后开发类似的基于机器视觉的产品外观质量检测系统具有一定的参考价值和指导意义。     

基于YOLOv3算法的工业分拣目标检测

在工业机器人视觉导引系统中，对于不规则的工业零部件，基于模板匹配的目标检测算法难以取得较好的定位效果和识别精度。在结构化环境下，对于传统视觉目标检测算法，利用YOLOv3算法开展视觉目标检测的实践。实验结果表明，该算法的定位精度和识别精度均取得了较高的水平，可以在工业现场进行部署。     

基于EVision的双目视觉系统的设计与实现

设计和开发了一个排爆机器人的双目立体视觉系统。该系统使用Matlab7.0和机器视觉软件EVision6.2的EasyMultiCam进行图像捕获并和EasyMath模式匹配库进行图像特征匹配,能实时捕获排爆机器人周围的图像信息、进行摄像机标定、图像预处理、立体图像匹配、求取可疑目标物的特征点的立体坐标，并把图中实时显示在控制台上，控制排爆机器人准确地抓取可疑目标物。该机器人视觉系统成功的抓取实验，表明了它在精度上能够满足排爆机器人的项目要求。     

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.     

基于双目视觉动态跟踪的机器人标定

采用双目视觉动态跟踪技术对自主研发的工业机器人进行运动学标定。区别于以往机器人运动学标定中复杂模型计算,在此利用双目视觉动态跟踪系统的静态测量和动态跟踪等优势特性来跟踪测量机器人的连杆参数误差,结合机器人控制系统开放性特点,运用提出的动态标定原理对机器人实施连杆参数测量、辨识、修正及补偿。实验表明,通过参数反馈补偿,自主研发的机器人的定位误差明显降低,且该方法易于实现,为机器人精度研究提供了可靠依据。     

Measurement error analysis and accuracy enhancement of 2D vision system for robotic drilling

Robotic drilling for aircraft structures demands higher accuracy on industrial robots than their traditional applications. Positioning error measurement and compensation based on 2D vision system is a cost-effective way to improve the positioning accuracy in robotic drilling. In this paper, we first discuss the principle of error measurement and compensation with a 2D vision system for robotic drilling and the determination of tool center point of the vision system so that the Abbe errors are eliminated in the measurement process. Measurement errors due to nonideal measurement conditions, i.e. nonperpendicularity of the camera optical axis to the workpiece surface and incorrect object distance, are mathematically modeled and experimentally verified. A method utilizing four laser displacement sensors is proposed to ensure perpendicularity of the camera optical axis to the workpiece surface and correct object distance in the measurement process, and hence to achieve high accuracy in 2D vision-based measurement. Experiments performed on a robotic drilling system show that the 2D vision system can achieve an accuracy of approximately 0.1 mm with the proposed method.     

工业机器人加工轨迹双目3D激光扫描成像修正方法

为了提高工业机器人作业柔性,本文提出了一种基于双目CCD激光扫描3D成像的“眼在外”(Eye-to-hand:ETH)工业机器人末端(tool center point, TCP)运动轨迹在线修正方法。以激光切割机器人视觉引导为研究背景,降低加工过程机器人对物理工装定位精度的依赖。首先,为提高机器人视觉控制精度,研究了目标工件双目3D激光扫描成像空间点云坐标精确提取方法;其次,融合ETH控制特点和扫描成像系统结构,建立了一种机器人TCP运动轨迹相对偏差在线补偿方法,并通过实验验证了所提方法的可行性,为工业应用奠定基础。     

基于改进ORB特征的遥操作工程机器人双目视觉定位

为了提高遥操作工程机器人定位系统图像处理的实时性,提出一种基于改进ORB特征的遥操作工程机器人双目视觉定位方法。利用图像二分法剔除掉图像中的无效区域,只保留匹配区域,采用ORB算法完成有效区域的匹配,最后采用RANSAC算法剔除伪匹配,得到较为精准的匹配点对。与传统算法对比实验结果表明,该方法可以提高匹配效率,消除误匹配,提高匹配精度,定位效率高,能满足遥操作工程机器人作业的实时性要求,可为遥操作工程机器人及其他领域遥操作机器人的视觉提示系统设计提供技术参考。     

工业机器人定位误差规律分析及基于ELM算法的精度补偿研究

针对工业机器人应用于飞机零部件自动化钻孔时绝对定位精度较差的问题,提出利用极限学习机（ELM）算法建立机器人法兰中心点理论位置与实际位置之间的误差模型,并优化补偿机器人定位精度的方法．首先基于空间网格采样方法,获得了机器人绝对定位误差沿机器人基坐标系不同方向的误差变化规律,分析了建模补偿的可行性;其次建立基于ELM算法的误差补偿模型,并针对误差模型训练中隐含层神经元个数取值问题进行了分析优化．实验结果表明,机器人绝对定位误差值沿其坐标系不同方向存在不同的变化规律,补偿前绝对定位误差分布范围为0.29 mm~0.58 mm,平均误差为0.41 mm;补偿后定位误差分布范围降低到0.04 mm~0.32 mm,平均误差为0.18 mm;采用ELM算法建模的补偿速度快,泛化性能好．     

Neuromorphic vision based control for the precise positioning of robotic drilling systems

The manufacturing industry is currently witnessing a paradigm shift with the unprecedented adoption of industrial robots, and machine vision is a key perception technology that enables these robots to perform precise operations in unstructured environments. However, the sensitivity of conventional vision sensors to lighting conditions and high-speed motion sets a limitation on the reliability and work-rate of production lines. Neuromorphic vision is a recent technology with the potential to address the challenges of conventional vision with its high temporal resolution, low latency, and wide dynamic range. In this paper and for the first time, we propose a novel neuromorphic vision based controller for robotic machining applications to enable faster and more reliable operation, and present a complete robotic system capable of performing drilling tasks with sub-millimeter accuracy. Our proposed system localizes the target workpiece in 3D using two perception stages that we developed specifically for the asynchronous output of neuromorphic cameras. The first stage performs multi-view reconstruction for an initial estimate of the workpiece’s pose, and the second stage refines this estimate for a local region of the workpiece using circular hole detection. The robot then precisely positions the drilling end-effector and drills the target holes on the workpiece using a combined position-based and image-based visual servoing approach. The proposed solution is validated experimentally for drilling nutplate holes on workpieces placed arbitrarily in an unstructured environment with uncontrolled lighting. Experimental results prove the effectiveness of our solution with maximum positional errors of less than 0.2 mm, and demonstrate that the use of neuromorphic vision overcomes the lighting and speed limitations of conventional cameras. The findings of this paper identify neuromorphic vision as a promising technology that can expedite and robustify robotic manufacturing processes in line with the requirements of the fourth industrial revolution.     

Integrated remote control of the process capability and the accuracy of vision calibration

This study aims at jointly controlling two critical process parameters from a remote site, of which include the process capability of robotic assembly operations and the accuracy of vision calibration. The process capability is regarded as the indication of robot positioning accuracy. When the robot is driven by the vision camera, the process capability becomes mainly dependent on the calibration accuracy of vision-guided robot system. Even though newly commissioned, high precision assembly robots typically display excellent positioning accuracies under normal working conditions, the imperfect mathematical conversion of vision coordinates into robot coordinates imparts the accuracy problems. In this study, a novel vision calibration method is proposed that effectively rectifies the inherent complications associated with lens distortions. Our analysis shows that the degree of lens distortion appears very differently along the vision field of view. Because of this non-uniform distortion, a single mathematical equation for vision calibration is deemed ineffective. The proposed methodology significantly improves the positioning accuracy, which can be performed over the network from a remote site. This is better suited for today?s global manufacturing companies, where fast product cycles and geographically distributed production lines dictate more efficient and effective quality control strategies.     

基于视觉检测技术的轮毂位置精确测定

针对工业技术智能化的发展趋势,及电动车轻质合金轮毂机械加工工艺,对装卡位置精度要求高,文章研究一种有效的轮毂装卡位置精准测定方法,以解决当前检测方法,易受人为因素影响、效率低、稳定性差等问题;提出一种基于机器视觉技术的非接触式轮毂装卡位置精确测定方法,应用CCD工业相机搭建视觉检测系统,运用双特征位置精确测定方法,提取相机摄入图像中轮毂气门孔和中心孔的两处主要特征,通过对双特征数据的分析与计算确定轮毂位置精度误差,并根据计算数据输出装卡位置误差,应用分段位移、逐渐次定位的方式进行反馈补偿;通过实验测量表明,在系统的测量参量值中,角度标准差为0.0226°,测量不确定度为0.0036°,测量精度理想,平均检测时间为0.29 s,检测时效性良好,能够满足轮毂自动化加工位置检测的性能要求。     

Accurate positioning of a drilling and riveting cell for aircraft assembly

Modern aircraft assembly demands assembly cells or machines with higher machining efficiency and accuracy. Thus, a dual-machine drilling and riveting cell is developed in this paper. We firstly discuss its physical design, as well as the automatic drilling and riveting process. With the automatic drilling and riveting cell, drilling and riveting production line of aircraft panels can be expected. The frame chain of the drilling and riveting cell is constructed to link the assembly cell to its task space, which is the kinematics base. System calibrations, including task space calibration, the sensor calibration of an orientation alignment unit, the floating calibration of the implicit hand-eye relationship, are explored. For high positioning accuracy, a multi-sensor servoing method is proposed for cell positioning. An orientation-based laser servoing strategy, which uses the feedback of the orientation errors measured by laser displacement sensors, is used to align drilling direction and camera shooting direction. Besides, A single-camera-based visual servoing is applied to align the tool center point (TCP) to reference holes, to obtain their coordinates for drilling position modification. Experiments of multi-sensor servoing for cell positioning are performed on an automatic drilling and riveting machine developed for the panel assembly of an aircraft in China. With the cell positioning method, the automatic drilling and riveting cell can approximately achieve an accuracy of 0.05 mm, which can adequately fulfill the requirement for the assembly of the aircraft.