共查询到17条相似文献,搜索用时 968 毫秒
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由于加工装配误差等原因,飞机壁板工件的数学模型和实际模型往往不一致。为解决不一致导致的制孔位置精度差的问题,提出了一种基于双目测量系统的孔位补偿方案。为了能够更好地设计满足制孔需要的视觉测量系统,分析了机器人自动化制孔系统的工作流程。然后介绍了视觉测量系统组成和工作流程,最后分别对视觉测量系统的基准孔三维坐标提取、孔位误差补偿、数据库读写3个重要功能的技术进行了详细的介绍。通过该双目视觉测量系统的孔位补偿方法,可以获取基准孔的三维坐标,对孔位误差进行补偿,补偿信息写入数据库,提高机器人自动化制孔系统的制孔位置精度。 相似文献
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机器人自动制孔中绝对定位误差的分析与补偿 总被引:4,自引:4,他引:0
《航空学报》2015,(7)
由于机器人绝对定位精度相对较低,无法直接满足自动制孔的孔位精度要求。为了提高机器人自动制孔的孔位精度,对机器人绝对定位误差进行了研究。首先,阐述了绝对定位误差的来源和产生过程,并通过理论分析和相关试验,证明了绝对定位误差会对机器人基坐标系的平移分量和姿态变换分量产生不同程度的影响。然后,为了补偿由于基坐标系标定不准确所引起的坐标转换误差,从飞机曲面构造原理角度,提出了一种基于误差Coons曲面函数的补偿方法。制孔试验表明,采用基于误差Coons曲面函数的补偿方法,可以使得坐标转换误差得到有效的补偿。机器人自动制孔的孔位平均位置误差为0.205mm,最大位置误差为0.343mm,满足孔位精度在0.5mm以内的要求,实现了机器人自动化精确制孔。 相似文献
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基于Kriging模型插值的孔位修正策略 总被引:2,自引:0,他引:2
飞机装配时的孔位精度直接影响其最终装配质量,而待制孔零件易发生变形和整体偏移,因此一般通过基准孔信息对待制孔位进行补偿。提出一种基于Kriging插值的孔位修正方法,通过基准孔建立孔位理论坐标与实际偏差值的Kriging模型,进而预测待制孔的孔位偏差,并给出每个位置的预测标准误差,以指导基准孔的增添和布置。最后通过有限元和数值实验分别验证了零件在极限变形和整体平移旋转情况下该方法的有效性,实现了孔位估计误差小于0.3 mm。 相似文献
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针对航空制孔机器人绝对定位精度补偿中存在的建模复杂及运算量大的问题,提出了一种基于极限学习机的绝对定位精度补偿方法。该方法通过将机器人视为一个黑箱系统,忽略机器人的几何因素和非几何因素的影响,通过高精度的激光跟踪仪测量获得机器人的末端运动误差,采用极限学习机建立机器人误差预测模型。由机器人误差预测模型获得机器人在期望位置的位置偏差,通过修正机器人位置坐标来实现机器人的绝对定位精度补偿。最后该方法在航空制孔机器人上进行了试验,试验结果显示机器人的绝对位置误差的平均值和最大值分别降低了75.69%和78.16%。 相似文献
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对由AGV承载的工业机器人组成的AGV式移动制孔机器人的定位误差补偿方法进行了研究。在面向飞机装配的AGV式移动制孔机器人系统中,利用激光跟踪仪构建坐标系,提出了AGV式移动制孔机器人机座坐标系的换站方法,能更好地适应飞机制造多品种、小批量的特点。基于对AGV式移动制孔机器人定位误差源的分析,利用定位误差相似性,提出针对AGV式移动制孔机器人的基于反距离加权定位误差的空间插值与补偿方法,克服了现有技术对于AGV式移动制孔机器人定位误差补偿的局限性。以AGV搭载的KUKA KR480型工业机器人制孔系统作为试验对象,通过试验选取最优网格步长,补偿结果表明,能将系统综合定位误差平均值由补偿前的1.045 mm降低到0.227 mm,最大绝对定位误差由补偿前的2.727 mm降低到0.478 mm,降低了82.47%,该方法能有效提高AGV式移动制孔机器人的绝对定位精度。 相似文献
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风洞试验中模型迎角的精准测量是降低阻力系数误差的重要途径之一,为此,提出了基于单应性矩阵的模型迎角单目视频测量方法。该方法通过两个单应性矩阵,获取试验过程中相机实时位姿和标记点物方空间位置坐标,应用坐标旋转关系,完成试验模型的迎角测量。数值仿真试验结果表明:迎角测量误差与待测标记点到风洞壁板间的距离偏差近似为线性关系,因此,当标记点不满足共面条件时,可根据该特点进行测量误差修正。静态标定和风洞迎角测量试验结果表明:修正系统误差后,迎角实测数据的测量准度在0.01°以内,精度不超过0.012°。本文方法易于实施,工程实用价值强。 相似文献
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飞机结构件连接以机械连接为主,连接孔的位置精度和表面质量对飞机的使用寿命及安全性有着重要影响。制孔设备的智能化、自动化、柔性化直接影响着飞机制造的质量、效率、成本等。国内对于自动化制孔设备的研究与应用还在较为初级的阶段,并未大规模投入使用,研制此类设备具有重大意义。为此研制了爬行制孔机器人,并使用旋量法针对研制的爬行制孔机器人的运动进行计算分析。首先,计算爬行制孔机器人的调姿以及纠偏运动的自由度,随后对该机器人的调姿和纠偏运动进行解算,并分析了机器人运动过程中的运动特点,为未来控制系统设计打下基础。 相似文献
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面向飞机装配的机器人定位误差和残差补偿 总被引:2,自引:1,他引:2
工业机器人由于其高柔性和低成本而被越来越多地应用到飞机自动钻铆系统中,使用精度补偿有效地提高机器人的绝对定位精度是保证产品质量的关键,为进一步提高机器人末端定位精度,提出了基于误差相似度的残差补偿方法。首先使用基于运动学参数标定的方法辨识出机器人的几何参数误差,再利用基于误差相似度的方法对残余误差进行估计,实现对机器人的误差和残差的补偿。以工业机器人KUKA KR-30 HA为对象所进行的试验验证表明,机器人的绝对定位精度平均值由补偿前的0.879mm经过定位误差补偿后提高到0.194mm,经过残差补偿后进一步提高到0.141mm,经过定位误差和残差补偿后的机器人最大误差由1.492mm降低为0.296mm,最大绝对定位精度误差降低了80.16%。该方法能有效地补偿参数辨识后遗留的残差,进一步提高机器人的定位精度。 相似文献
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Positioning error compensation of an industrial robot using neural networks and experimental study 总被引:1,自引:1,他引:0
Due to the characteristics of high efficiency, wide working range, and high flexibility,industrial robots are being increasingly used in the industries of automotive, machining, electrical and electronic, rubber and plastics, aerospace, food, etc. Whereas the low positioning accuracy,resulted from the serial configuration of industrial robots, has limited their further developments and applications in the field of high requirements for machining accuracy, e.g., aircraft assembly.In this paper, a... 相似文献
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Calibration of robotic drilling systems with a moving rail 总被引:2,自引:1,他引:2
Industrial robots are widely used in aircraft assembly systems such as robotic drilling systems. It is necessary to expand a robot's working range with a moving rail. A method for improving the position accuracy of an automated assembly system with an industrial robot mounted on a moving rail is proposed. A multi-station method is used to control the robot in this study. The robot only works at stations which are certain positions defined on the moving rail. The calibration of the robot system is composed by the calibration of the robot and the calibration of the stations.The calibration of the robot is based on error similarity and inverse distance weighted interpolation.The calibration of the stations is based on a magnetic strip and a magnetic sensor. Validation tests were performed in this study, which showed that the accuracy of the robot system gained significant improvement using the proposed method. The absolute position errors were reduced by about 85%to less than 0.3 mm compared with the maximum nearly 2 mm before calibration. 相似文献
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Industrial robots are used for automatic drilling and riveting.The absolute position accuracy of an industrial robot is one of the key performance indexes in aircraft assembly,and can be improved through error compensation to meet aircraft assembly requirements.The achievable accuracy and the difficulty of accuracy compensation implementation are closely related to the choice of sampling points.Therefore,based on the error similarity error compensation method,a method for choosing sampling points on a uniform grid is proposed.A simulation is conducted to analyze the influence of the sample point locations on error compensation.In addition,the grid steps of the sampling points are optimized using a statistical analysis method.The method is used to generate grids and optimize the grid steps of a Kuka KR-210 robot.The experimental results show that the method for planning sampling data can be used to effectively optimize the sampling grid.After error compensation,the position accuracy of the robot meets the position accuracy requirements. 相似文献
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《中国航空学报》2023,36(8):422-453
An on-machine measuring (OMM) system with a laser displacement sensor (LDS) is designed for measuring free-form surfaces of hypersonic aircraft’s radomes. To improve the measurement accuracy of the OMM system, a novel Iteratively Automatic machine learning Boosted hand-eye Calibration (IABC) method is proposed. Both the hand-eye relationship and LDS measurement errors can be calibrated in one calibration process without any hardware changes via IABC. Firstly, a new objective function is derived, containing analytical parameters of the hand-eye relationship and LDS errors. Then, a hybrid calibration model composed of two kernels is proposed to solve the objective function. One kernel is the analytical kernel designed for solving analytical parameters. Another kernel is the automatic machine learning (AutoML) kernel designed to model LDS errors. The two kernels are connected with stepwise iterations to find the best calibration results. Compared with traditional methods, hand-eye experiments show that IABC reduces the calibration RMSE by about 50%. Verification experiments show that IABC reduces the measurement deviations by about 25%-50% and RMSEs within 40%. Even when the training data are obviously less than the test data, IABC performs well. Experiments demonstrate that IABC is more accurate than traditional hand-eye methods. 相似文献