双向运动型视觉导引AGV关键技术研究及实现
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摘要
AGV（Automated Guided Vehicle，AGV）作为现代制造系统中的物料传送设备，已经得到了广泛应用。从理论上看，视觉导引AGV具有较好的技术应用前景，然而其却没能像电磁导引和激光导引AGV那样广泛使用，主要问题在于视觉导引技术在实时性、鲁棒性和测量精度方面还有待进一步突破。本文在回顾了国内外研究人员在AGV和视觉导航方面的研究现状的基础上，以双向运动型视觉导引AGV为对象，主要围绕四个关键技术问题开展研究。
视觉导引AGV的系统误差主要来自两个方面，即图像失真和摄像机相对AGV运动控制坐标系的位姿误差，精确地标定能够有效消除其系统误差。本文针对双向运动型视觉导引AGV的系统结构特性，本文提出一种基于静止和运动两种状态的系统标定方法。首先在静止状态下采用平面模板标定法标定出摄像机的内部参数、径向畸变参数和相对地面的外部参数，然后建立了一种针对三种图像失真的联合校正模型。最后，在AGV直线运动和自转运动两种状态下，标定出失真校正后的图像坐标系相对AGV运动控制坐标系的旋转和平移参数，在AGV运动控制坐标系中得到摄像机的精确位置姿态，以消除系统误差。实验证明，该方法具有精度高、柔性好的特点。
视觉信息处理要建立在真实、准确的路径特征提取基础之上，即解决与摄像机同轴的环形LED阵列光源对远场景、大视场的非均匀光照问题和高光现象。本文建立了远场景环形LED光源的光照模型，由单色漫反射模板图像，采用非线性最小二乘算法列文伯格—马夸尔特法（Levenberg-Marquardt）估计出光照模型的参数，并通过基于平均辐照度的归一化方法去除非均匀光照的影响。为了解决高光现象，本文首先对YCbCr彩色图像的蓝色色度分量Cb补色，再采用双边滤波算法做图像增强，提高蓝色导引路径特征提取的鲁棒性。
采集的图像经图像失真校正和非均匀光照图像增强后，由彩色图像处理算法可以提取双向导引路径中心线。为了实现直线、圆弧和其他非理想路径的自适应模型估计，本文提出了基于曲率角估计统计特征的自适应路径模型分类和模型估计新方法。首先分析了双向运动型AGV运动特性和3种平面曲线特性，再根据测量目标精度，提出了一种基于曲率角估计统计特征的路径模型分类方法，将路径分为直线、圆弧转弯和非圆弧转弯三种模型；最后，采用基于曲率角估计的自适应加权拟合算法对三种模型的参数进行回归，并对计算结果进行系统误差补偿，有效提高了视觉测量精度。
视觉导引AGV导引路径分为双向路径和多分支路径。为了实时、可靠地识别双向运动型视觉导引AGV的多分支路径，本文根据智能信息融合的思想，将粗糙集理论与多类支持向量机方法结合起来，提出了一种基于知识获取实时性和类的相似性的分层多分支路径识别新方法。利用粗糙集信息粒化理论，采用分层递阶的规则约简方法获得最小的识别决策规则，有效降低分类识别的复杂性；利用分类决策安全区域学习的方法，使线性不可分的不确定性问题有条件地线性可分，提高多分支路径识别的鲁棒性。最后，多种环境下的AGV运行测试验证了该方法的有效性和可靠性。
本文在理论研究的基础上，设计开发了一种双向运动型视觉导引AGV样车NHV-II。NHV-II以TMS320DM642DSP作为视觉导引系统处理器，在嵌入式实时操作系统DSP/BIOS上实现了视频采集、视频处理、通讯等多任务算法。通过车载RFID读卡器、工业无线以太网通讯和地面控制站实现AGVS地图创建、工位识别、路径规划、任务调度和状态监控。最后，在多种不同实验环境下通过NHV-II，对本文提出的方法和技术进行了实验验证。实验表明其有效解决了视觉导引技术在实时性、鲁棒性和测量精度方面技术难题，为视觉导引AGV的推广应用打下了技术基础。
最后，总结了本文的主要创新之处和研究成果，指出了视觉导引AGV有待进一步研究解决的难点问题和方向，期望在以后的研究中能够逐步完善、解决。
Automated Guided Vehicle（AGV）has been widely used as transport for materials in modernmanufacturing system. Theoretically, vision-guided AGV has a good application prospect, but it hasnot been widely equipped as electromagnetic guided and laser guided AGV. The key issues are thatthe real time ability, robustness and accuracy of the vision-guided technologies have to be improved.This paper reviews the current researches on AGV and vision-guided technologies, and presents a setof new methods mainly to solve the following four key problems for bidirectional vision-guided AGV.
System error of vision-guided AGV comes from two aspects, the image distortion and the poseerror of camera compared to the motion control coordinate system, which can be effectivelyeliminated by accurate system calibration. Considering the system structure characteristics ofbidirectional vision-based AGV, a system calibration technology based on static state and motion stateis proposed. Firstly, the internal parameters, radial distortion parameters and external parameters ofcamera are estimated by using planar patterns in the static scene. Then a union model for correctingdistortion is built for three individual image distortion models. Finally, the rotation and translationparameters between the corrected image coordinate system and AGV motion control coordinatesystem can be calibrated in two motion cases, so the camera pose is estimated accurately in AGVmotion control coordinate system. Experimental results show that the technology has the features ofgood flexibility and high accuracy.
Vision information processing must be built on the basis of the authentic and accurate featureextraction for guide path. In practice, the problems of non uniform illumination and high lightphenomena caused by the coaxial annular LED array light used for the far scene and large field mustbe solved. This paper builds an illumination model for the annular LED light at the far scene. Themodel parameters are estimated by using the nonlinear least squares Levenberg-Marquardt algorithmfor the monochromatic diffuse reflection template image. Another method based on irradiance averagenormalization is proposed to remove the influence of non uniform illumination. In order to solve thehigh light phenomenon, the blue color chrominance Cb of YCbCr image is complemented, and thenthe bilateral filtering algorithm is used for image enhancing, which improves the robustness of featureextraction for blue guide path.
After image distortions correcting and non uniform illumination image enhancement, the centerlineof bidirectional guide-path is extracted by using the color image pre-processing algorithm. A newmethod based on statistical characteristics of curvature estimation is presented to adaptively classify the models of straight line, arc turning and other non-ideal paths in this paper. Firstly, the motioncharacteristics of bidirectional AGV and characteristics of three types of planar curve are analyzed.Then in terms of the target accuracy of measurement, a classification method based on the curvatureestimation is proposed to classify path models into straight line, arc turning and non-circular turning.Finally, the curvature estimation based adaptive weight fitting method is proposed for the parametersregression of the three models. The refined parameters are used to compensate system errors, whichimproves the accuracy of vision measurement.
Guide-path for vision-guided AGV can be divided into bidirectional path and cross path. A newhierarchical recognition method based on the real-time ability of knowledge acquisition and thesimilarity of classes is presented to robustly recognize the cross path models for bidirectionalvision-guided AGV in real time by the combination of the rough set theory and the multi-class supportvector machine The knowledge granularities conception and the hierarchical reduction rules of roughset theory are both used to obtain the minimum decision rule, which effectively reduce the complexityof the classification. To improve the robustness of recognition, the learning method of safe area forclassification is presented, which makes the linear inseparable uncertain problem become linearseparable conditionally. Finally, the tests and experiments at various environments verify the validityand reliability of the method.
A prototype of bidirectional vision-based AGV, NHV-II, is implemented based on the study of thetheory. TMS320DM642DSP is used as the vision system processor of NHV-II. The multitaskalgorithms related to video capturing, video processing, and communications, etc are performed onthe real-time embedded system DSP/BIOS. The RFID reader, the industrial wireless local areanetwork communication and the center control workstation are integrated to implement map building,station identification, path planning, scheduling and state monitoring of the AGV system. Finally, themethods and technologies presented in this paper are tested by using NHV-II on different environmentconditions. Experimental results show that the problems of real time ability, robustness and accuracyof the vision-guided technology are improved significantly, which lay fundaments for the promotionof vision-guided AGV.
Finally, the main research results of the thesis are summarized. The difficult problems andvaluable directions for further researching on vision-based AGV are pointed out, which are expectedto be solved and improved gradually in future studies.
视觉导引AGV的系统误差主要来自两个方面，即图像失真和摄像机相对AGV运动控制坐标系的位姿误差，精确地标定能够有效消除其系统误差。本文针对双向运动型视觉导引AGV的系统结构特性，本文提出一种基于静止和运动两种状态的系统标定方法。首先在静止状态下采用平面模板标定法标定出摄像机的内部参数、径向畸变参数和相对地面的外部参数，然后建立了一种针对三种图像失真的联合校正模型。最后，在AGV直线运动和自转运动两种状态下，标定出失真校正后的图像坐标系相对AGV运动控制坐标系的旋转和平移参数，在AGV运动控制坐标系中得到摄像机的精确位置姿态，以消除系统误差。实验证明，该方法具有精度高、柔性好的特点。
视觉信息处理要建立在真实、准确的路径特征提取基础之上，即解决与摄像机同轴的环形LED阵列光源对远场景、大视场的非均匀光照问题和高光现象。本文建立了远场景环形LED光源的光照模型，由单色漫反射模板图像，采用非线性最小二乘算法列文伯格—马夸尔特法（Levenberg-Marquardt）估计出光照模型的参数，并通过基于平均辐照度的归一化方法去除非均匀光照的影响。为了解决高光现象，本文首先对YCbCr彩色图像的蓝色色度分量Cb补色，再采用双边滤波算法做图像增强，提高蓝色导引路径特征提取的鲁棒性。
采集的图像经图像失真校正和非均匀光照图像增强后，由彩色图像处理算法可以提取双向导引路径中心线。为了实现直线、圆弧和其他非理想路径的自适应模型估计，本文提出了基于曲率角估计统计特征的自适应路径模型分类和模型估计新方法。首先分析了双向运动型AGV运动特性和3种平面曲线特性，再根据测量目标精度，提出了一种基于曲率角估计统计特征的路径模型分类方法，将路径分为直线、圆弧转弯和非圆弧转弯三种模型；最后，采用基于曲率角估计的自适应加权拟合算法对三种模型的参数进行回归，并对计算结果进行系统误差补偿，有效提高了视觉测量精度。
视觉导引AGV导引路径分为双向路径和多分支路径。为了实时、可靠地识别双向运动型视觉导引AGV的多分支路径，本文根据智能信息融合的思想，将粗糙集理论与多类支持向量机方法结合起来，提出了一种基于知识获取实时性和类的相似性的分层多分支路径识别新方法。利用粗糙集信息粒化理论，采用分层递阶的规则约简方法获得最小的识别决策规则，有效降低分类识别的复杂性；利用分类决策安全区域学习的方法，使线性不可分的不确定性问题有条件地线性可分，提高多分支路径识别的鲁棒性。最后，多种环境下的AGV运行测试验证了该方法的有效性和可靠性。
本文在理论研究的基础上，设计开发了一种双向运动型视觉导引AGV样车NHV-II。NHV-II以TMS320DM642DSP作为视觉导引系统处理器，在嵌入式实时操作系统DSP/BIOS上实现了视频采集、视频处理、通讯等多任务算法。通过车载RFID读卡器、工业无线以太网通讯和地面控制站实现AGVS地图创建、工位识别、路径规划、任务调度和状态监控。最后，在多种不同实验环境下通过NHV-II，对本文提出的方法和技术进行了实验验证。实验表明其有效解决了视觉导引技术在实时性、鲁棒性和测量精度方面技术难题，为视觉导引AGV的推广应用打下了技术基础。
最后，总结了本文的主要创新之处和研究成果，指出了视觉导引AGV有待进一步研究解决的难点问题和方向，期望在以后的研究中能够逐步完善、解决。
Automated Guided Vehicle（AGV）has been widely used as transport for materials in modernmanufacturing system. Theoretically, vision-guided AGV has a good application prospect, but it hasnot been widely equipped as electromagnetic guided and laser guided AGV. The key issues are thatthe real time ability, robustness and accuracy of the vision-guided technologies have to be improved.This paper reviews the current researches on AGV and vision-guided technologies, and presents a setof new methods mainly to solve the following four key problems for bidirectional vision-guided AGV.
System error of vision-guided AGV comes from two aspects, the image distortion and the poseerror of camera compared to the motion control coordinate system, which can be effectivelyeliminated by accurate system calibration. Considering the system structure characteristics ofbidirectional vision-based AGV, a system calibration technology based on static state and motion stateis proposed. Firstly, the internal parameters, radial distortion parameters and external parameters ofcamera are estimated by using planar patterns in the static scene. Then a union model for correctingdistortion is built for three individual image distortion models. Finally, the rotation and translationparameters between the corrected image coordinate system and AGV motion control coordinatesystem can be calibrated in two motion cases, so the camera pose is estimated accurately in AGVmotion control coordinate system. Experimental results show that the technology has the features ofgood flexibility and high accuracy.
Vision information processing must be built on the basis of the authentic and accurate featureextraction for guide path. In practice, the problems of non uniform illumination and high lightphenomena caused by the coaxial annular LED array light used for the far scene and large field mustbe solved. This paper builds an illumination model for the annular LED light at the far scene. Themodel parameters are estimated by using the nonlinear least squares Levenberg-Marquardt algorithmfor the monochromatic diffuse reflection template image. Another method based on irradiance averagenormalization is proposed to remove the influence of non uniform illumination. In order to solve thehigh light phenomenon, the blue color chrominance Cb of YCbCr image is complemented, and thenthe bilateral filtering algorithm is used for image enhancing, which improves the robustness of featureextraction for blue guide path.
After image distortions correcting and non uniform illumination image enhancement, the centerlineof bidirectional guide-path is extracted by using the color image pre-processing algorithm. A newmethod based on statistical characteristics of curvature estimation is presented to adaptively classify the models of straight line, arc turning and other non-ideal paths in this paper. Firstly, the motioncharacteristics of bidirectional AGV and characteristics of three types of planar curve are analyzed.Then in terms of the target accuracy of measurement, a classification method based on the curvatureestimation is proposed to classify path models into straight line, arc turning and non-circular turning.Finally, the curvature estimation based adaptive weight fitting method is proposed for the parametersregression of the three models. The refined parameters are used to compensate system errors, whichimproves the accuracy of vision measurement.
Guide-path for vision-guided AGV can be divided into bidirectional path and cross path. A newhierarchical recognition method based on the real-time ability of knowledge acquisition and thesimilarity of classes is presented to robustly recognize the cross path models for bidirectionalvision-guided AGV in real time by the combination of the rough set theory and the multi-class supportvector machine The knowledge granularities conception and the hierarchical reduction rules of roughset theory are both used to obtain the minimum decision rule, which effectively reduce the complexityof the classification. To improve the robustness of recognition, the learning method of safe area forclassification is presented, which makes the linear inseparable uncertain problem become linearseparable conditionally. Finally, the tests and experiments at various environments verify the validityand reliability of the method.
A prototype of bidirectional vision-based AGV, NHV-II, is implemented based on the study of thetheory. TMS320DM642DSP is used as the vision system processor of NHV-II. The multitaskalgorithms related to video capturing, video processing, and communications, etc are performed onthe real-time embedded system DSP/BIOS. The RFID reader, the industrial wireless local areanetwork communication and the center control workstation are integrated to implement map building,station identification, path planning, scheduling and state monitoring of the AGV system. Finally, themethods and technologies presented in this paper are tested by using NHV-II on different environmentconditions. Experimental results show that the problems of real time ability, robustness and accuracyof the vision-guided technology are improved significantly, which lay fundaments for the promotionof vision-guided AGV.
Finally, the main research results of the thesis are summarized. The difficult problems andvaluable directions for further researching on vision-based AGV are pointed out, which are expectedto be solved and improved gradually in future studies.
引文
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