视觉导引AGV鲁棒特征识别与精确路径跟踪研究

Robust Feature Recognition and Precise Path Tracking for Vision-guided AGV

针对AGV多分支路径与工位点标识的可靠识别以及导引路径的精确跟踪问题,提出了一种基于双视野窗口的鲁棒特征识别与精确路径跟踪方法。采用整幅视野范围作为模式识别窗口,在该窗口采用基于核主成分分析(KPCA)和BP神经网络的识别方法,将路径特征通过核函数映射到高维空间进行PCA降维,再利用BP神经网络识别降维后的样本矩阵。同时提出一种导引扫描窗口设置方法,该窗口范围取决于摄像机竖直视角以及摄像机安装倾斜角,在导引扫描窗口内将导引路径简化为直线模型并用最小二乘法拟合,针对拟合直线计算导引所需的路径偏差。实验结果表明,KPCA-BP方法显著提高了路径特征识别的实时性和鲁棒性,6类路径特征的平均特征识别正确率为99.5%;导引扫描窗口有效减小了导引路径直线拟合的计算误差,直线路径跟踪误差小于3 mm,曲线路径跟踪误差小于30 mm。

An approach of robust feature recognition and precise path tracking based on two visual field windows was proposed for an AGV to identify multi-branch paths and station point reliably,and to follow guide paths accurately. The whole visual field was used as a pattern recognition window,in which a recognition method based on kernel principal component analysis（ KPCA） and BP neural network was developed. Path features were mapped to a high-dimensional space by using the kernel function and then their dimensionalities were reduced by using PCA. After dimensionality reduction,the sample matrices were recognized by utilizing a BP neural network. Besides,a scaling window method based on a vertical view angle and a tilt installation angle of a camera was suggested for a guidance scanning window. In this window,guide paths were simplified according to a linear model and fitted by using the least square method. Path deviations with respect to the fitted straight line were estimated for AGV guidance.Experimental results show that the KPCA-BP approach improves the real-time performance and robustness of path feature recognition significantly,the average correct rate of which is 99. 5% for six types of landmark feature,and that the guidance scanning window decreases the computing error resulted from linear fitting of guide paths effectively,the tracking error of which is no more than 3 mm for linear path and 30 mm for curvilinear path.