针对自动引导车(AGV)视觉引导过程中多分支路径识别与跟踪的实时性与稳健性要求, 提出一种主成分分析(PCA)-线性判别分析(LDA)与支持向量机(SVM)相结合的路径识别算法。首先对AGV行驶过程中拍摄的图像进行预处理, 并用PCA与LDA对处理后的图像进行降维和特征提取, 再利用灰狼优化算法优化后的SVM分类器对图像进行识别。在路径跟踪方面, 利用最小二乘拟合方法计算横向偏差与航向偏差。实验表明, PCA-LDA与SVM相结合能够使路径识别率达到99.3%, 并且满足实时性要求, 路径跟踪误差在20 mm以内, 满足一般工业环境需求。

An algorithm combining principal component analysis (PCA)-linear discriminant analysis (LDA) with support vector machine (SVM) is proposed for the real-time and robustness requirements of multi-branch path identification and tracking in the process of automated guided vehicle(AGV) visual guidance. Firstly, the image features are obtained by image preprocessing algorithm and PCA-LDA algorithm. Next, the image is identified by SVM classifier, which is optimized by gray wolf optimization algorithm. In the aspect of path tracking, the lateral deviation and course deviation are calculated by using the least square fitting method. The experimental results show that the rate of path recognition is 99.3% and real-time requirements are achieved by using the algorithm combining PCA-LDA with SVM, and the path tracking error is within 20 mm to meet the general industrial environmental needs.