## 6 Experiments  
A vision-guided differential-driving AGV prototype was developed, as shown in Figure 15, to verify the accuracy, real-time, and resist-interference performance of the intelligent path recognition based on KPCA-BPNN and IPSO-BTGWP. An obliquely looking-down CCD camera in the front of the vision-guided AGV was used to acquire the straight and curvilinear path images in the different operation fields. The Otsu threshold segmentation method was utilized to extract the possible path pixels from the original path images, which produces 100 experimental samples in total. The curvilinear paths were firstly discriminated from the straight paths in these sample data by means of the path  
Figure 14: The relationship between \(F_{\mathrm{q}}\) and \(\Delta\alpha\). Figure 13: Experiment results of curvilinear path approximation. (**a**) The curve path and (**b**) the curve path with highlights. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Image** & **Guidance Window** & **Number of Noise Points** & **Number of Target Points** & \(\sigma_{\mathrm{q}}^{\mathrm{min}}\)**(_pixel_)** & **Accuracy Rate \%** \\ \hline Figure 12a & 1 & 3 & 44 & 1.42 & 100 \\ \hline Figure 12b & 1 & 18 & 37 & 1.56 & 100 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The approximation accuracy of the curvilinear path. classifier based on KPCA-BPNN. The IPSO-based estimation method was used to obtain the parameters of the straight paths. Each curvilinear path was converted into a polyline path, consisting of a series of end-to-end connected straight paths, which can then be solved using the similar IPSO-based method as the straight path. Since the linear model estimation methods are needed for both the straight path recognition and the curvilinear path approximation, our IPSO-based method was compared with the improved RANSAC (IRANSAC) algorithm [44], and the least squares (LS) method [45] in the experiment. The experiment system includes an industrial computer with a 2 GHz Intel Core i7-4510U processor and a 4 GB RAM at the hardware level, and a Matlab R2014a software on the Windows 8 operating system. The recognition rate and the average time consumption of the three methods are listed in Table 4. Since most experimental samples suffer from noise points, the model estimations of a straight path based on the IRANSAC and LS methods have obvious errors, making it impossible to identify the guide path correctly. Moreover, the real-time performance of the IPSO method is much better than that of the IRANSAC algorithm, i.e., the average time consumption is 61 versus 131.5 ms. The fitting results of straight path based on the IPSO, IRANSAC, and LS methods are shown in Figure 16. The blue points are the raw data of the center pixel set \(\mathrm{S_{C}}\). The red solid line is the estimation result for the straight path using the IPSO method, while the green dotted line and the yellow dashed line are the results based on the IRANSAC and LS methods, respectively. The parameters of the straight path model estimated using the three methods are listed in Table 5.  
If there are no or few noise points in the original path images, the Otsu threshold segmentation method can extract the center pixel set \(\mathrm{S_{C}}\) correctly. In this case, the three estimation methods can all recognize the straight path by fitting the center pixels, with similar recognition accuracy, as shown in Figure 16a. If the original path images are disturbed by the complex illumination, sight-line occlusion or stripe damage, the center pixel set \(\mathrm{S_{C}}\) obtained by the Otsu threshold segmentation inevitably contains many noise points. In this case, the IPSO method still preserves high recognition accuracy for the straight path, while the recognition performance of the IRANSAC and LS methods deteriorates rapidly. Due to the distraction by the noise points, the model parameters estimated by the latter two methods deviate from the actual guide path significantly, as shown in Figure 16b,c.