###### Abstract
If the path pixels are recognized correctly with few under- or over-segmentation errors, it is not difficult to obtain the path equation by means of model estimation methods. The points conforming to the parallel characteristics, the length and angle characteristics, and the intercept characteristics of lane lines are selected in Hough space and then directly converted into the lane line equation [20]. An effective lane detection method based on an improved Canny edge detector and least square fitting was proposed in [21].The dual-threshold selection of a traditional Canny detector was improved using the histogram concavity analysis, and the least square method was used to fit the feature points of detected edges to accurate and single-pixel wide lane. [PERSON] [22] proposed an algorithm based on improved river flow and random sample consensus (RANSAC) for the detection of curve lanes under challenging conditions, including dashed lane markings and vehicle occlusion. RANSAC was used to calculate the curvature of curve lanes according to the feature points with noises, derived from the process of improved river flow. A new method based on a binary image pyramid and two different dimensional Hough transforms was developed to extract lines from the images with noise points [23]. The foregoing methods, i.e., the least square method, the RANSAC algorithm, and the Hough transform, can provide satisfactory recognition results under the ideal conditions of feature points without noises, while they tend to be sensitive to the interferences from the complex surrounding illumination. In fact, if high-quality images can be obtained by means of cameras, image-based navigation methods will be used broadly for maritime, aerial, and terrestrial environments. In the framework of comparative navigation, or termed terrain reference navigation, the position can be plotted by comparing a dynamically acquired image with a pattern image, which may be a numeric radar, sonar or aerial charts [24; 25]. Similarly, the nonlinear characteristics of a feature-based positioning sensor need to be handled for terrain reference navigation systems [26]. In this sense, the acquisition of the high-quality images is vital for vision navigation of mobile robots, which requires an accurate and reliable vision recognition system. The motivation of this paper lies in improving the accuracy and reliability of vision recognition of guide paths for AGVs running in a complex circumstance, by means of artificial intelligence methods. An intelligent path recognition framework consisting of path classification, straight path estimation, and curvilinear path approximation is devised for vision guidance of AGVs. Our main contributions are three-fold: (1) An intelligent path classifier based on kernel principal component analysis (KPCA) and a back-propagation neural network (BPNN) is proposed for distinguishing curvilinear paths from straight paths in an image with noise points; (2) a curvilinear path approximation method based on binary tree guidance window partition (BTGWP) is developed for substituting a series of piecewise linear paths for curvilinear paths, in accordance with a given approximation accuracy; and (3) a resist-interference path estimation method based on improved particle swarm optimization (IPSO) is investigated to figure out the path parameters from a set of path pixels with many under- or over-segmentation error points. The remainder of this paper is organized as follows. The path recognition problem for AGVs traveling in a complex illumination circumstance is formulated in Section 2. Feature extraction and path classification based on KPCA and BPNN is proposed in Section 3. A path measurement model and the straight path estimation method based on IPSO are investigated in Section 4. The curve approximation method based on BTGWP is developed in Section 5. Experimental results of path recognition are analyzed by comparing our approach with some previous methods in Section 6. Conclusions and recommendations for future research are given in Section 7.