###### Abstract  
Applying computer vision to mobile robot navigation has been studied more than two decades. The most challenging problems for a vision-based AGV running in a complex workspace involve the non-uniform illumination, sight-line occlusion or stripe damage, which inevitably result in incomplete or deformed path images as well as many fake artifacts. Neither the fixed threshold methods nor the iterative optimal threshold methods can obtain a suitable threshold for the path images acquired on all conditions. It is still an open question to estimate the model parameters of guide paths accurately by distinguishing the actual path pixels from the under- or over- segmentation error points. Hence, an intelligent path recognition approach based on KPCA-BPNN and IPSO-BTGWP is proposed here, in order to resist the interferences from the complex workspace. Firstly, curvilinear paths were recognized from their straight counterparts by means of a path classifier based on KPCA-BPNN. Secondly, an approximation method based on BTGWP was developed for replacing the curve with a series of piecewise lines (a polyline path). Thirdly, a robust path estimation method based on IPSO was proposed to figure out the path parameters from a set of path pixels surrounded by noise points. Experimental results showed that our approach can effectively improve the accuracy and reliability of a low-cost vision-guidance system for AGVs in a complex workspace. vision guidance; path classification; model estimation; image processing; curve approximation  
navigation has been studied for more than two decades [4; 5; 6]. Although the well-known Kiva robot can visually recognize an array of bars or two-dimensional codes on the floor as landmarks [7], cameras are still not widely used for the vision guidance of commercial off-the-shelf (COTS) AGV products in the current industrial applications [8]. In order to convince industrial practitioners of the virtues of computer vision, a low-cost vision guidance system based on intelligent recognition and decision-making mechanisms was developed for an AGV operating in a complex flow network [9]. Moreover, [PERSON] [1] declared that their floor mosaic guidance system had achieved sufficient maturity tested in an auto assembly plant. However, system integrators and AGV end-users still hesitate to adopt the scientific advances to their industrial practice, questioning their accuracy, complexity, cost, and reliability. It seems that scientific results published to date neither provide sufficient accuracy for industrial applications nor have they been extensively tested in realistic, industrial-like operating conditions [10]. In fact, the recognition performance of machine vision systems is susceptible to external environments, especially the surrounding illumination [11; 12]. As mentioned by [PERSON] in [6], future developments should concern how to improve the adaptability of the vision system to illumination changes, since the frame grabber gain and offset parameters in image processing have to be adjusted in the light of variable contrast conditions. Some progress has been achieved in the field of dealing with illumination changes in vision navigation [13; 14; 15; 16; 17; 18; 19], e.g., a colour model that separated brightness from chromaticity for feature matches between images with dynamic light sources [14], an entropy minimization method in the log-chromaticity colour space for the estimation of illumination invariant road direction [16], a parallel-snake model for lane detection with broken boundaries [17], a multilayer perceptron learning the relation between lane colour and illumination condition [18], and an adaptive image illumination partitioning and threshold segmentation approach based on a model of illumination and colour [19]. However, these current methods still have some under- or over-segmentation problems that fail to distinguish path pixels from background pixels or vice versa when road surfaces consist of shadow and highlight regions at the same time.