A new group of fuzzy masks is proposed in this study to perform edge detection of images. A group of 3-pixel masks is used to detect edges in four directions. The resulting images retain more edges and show better continuity of the edges compared to the ones produced by the conventional 3 x 3 fuzzy masks. This is accomplished more efficiently using fewer fuzzy rules. Furthermore, thinning functions incorporated yielded edges with good continuity and optimal thickness.

Edge detection is a computationally efficient and economical image processing technique as this method retains only the edges of surrounding objects for guiding the navigation and localization of a mobile robot. However, present edge detection based methods are heavily dependent on fusion with odometrical data which gradually accumulates localization errors. Moreover, they lack descriptive capabilities. In this paper, a fuzzy view descriptor which fused the information of the detected adjacent primary colours of the vertical lines in the mobile robot’s view with the fuzzified digital compass readings was used to ascertain the actual view. This fusion provided a sense of direction in addition to recognizing the frontal view. The fuzzified distance of the base pixel coordinates of the vertical line of interest determined the attraction of the mobile robot towards it or the repulsion from it. A fuzzy control rule base guided the mobile robot towards the vertical line of interest by constantly reducing its deviation in the edge image. The mobile robot managed to execute its consecutive movements in an intuitive manner without a precise mathematical model. The deviations of the vertical lines were kept small and the detection of the correct views was constantly reliable.