Navigation Path Detection for Cotton Field Operator Robot Based on Horizontal Spline Segmentation

Visual navigation is one of the fundamental techniques of intelligent cotton-picking robot. Cotton field composition is complex and the presence of occlusion and illumination makes it hard to accurately identify furrows so as to extract the navigation line. In this paper, a new field navigation path extraction method based on horizontal spline segmentation is presented. Firstly, the color image in RGB color space is pre-processed by the OTSU threshold algorithm to segment the binary image of the furrow. The cotton field image components are divided into four categories: furrow (ingredients include land, wilted leaves, etc.), cotton fiber, other organs of cotton and the outside area or obstructions. By using the significant differences in hue and value of the HSV model, the authors segment the threshold by two steps. Firstly, they segment cotton wool in the S channel, and then segment the furrow in the V channel in the area outside the cotton wool area. In addition, morphological processing is needed to filter out small noise area. Secondly, the horizontal spline is used to segment the binary image. The authors detect the connected domains in the horizontal splines, and merger the isolate small areas caused by the cotton wool or light spots in the nearby big connected domains so as to get connected domain of the furrow. Thirdly, they make the center of the bottom of the image as the starting point, and successively select the candidate point from the midpoint of the connected domain, according to the principle that the distance between adjacent navigation line candidate is smaller. Finally, the authors count the number of the connected domains and calculate the change of parameters of boundary line of the connected domain to make sure whether the robot reaches the outside of the field or encounters obstacles. If there is no anomaly, the navigation path is fitted by the navigation points using the least squares method. Experiments prove that this method is accurate and effective, which is suitable for visual navigation in the complex environment of a cotton field in different phases.