Study on navigating path recognition for the greenhouse mobile robot based on K-means algorithm

In order to improve the robustness to the nonuniform illumination and the real-timness of the mobile robot navigation path recognition system in a greenhouse, firstly, the three components H, S and I are respectively separated from HSI color space, and the H component which has nothing to do with light intensity and can restrain effectively the effect of noise is extracted for the subsequent image processing. For the color characteristic of greenhouse environment, the clustering segmentation of the image is performed based on K-means algorithm to achieve the respective cluster of the path and green crop information. Then, the redundant and interference information existing in the clustered image is eliminated by a morphological corrosion so as to obtain the complete and clear path information. Compared with the conventional threshold segmentation methods, the proposed method can solve the problem of too large memory occupation and too long calculation time caused by the unclear segmentation information for the subsequent Hough transform, thus can enhance the rapidity of the greenhouse path recognition and meet the real-time requirements of automatic navigation and operation of the greenhouse robot. The experiment results show that for the greenhouse robot working in the environment with a complex background and variable light, the proposed method can significantly reduce the effect of the nonuniform illumination on the navigation, that is, has a good robustness to the nonuniform illumination. Furthermore, the processing time of a single image is reduced by 53.26%, so the rapidity of the path recognition can be significantly improved.