Navigation path extraction for greenhouse cucumber-picking robots using the prediction-point Hough transform

Accurate extraction of navigation path is very important for automated navigation of agricultural robots. Based on the machine vision system, this paper proposes a new algorithm for the fitting of navigation path of greenhouse cucumber-picking robots. Aiming at the problems that the traditional Hough transform has a large amount of calculation and the least square method has a low precision, the prediction point Hough transform algorithm is proposed to extract the navigation path. The prediction-point Hough transform contains 4 parts: intercept area of interest, image segmentation, navigation point extraction, navigation path fitting. In this paper, only the final 160-pixel rows of image captured by the camera are taken as the region of interest. In the image segmentation stage, this paper proposes a new graying factor. For navigation path extraction, a regression equation is used to determine the prediction point, and finally the proposed prediction point Hough transform is used to fit the navigation path. The experimental results show that the proposed grayscale factor can well segment cucumber plants and soil, and the segmentation effect is better than 2G-B-R and G-B grayscale factors. The proposed prediction point Hough transform fits the navigation paths with an average error less than 0.5 degrees, which is 10.25 degrees lower than the average error of the least-square method. Also, the computation time of the proposed Hough transform is 17.92 ms. Compared with the traditional Hough transform, it saves 35.20 ms.

Automated summary

Accurate extraction of navigation path is crucial for automated navigation of agricultural robots. This paper proposes a new algorithm for fitting navigation path of greenhouse cucumber-picking robots using prediction point Hough transform, which shows better segmentation effect and lower error compared to traditional methods. The proposed algorithm has computation time of 17.92 ms, saving 35.20 ms compared to traditional Hough transform.