Recognition algorithm of drivable area between rows of fruit trees based on double robustness regression

In order to extract the working path in the agricultural robot navigation system, we proposed an algorithm for identifying the drivable area between rows of fruit trees with the sky as the background in a complex environment.

The tree crown and the background sky were separated by the blue component (B component), and the Otsu algorithm was improved to achieve a better effect of segmentation. After morphological processing, according to the regularity of tree top distribution, dynamic threshold was used to find “V-shaped” region of interest and extract feature points. After the interference points were eliminated by Theil-Sen robustness regression, the straight line at the tree top was fitted by random sample consensus (RANSAC) algorithm, the slope of the straight line at the edge of the drivable area was obtained through the slope transformation relationship, and the key point coordinates were obtained using the information of the feature points after elimination and the threshold elimination. Taking the slope as the constraint condition, the linear equation of the edge of the drivable area was obtained by substituting the key points. The least square method was used to fit the data for realizing the recognition of the drivable area.

The experimental results showed that compared with Theil-Sen algorithm and RANSAC algorithm, the average deviation angle of the double robustness regression algorithm in this paper was reduced by 8.28% and 9.88%, the standard deviation was reduced by 6.25% and 22.89%, and the accuracy was improved by 4.64% and 10.49%.

The research results can provide research ideas for the drivable area recognition and path extraction of agricultural robots in the complex environment of most standardized orchards.