Navigation algorithm based on semantic segmentation in wheat fields using an RGB-D camera

Determining the navigation line is critical for the automatic navigation of agricultural robots in the farmland.In this research,considering a wheat field as the typical scenario,a novel navigation line extraction algorithm based on semantic segmentation is proposed.The data containing horizontal parallax,height,and grayscale information(HHG)is constructed by combining re-encoded depth data and red-green-blue(RGB)data.The HHG,RGB,and depth data are used to achieve scene recognition and navigation line extraction for a wheat field.The method includes two main steps.First,the semantic segmentation of the wheat,ground,and background are performed using a fully convolutional network(FCN).Second,the navigation line is fitted in the camera coordinate system on the basis of the semantic segmentation result and the principle of camera pinhole imaging.Our segmentation model is trained using 508 randomly selected images from a data set,and the model is tested on 199 images.When labelled data are used as the reference benchmark,the mean intersection over union(mIoU)of the HHG data is greater than 95%,which is the highest among the three types of data.The semantic segmentation methods based on the RGB and HHG data show higher navigation line extraction accuracy rates(with the absolute value of the angle deviation less than 5)than the compared methods.The mean and standard deviation of the angle deviation of the two methods are within 0.1and 2.0,while the mean and standard deviation of the distance deviation are less than 30 mm and 60 mm,respectively.These values meet the basic requirements of agricultural machinery field navigation.The novelty of this work is the proposal of a navigation line extraction algorithm based on semantic segmentation in wheat fields.This method is high in accuracy and robustness to interference from crop occlusion.

Ideal proportional navigation for exoatmospheric interception

Ideal proportional navigation （IPN） is a natural choice for exoatmospheric interception for its mighty capture capability and ease of implementation. The closed-form solution of two- dimensional ideal proportional navigation was conducted in previous public literature, whereas the practical interception happens in the three-dimensional space. A novel set of relative dynamic equations is adopted in this paper, which is with the advantage of decoupling relative motion in the instantaneous rotation plane of the line of sight from the rotation of this plane. The dimension-reduced IPN is constructed in this instantaneous plane, which functions as a three-dimensional guidance law. The trajectory features of dimension-reduced IPN are explored, and the capture regions of dimension-reduced IPN with limited acceleration against nonmaneuvering and maneuvering targets are analyzed by using phase plane method. It is proved that the capture capability of IPN is much stronger than true proportional navigation （TPN）, no matter the target maneuvers or not. Finally, simulation results indicate that IPN is more effective than TPN in exoatmospheric interception scenarios.