An End-to-End Learning-Based Row-Following System for an Agricultural Robot in Structured Apple Orchards

A row-following system based on end-to-end learning for an agricultural robot in an apple orchard was developed in this study. Instead of dividing the navigation into multiple traditional subtasks, the designed end-to-end learning method maps images from the camera directly to driving commands, which reduces the complexity of the navigation system. A sample collection method for network training was also proposed, by which the robot could automatically drive and collect data without an operator or remote control. No hand labeling of training samples is required. To improve the network generalization, methods such as batch normalization, dropout, data augmentation, and 10-fold cross-validation were adopted. In addition, internal representations of the network were analyzed, and row-following tests were carried out. Test results showed that the visual navigation system based on end-to-end learning could guide the robot by adjusting its posture according to different scenarios and successfully passing through the tree rows.

Automated summary

An agricultural robot row-following system based on end-to-end learning was developed in this study, simplifying the navigation system complexity by mapping images directly to driving commands. An automatic driving data collection method was proposed, and the system was validated through improvements in network generalization and row-following tests in different scenarios.