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.

中文翻译：

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面向结构化苹果园农业机器人的基于端到端学习的行跟踪系统

本研究开发了一种基于端到端学习的苹果园农业机器人行跟随系统。设计的端到端学习方法不是将导航划分为多个传统的子任务，而是将来自摄像头的图像直接映射到驾驶命令，从而降低了导航系统的复杂性。还提出了一种用于网络训练的样本采集方法，通过该方法，机器人可以在无需操作员或遥控器的情况下自动驾驶并采集数据。不需要手动标记训练样本。为了提高网络泛化能力，采用了批量归一化、dropout、数据增强和10倍交叉验证等方法。此外，还分析了网络的内部表示，并进行了行跟踪测试。