Agricultural robots have the potential to increase production yields and reduce costs by performing repetitive and time-consuming tasks. However, for robots to be effective, they must be able to navigate autonomously in fields or orchards without human intervention. In this paper, we introduce a navigation system that utilizes LiDAR and wheel encoder sensors for in-row, turn, and end-row navigation in row structured agricultural environments, such as vineyards. Our approach exploits the simple and precise geometrical structure of plants organized in parallel rows. We tested our system in both simulated and real environments, and the results demonstrate the effectiveness of our approach in achieving accurate and robust navigation. Our navigation system achieves mean displacement errors from the center line of 0.049 m and 0.372 m for in-row navigation in the simulated and real environments, respectively. In addition, we developed an end-row points detection that allows end-row navigation in vineyards, a task often ignored by most works.

翻译：农业机器人通过执行重复且耗时的任务，有望提升产量并降低成本。然而，机器人要有效运作，必须能在农田或果园中无需人工干预地自主导航。本文提出一种导航系统，利用激光雷达（LiDAR）和轮式编码器传感器，在葡萄园等结构化农业环境中实现行内、转弯及行末导航。该方法利用植物平行排列结构中简单而精确的几何特征。我们在仿真环境与真实场景中测试了该系统，结果表明该方法能够实现精确且鲁棒的导航。在仿真与真实环境下，该系统行内导航的中线位移误差均值分别为0.049米和0.372米。此外，我们还开发了一种行末点检测方法，实现了葡萄园中的行末导航——而这一任务常被大多数研究忽略。