Robots are increasingly being deployed in agriculture to support sustainable practices and improve productivity. They offer strong potential to enable precise, efficient, and environmentally friendly operations. However, most existing path-following controllers focus solely on the robot's center of motion and neglect the spatial footprint and dynamics of attached implements. In practice, implements such as mechanical weeders or spring-tine cultivators are often large, rigidly mounted, and directly interacting with crops and soil; ignoring their position can degrade tracking performance and increase the risk of crop damage. To address this limitation, we propose a closed-form predictive control strategy extending the approach introduced in [1]. The method is developed specifically for Ackermann-type agricultural vehicles and explicitly models the implement as a rigid offset point, while accounting for lateral slip and lever-arm effects. The approach is benchmarked against state-of-the-art baseline controllers, including a reactive geometric method, a reactive backstepping method, and a model-based predictive scheme. Real-world agricultural experiments with two different implements show that the proposed method reduces the median tracking error by 24% to 56%, and decreases peak errors during curvature transitions by up to 70%. These improvements translate into enhanced operational safety, particularly in scenarios where the implement operates in close proximity to crop rows.

翻译：机器人正越来越多地被部署于农业领域，以支持可持续实践并提升生产力。它们在实现精准、高效且环境友好型操作方面展现出巨大潜力。然而，现有的大多数路径跟踪控制器仅关注机器人的运动中心，而忽视了所挂接农具的空间占用和动力学特性。实际应用中，诸如机械除草机或弹齿式耕耘机等农具通常体积庞大、刚性安装，并直接与作物及土壤交互；忽略其位置会降低跟踪性能，并增加作物受损风险。为应对这一局限，我们提出了一种闭环形式的预测控制策略，该策略拓展了文献[1]所引入的方法。该方法专门针对阿克曼型农业车辆开发，将农具显式建模为刚性偏置点，同时考虑横向滑移和杠杆臂效应。该策略与当前最先进的基线控制器（包括反应式几何方法、反应式反步法以及基于模型的预测方案）进行了基准对比。使用两种不同农具的实际农业实验表明，所提方法将中位跟踪误差降低了24%至56%，且在曲率变化过程中的峰值误差降低了高达70%。这些改进直接转化为操作安全性的增强，尤其是在农具紧邻作物行作业的场景中。