农业移动机器人偏移点跟踪的预测控制策略
A Predictive Control Strategy to Offset-Point Tracking for Agricultural Mobile Robots
AI总结：
针对农业机器人忽略机具位置导致跟踪误差大的问题，提出一种闭环预测控制策略，显式建模刚性偏移点机具并考虑侧滑和杠杆臂效应，田间实验表明中位跟踪误差降低24%-56%，峰值误差降低70%。
摘要：
机器人越来越多地被部署在农业中，以支持可持续实践并提高生产力。它们为实现精确、高效和环保的操作提供了巨大潜力。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，忽略了所连接机具的空间占用和动力学。在实践中，诸如机械除草机或弹簧齿中耕机之类的机具通常是大型的、刚性安装的，并直接与作物和土壤相互作用；忽略它们的位置会降低跟踪性能并增加作物受损的风险。为解决这一局限，我们提出了一种闭环预测控制策略，扩展了文献[1]中介绍的方法。该方法专门针对阿克曼型农业车辆开发，将机具显式建模为刚性偏移点，同时考虑横向滑移和杠杆臂效应。该方法与最先进的基线控制器进行了基准测试，包括反应式几何方法、反应式反步法和基于模型的预测方案。使用两种不同机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%，并在曲率过渡期间将峰值误差降低了高达70%。这些改进转化为增强的操作安全性，特别是在机具靠近作物行作业的场景中。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
