Accurate steering through crop rows that avoids crop damage is one of the most important tasks for agricultural robots utilized in various field operations, such as monitoring, mechanical weeding, or spraying. In practice, varying soil conditions can result in off-track navigation due to unknown traction coefficients so that it can cause crop damage. To address this problem, this paper presents the development, application, and experimental results of a real-time receding horizon estimation and control (RHEC) framework applied to a fully autonomous mobile robotic platform to increase its steering accuracy. Recent advances in cheap and fast microprocessors, as well as advances in solution methods for nonlinear optimization problems, have made nonlinear receding horizon control (RHC) and receding horizon estimation (RHE) methods suitable for field robots that require high frequency (milliseconds) updates. A real-time RHEC framework is developed and applied to a fully autonomous mobile robotic platform designed by the authors for in-field phenotyping applications in Sorghum fields. Nonlinear RHE is used to estimate constrained states and parameters, and nonlinear RHC is designed based on an adaptive system model which contains time-varying parameters. The capabilities of the real-time RHEC framework are verified experimentally, and the results show an accurate tracking performance on a bumpy and wet soil field. The mean values of the Euclidean error and required computation time of the RHEC framework are respectively equal to $0.0423$ m and $0.88$ milliseconds.

翻译：为解决这一问题,本文件介绍了避免作物损害的作物行的准确方向,这是在各种实地行动中,如监测、机械除草或喷洒等,用于农业机器人的农业机器人的最重要任务之一,在监测、机械除草或喷洒等各种实地作业中,不同的土壤条件可能会由于未知的牵引系数导致偏离轨道导航,从而导致作物损害。为解决这一问题,本文件介绍了实时后退地平线估计和控制框架的开发、应用和实验结果,该框架适用于一个完全自主的移动机器人平台,用于一个完全自主的移动机器人平台,用于一个完全自主的移动机器人平台,以提高其指导准确性。最近廉价和快速微处理器的进步,以及非线性优化问题的解决方案方法的进步,使非线性退缩地平线控制(RHC)和重新降低地平线估计方法,这适合于需要高频(毫秒)更新的外地机器人。一个实时的RHEC框架被应用于一个完全自主的移动的移动机器人平台,由作者设计,用于Sorghum田内的平等运动应用。非线性RHE用来估计受限的状态和参数,非线性成本平整的 RHC框架的准确时间框架是用于实时EC的模型,一个测试的模型的模型和测试的模型的模型,一个测试结果的模型的模型的模型和土壤的模型的模型的模型的模型的模型的模型的模型的模型的模型的测试。