  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.
