High‐precision control of tracked field robots in the presence of unknown traction coefficients

Abstract

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 that 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 equal to 0.0423 m and 0.88 ms, respectively.

Receding horizon estimation and control with structured noise blocking for mobile robot slip compensation

“…Simulations were run with the configuration as outlined in Section VI above for a range of block sizes S ∈ I Figure 6, where the solution time for SB-O is seen to be competitive with the FHB method, with sub-millisecond scale solutions achieved for all except the unblocked case. While the results presented here focus primarily on higher-speed tracking, the performance of the SB-O method at lower speeds performs at least as well as the FHB method, with performance competitive with [3] [17].…”

Soft Robotics as an Enabling Technology for Agroforestry Practice and Research

“…The same iterative mechanism can be used for integrating the continuum arms discussed previously. TerraSentia traverses rough field terrains successfully due to fine-tuned ground clearance, independent suspension on all four wheels, and independently controlled powerful motors with adaptive traction [62]. The wheels of the robot were also selected to minimize point-pressure while maximizing motion control through an extensive field testing of several different wheel designs.…”

“…For agroforestry applications, the robot must have the ability to move autonomously through a rough field environment. Researchers have designed an algorithm that estimates and accounts for robot wheel slip, and this approach has been validated in several field trials [62,63]. Field trials indicate that the autonomy system is highly accurate in navigating within rows when the reception for real-time kinematic (RTK) satellite based-positioning systems (GPS) is good.…”

Novel Laser-Based Obstacle Detection for Autonomous Robots on Unstructured Terrain

“…operate in dynamic and unstructured environments and face huge challenges due to the inherent uncertainties and the unpredictable conditions [ 3 ]. To achieve stable and robust operations, researchers have to develop many decision-making, autonomous navigation, and control algorithms [ 4 , 5 , 6 ].…”