## 4 Research Challenges in Agricultural Vehicle Path Tracking Control
### Controller Development  
High-speed working of agricultural machinery is one of the future development trends, which is conducive to improving agricultural production efficiency. Therefore, further research should be conducted on automatic navigation path tracking methods for agricultural machinery under high-speed and variable-speed conditions. In the future development of agricultural machinery path-tracking controllers, the impact of speed on the system cannot be ignored. The slip of agricultural vehicle tires caused by uneven and slippery surfaces is still a key factor in reducing controller accuracy and stability. Although extensive research has been conducted on slip issues, most of them are based on linearized models, and the force analysis has not been fully carried out, resulting in \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Working Scenario** & \multicolumn{2}{c}{**Agricultural Vehicle**} & \multicolumn{2}{c}{**Control Method**} & \multicolumn{2}{c}{**Improvement of Control Methods**} & \multicolumn{2}{c}{**Practical Difficulties**} \\ \hline \multirow{6}{*}{Orchard} & \(\bullet\) & Orchard tractor & \(\bullet\) & Pure pursuit & \multirow{6}{*}{Adjusting the look-ahead distance by the fuzzy algorithm} & \(\bullet\) & High plant density \\  & \(\bullet\) & Orchard sprayer & & model control & & \(\bullet\) & Narrow walking \\  & \(\bullet\) & Orchard vehicle & \(\bullet\) & Fuzzy control & & distance by the fuzzy algorithm & \(\bullet\) & channels \\  & \(\bullet\) & Small, tracked tractor & & & & & & \\ \hline \multirow{6}{*}{Agricultural robot Facility agricultural vehicle} & \multirow{6}{*}{*} & \multirow{6}{*}{Wheeled robots} & \(\bullet\) & Sliding model control & \multirow{2}{*}{\(\bullet\)} & Improving robustness and reducing chattering by combining RBF neural network and SMC & \(\bullet\) & Agricultural facilities have limited space \\  & & & & & & & & \\  & & & & & & & \\  & & & & & & & \\  & & & & & & & \\  & & & & & & & \\ \hline \multirow{6}{*}{Tractor-trailer system} & \multirow{6}{*}{*} & \multirow{6}{*}{Tractor-trailer} & \multirow{6}{*}{\(\bullet\)} & Sliding model control & \multirow{2}{*}{\(\bullet\)} & Suppressing chattering by replacing the symbolic function with a continuous function & \(\bullet\) & The steering structure of articulated vehicles increases vehicles increases the difficulty of path-tracking algorithm & \(\bullet\) & The steering structure of articulated vehicles increases the difficulty of path-tracking algorithm & \(\bullet\) & The steering structure of articulated vehicles increases the difficulty of path-tracking algorithm & \(\bullet\) & The steering structure of articulated vehicles increases the difficulty of path-tracking control \\ \hline \hline \end{tabular}
\end{table} 
Table 5: _Cont._limited accuracy in the model establishment. The pure pursuit model algorithm is applied to different types of agricultural machinery in different scenes, such as dry field tractors, rice transplanters, orchard tractors, and greenhouse agricultural robots. The look-ahead distance plays an important role in improving tracking accuracy. To achieve the adaptive adjustment of the look-ahead distance, existing research has combined the traditional pure pursuit model algorithm with fuzzy control, reinforcement learning algorithm, neural network algorithm, particle swarm optimization algorithm, etc. The determination of the look-ahead distance is affected by many factors, including vehicle speed, steering frequency, and path curvature while existing research has not fully considered the impact of speed and curvature variation on it. The Stanley model is a nonlinear feedback function based on the lateral tracking deviation, which generates steering angle commands using the relative geometric relationship between the vehicle and the predefined trajectory. Different from the pure pursuit model algorithm, the Stanley controller does not need to find the optimal look-ahead distance.