# Automating the Short-Loading Cycle: Survey and Integration Framework
## 3 Automation of Wheel Loaders
### Survey
#### 3.2.3 The Full Cycle
\begin{table}
\begin{tabular}{c c c c c} \hline
**Publ.** & **Steps** & **Summary** & **Advantages** & **Disadvantages** \\ \hline
[29] & 2 & \begin{tabular}{c} Compares a neural network and \\ random forest for autonomous \\ scooping. Trained using human \\ demonstrations. Data was collected \\ during the summer months. \\ Random forest using low-level \\ signals and vision performs the best. \\ \end{tabular} & \
\begin{tabular}{c} A simple RF controller successfully \\ learns and performs scooping from \\ a pile. Tested over multiple weather \\ conditions, daytime, and two types \\ of pile material. \\ \end{tabular} &
\begin{tabular}{c} Uncertainty of the skill of the \\ operator whose demonstrations are \\ used. The performance is reported \\ in terms of success classes rather \\ than the fill factor. \\ \end{tabular} \\ \hline
[30] & 2 & \begin{tabular}{c} Based on previous work [29]. \\ Examines how the control would \\ perform during winter months. The \\ simple RF controller that functions \\ during the summer months \\ struggles during the winter months. \\ \end{tabular} & \
\begin{tabular}{c} Shows that the difficulty differences \\ due to seasons can impact what \\ type of controller performs the best. \\ The new proposed controller has \\ higher robustness to changing \\ conditions and a superior success \\ rate compared to the author’s \\ previous work. \\ \end{tabular} &
\begin{tabular}{c} Uncertainty of the skill of the \\ operator whose demonstrations are \\ used. The performance is reported \\ in terms of success classes rather \\ than the fill factor. \\ \end{tabular} \\ \hline
[34] & 2 & \begin{tabular}{c} Q-learning to perform \\ bucket-filling. The model also \\ covers the working state of the \\ wheel loader. Outperforms human \\ operators in terms of fuel \\ consumption. The trained model \\ can be transferred to \\ other materials. \\ \end{tabular} & \
\begin{tabular}{c} Due to being based on a predictive \\ model, there is no need for direct \\ interaction with the real \\ environment during training. \\ Improves the fuel efficiency \\ compared to the operator data. \\ \end{tabular} &
\begin{tabular}{c} Not tested on an actual vehicle; \\ however, it is based on real data. \\ Because it is not being tested on an \\ actual vehicle, it is difficult to say \\ how well the predictive model \\ performs compared to a dynamical \\ model or online training. \\ \end{tabular} \\ \hline
[36] & 2 & \begin{tabular}{c} Develops a test platform to test the \\ operational performance of wheel \\ loaders. Tests and analyzes 9 \\ different bucket trajectories during \\ bucket-filling. The optimal shovel \\ depth is found to be 400 mm. \\ \end{tabular} & \
\begin{tabular}{c} Data and model based on a real \\ wheel loader. The model shows \\ that the optimal scooping depth is \\ 400 mm, outperforming human \\ operators in terms of fuel usage and \\ operational time. \\ \end{tabular} &
\begin{tabular}{c} Total energy consumption is higher \\ for the given solution compared to \\ the human operator. Unclear \\ whether an autonomous solution \\ can have mm preciseness when \\ scooping in a real situation. \\ \end{tabular} \\ \hline
[35] & 2 & \begin{tabular}{c} Formulates the bucket filling as a \\ control problem. Proposes a \\ bucket-filling strategy using \\ optimal control in simulation. \\ \end{tabular} & \
\begin{tabular}{c} The proposed method reduces fuel \\ consumption by around 30\%. The \\ model is validated using a real \\ wheel loader. The proposed \\ algorithm is compared to real \\ drivers. The proposed algorithm \\ can be used online. \\ \end{tabular} &
\begin{tabular}{c} The skill level of the human drivers \\ is unclear. Unclear whether the \\ proposed algorithm is tested on \\ multiple different types of material. \\ \end{tabular} \\ \hline
[50] & 3 & \begin{tabular}{c} Examines the path planning from \\ the scooping point to the reversal \\ point. Formulates the problem as a \\ control of a switching system at \\ some time. Uses approximate \\ dynamic programming to solve this \\ formulation. Finds a \\ near-optimal solution. \\ \end{tabular} & \
\begin{tabular}{c} Compared to other work, \\ the authors consider the lifting \\ action as it is vital for the efficiency \\ of the cycle. The proposed \\ closed-loop solution is insensitive \\ to different initial conditions. \\ \end{tabular} &
\begin{tabular}{c} The solution is not tested on a real \\ wheel loader or compared to real \\ operator data. The model does not \\ seem to be validated towards a real \\ wheel loader, making the results \\ very dependent on the model used. \\ \end{tabular} \\ \hline
[37] & 3, 4 & \begin{tabular}{c} Clothoid-based path generation \\ and path tracking. Tested on a \\ miniature wheel loader. The \\ tracking point is changed during \\ navigation, the rear axle during \\ reversing, and the front axle during \\ forward motion. \\ \end{tabular} & \
\begin{tabular}{c} Highly explainable due to the low \\ randomness in the solution. The \\ solution is validated on a miniature \\ the lift speed is one of the important \\ factors determining the path. \\ \end{tabular} &
\begin{tabular}{c} Because it is a clothoid-based \\ solution, assumptions might not hold in every situation. The lift is not considered in this work, where \\ the lift speed is one of the important \\ factors determining the path. \\ \end{tabular} \\ \hline \end{tabular}
\end{table}