# 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
[24] & \(1\) & Path generation towards scoping point. Clothoid-based. Determining the scooping direction. & High explainability due to being a rule-based solution. The solution is evaluated on a real miniature wheel loader. & The solution makes a set of assumptions for the generated path that might not hold in all situations. Does not discuss the fill factor or similar metrics. \\ \hline
[27] & \(1\) & Selecting attack point. Achieved by estimating convexity and sideload of the pile. Point cloud data. & Identifies good attack poses from both simulated and real data. The proposed method does not require creating an elevation map, leading to good time complexity. & Difficulties in finding the ground truth for the optimal attack pose due to the complexity of the task. The solution is only tested on a single gravel pile. \\ \hline
[31] & \(1\), \(2\) & Deep deterministic policy gradient [32]. Approach and scooping. Simulation-to-scale-model transfer. & Simulation-to-reality transfer with comparable performance shows that this type of pipeline has potential. The average cycle time is quite low. & Low fill factor compared to the usually desired fill factor (100–110\% depending on material). \\ \hline
[33] & \(1\), \(2\) & Soft Actor-Critic [58]. Teaching one agent to find the scooping point. The second agent performs approach and scooping. Trained in a high-fidelity simulation. & Shows the potential of a purely data-driven solution in this use case. Embedded energy usage in the reward function. & No validation towards real data or real vehicles. Low fill factor compared to the desired fill factor during operation. \\ \hline
[22] & \(2\) & Automated digging. Fuzzy logic for behavior formulation. Finite-state machine to create the formulated behavior. & The solution uses only production sensors. Tested on multiple different pile materials. Good performance compared to expert operators in terms of energy/payload. & Worse performance than operators in terms of the most important metric—productivity. A faster pulling rate might have allowed for even closer performance compared to operators. \\ \hline
[23] & \(2\) & Automated digging. Based on acting bucket forces. A three-step algorithm. Stereo vision for scooped volume estimation. & The solution uses only production sensors. Tested on multiple different pile materials. Good performance compared to expert energy/payload. & Full factor or similar performance not reported. Pile modeling is difficult due to the intra-pile forces. Only tested on a single material. \\ \hline
[19] & \(2\) & Examined different bucket-filling strategies using DEM simulation. A wide set of different trajectory types is examined. Qualitatively “slicing cheese” is the top-performing strategy in both simulation and real-world applications. & Issues in measuring setup make it difficult to conclude the best bucket-filling strategy. Not all type-E trajectories can be tested due to computing limitations. Only general was considered as the material. \\ \hline
[7] & \(2\) & Autonomous scooping. Uses a time-delayed neural network. Trained using imitation learning on 96 examples. Scooped from a pile containing medium coarse gravel. & Matches performance in terms of weight in the bucket while only having a 26\% longer fill time compared to teleoperated. Trained offline and tested on real machines. & Only trained on a single type of material. The throttle is to a constant value, meaning the network cannot optimize this variable. \\ \hline
[28] & \(2\) & Adapts a previous machine-learning model [7]. From medium coarse gravel to cobble gravel. Deterministic actor-critic reinforcement learning algorithm. & The proposed solution is tested on a real vehicle. Requires a low amount of trials to adapt the solution to new material. Not learning from human operators allows for the model to learn tasks where human productivity-based reward function fails to improve productivity. \\ \hline \end{tabular}
\end{table}