# Automating the Short-Loading Cycle: Survey and Integration Framework
## 3 Automation of Wheel Loaders
### Survey
#### 3.2.1 Bucket-Filling  
Bucket-filling, or scooping, has seen the most research activity when it comes to the automation of the short-loading cycle. One explanation for this is that during the bucket-filling phase, it is possible to receive direct feedback from the interactions between material and bucket. Furthermore, the phase is short and also complex. Lastly, modeling the pile is difficult because of the internal forces acting on each other when there is interaction with the pile [4]. Rule-based solutions attempt to mimic observed human operator behavior through handwritten rules. One of these behaviors is lifting the bucket from the ground to the exit height while simultaneously tiling the bucket [19]. This was found to be the optimal strategy when examining bucket-filling in simulations. However, this could unfortunately not be verified to be the optimal bucket-filling strategy for expert operators on real machines. Tentatively, the authors still concluded that this is the best strategy for human operators when filling the bucket. Fuzzy logic was used to formulate another observed operator behavior programmatically to perform the scooping of material [22] together with an FSM that executed the programmed behavior, successfully matching operator performance in terms of energy and payload. This seems to indicate that there is not one unique strategy that will result in meeting the recommended fill factor after scooping on real machines. Following this, a three-step algorithm was proposed based on the three observed bucket-moving phases during scooping [23]. It is concerned with the way the bucket moves inside the pile throughout the bucket-filling process. This solution was tested on a miniature wheel loader and the fill factor was estimated through stereo vision and a column model of the pile, but it was not compared to real operators. Multiple early attempts were made to generate the path towards the scooping point using different algorithms such as a mixture of clothoid curves and straight lines [24], where a clothoid is a curve whose curvature is proportional to the length of the path [25]. This, together with the column model and the stereo vision setup to detect the pile, was used to scoop material from a pile. The authors used a method similar to that which has been used to generate the V path between the scooping point and the dump truck [26]. Not comparing the performance of the autonomous system to expert operators or the recommended fill factor for the machine is problematic due to the importance of matching the performance of expert operators in the short-loading cycle. As previously mentioned, the first step of scooping material from a pile is to determine a spot from where to scoop. Some attempts have been made to create a system that leverages Light Detection and Ranging (LIDAR) to find the optimal scooping point [27]. Here, four algorithms were tested. These include column-model estimates, the two-part bucket model, the three-part bucket model, and quadric fitting. The authors concluded that the quadratic fitting was the best-performing solution for determining a scooping position. However, the authors found that it seems to be difficult to create a dataset or determine the ground truth of the best scooping position. The authors found that when operators of the same skill level were presented with a pile, they offered multiple different answers for the optimal scooping point, making it difficult to determine a true optimal scooping point for each pile shape. In later years, deep learning solutions were applied to the problem of autonomous bucket-filling since modeling the internal forces within a pile during the bucket-filling phase is difficult, for example, when teaching a time-delayed neural network to perform regression by classification and using that solution to autonomously scoop material using a real wheel loader [7]. This time-delayed neural network was trained on 100 bucket-filling examples by an expert operator and could match the operator in terms of the fill factor.