# Automating the Short-Loading Cycle: Survey and Integration Framework
## 3 Automation of Wheel Loaders
### Survey
#### 3.2.3 The Full Cycle  
In terms of the attempts to automate the full short-loading cycle, only a handful of solutions have been proposed. Furthermore, only a single solution demonstrated the full cycle on a real machine, whereas the majority of works only examined the theoretical benefits of this type of automation. The demonstrated solution was based on three different subsystems: measuring and modeling the environment, task planning, and motion control [6]. This solution was capable of detecting the pile, navigating towards the pile, scooping, navigating to the tipping body, and dumping material in the tipping body. This was performed in a semi-realistic environment; however, it was at low productivity, probably due to safety concerns of controlling such a large vehicle autonomously and not pushing the limits of the solution. All of the subsystems were first tested on miniature wheel loaders, further showing that the development pipeline of transferring the solution from miniature wheel loaders to real wheel loaders can be successful. In terms of data-driven approaches, long-short-term memory (LSTM) has been used to predict the brake pedal aperture [52]. The model was trained on real data from an expert operator and the model could successfully predict the brake pedal aperture. Furthermore, a set of sequential LSTMs has been used to predict the throttle and state variables of the wheel loader throughout the short-loading cycle [53]. The state variable includes tilt pressure, lift pressure, engine speed, and velocity of the wheel loader. The theoretical benefits of automation of the short-loading cycle are, however, substantial, such as a 14% increased fuel efficiency compared to real operators [54]. This makes the pursuit of an autonomous solution worthwhile. Some of the behaviors required for optimality can be difficult for human operators to maintain during long periods. For example, one optimal control formulation showed that it is optimal to perform very fast steering changes throughout the short-loading cycle [55], something that is difficult to maintain over a long time. Furthermore, a multistage optimization problem has been defined where both the transport and the bucket-loading operation were included [56]. The control solution showed a fuel usage decrease of 42.1% over human operators. The theoretical benefits of automation depend not only on the operator choices but also on external variables. This includes the positioning of the dump truck, where a 20-degree change in the orientation of the dump truck increases the fuel consumption during the short-loading cycle by 18% [57]. In addition, if the dump truck is positioned optimally, the trajectory for both time and fuel efficiency is identical. From these attempts at automation of different parts of the short-loading cycle, it is unclear which is the best approach; however, it is clear that there is a requirement of some type of structured pipeline for development. This includes either decomposing the problem into smaller parts having different systems be in charge of different functionality or having some platform that allows for fast testing. This includes having, for example, an LSTM be responsible for the braking aperture while other systems are responsible for the other actions, or creating a development platform such as a miniature wheel loader.