# Automating the Short-Loading Cycle: Survey and Integration Framework
## 3 Automation of Wheel Loaders
### Survey
#### 3.2.2 Navigation
Lastly, optimal control has been used to perform path generation for both pile-to-truck and truck-to-pile [45]. The authors of the aforementioned reference tested a set of different controllers to control the articulation angle (steering) in a 3D simulation and found that the LVP-MPC controller performed the best. Due to a high time complexity, the path had to be generated offline, while tracking could be performed online. To track the generated paths, in addition to the articulation controller, a Stanley controller for acceleration, deacceleration, and shifting was used. Multiple different techniques have been applied to examine the optimal trajectory during the short-loading cycle. Based on 2D space discretization, ref. [46] formulated the path planning problem as an optimal control problem, where optimally was defined by fuel efficiency and environmental impact. To find the optimal path, a discrete dynamic programming approach was used. The best performing was the free path, where no working area limitation existed, and adding an obstacle increased the fuel usage; however, compared to measured data, the optimal path reduced the fuel consumption by 14%. Furthermore, this solution was later extended to optimize a path within a known construction environment while also considering the terrain information [47]. From an a priori optimal path, the optimal tracking and switching time from reversing to forward was examined [48]. The wheel loader was modeled using three modes: reversing, forward, and stopping. Adaptive dynamic programming was used to solve the optimal control problem where a neural network was trained to approximate the optimal cost. A driver-in-the-loop model was proposed to examine trajectory traversal optimization in terms of energy flow and energy loss during the short-loading cycle [49]. The analyzed human behavior was embedded into the driving model using a linear quadratic regulator and model predictive control. By applying the driving model to three predefined paths, the authors examined where losses and gains could be found. For example, a longer path increased the travelled length but decreased the steering actuation amount. Therefore, it seems that very few of the mentioned solutions considered the lifting speed of the boom, which is an important variable due to the coupled nature of the wheel loaders. The boom speed dictates the absolute minimum time the navigation can take as the boom has to be raised high enough to make sure the bucket clears the rim of the tipping body before dumping. When examining energy optimization for navigation during the short-loading cycle, boom speed and correct steering movements were found to be essential [50]. Reinforcement learning has been applied to the problem of navigation [51]. The authors used two agents: one that performed the reversal and another that performed the approach towards the dump truck. The agents controlled the vehicle through lift, velocity, and steering. The task was possible for two agents to learn, whereas a single agent trained on the same reward functions failed to learn. The results indicate that task decomposition can facilitate the automation of the short-loading cycle, especially when attempting to leverage deep learning techniques. It is not yet clear what approach is superior due to a small amount of research using data-driven approaches and the lack of validation on real vehicles. However, what is clear is that validating on either miniature or real wheel loaders appears to accelerate the development and is something that should be leveraged for the development of an MVP.