# Automating the Short-Loading Cycle: Survey and Integration Framework
## 4 Plausible MVP  
From the discussion above, it is clear that there is still a large set of challenges that need to be overcome to realize a fully autonomous solution which is ready for deployment. However, the continuation of the presented approaches is furthering the domain progressively. To continue this positive trend, we attempt to present a reasonable MVP that manages to capture the most important metrics, which are productivity and energy efficiency, while also showing consideration for the challenges outlined in Section 2. Furthermore, this MVP attempts to capture what is required to see some adoption in the industry while considering the current status of the domain together with what appears to be possible to achieve in a shorter time horizon. Prescooping: Prescooping entails both the task of identifying the scooping position and navigating towards it while positioning the wheel loader and bucket in the correct scooping position. From previously presented work, it can be difficult to obtain the ground truth for the optimal scooping point, as different expert operators choose different scooping points on the same pile structure. However, it is clear that operators choose the scooping point at a convex point of the pile to simplify the correct loading of the bucket. The MVP should thus be capable of determining a good scooping point through some vision system, such as LIDAR or cameras, based on where the pile is convex. Furthermore, the MVP should be able to navigate towards the identified scooping point while positioning the bucket and making the rest of the vehicle ready for scooping under some assumptions. Scooping: As previously mentioned, the step that has been given the most amount of attention is the scooping step, where multiple different approaches have been able to match expert operator performance of small and medium grain-sized materials. The majority of these high-performing solutions leveraged deep learning-based solutions to deal with the difficulties of modeling the pile-bucket interactions during scooping. Due to this, we consider that a scooping system for the MVP is capable of matching expert operators in terms of both fill factor and fill time on any small- and medium-sized material. Navigation under load: After the scooping step is completed, the operator reverses from the pile while moving the bucket upwards until the bucket is sufficiently high, at which point the operator changes direction to approach the tipping body of the dump truck. During navigation, the operator has to understand how much steering actuation is proper depending on the load in the bucket, ground material, and conditions. As discussed in Section 2, these interactions can be difficult to model. Many of the previously proposed solutions abstract away many of the difficult variables of the navigation steps, such as lift timing. However, understanding and dealing with at least some of these variables is a requirement of any MVP to see adoption. Furthermore, the MVP should be capable of autonomously deciding both the dumping and the reversal point depending on the relevant variables. Once these points are established, the MVP has to be capable of navigating between the identified points. Dumping: While dumping, the operator spreads the scooped material in the tipping body to balance the load for the operator of the dump truck. This is achieved by leveraging forward and backward motion while emptying the bucket which allows better dispersion of the material in the tipping body. Furthermore, the chosen dumping point depends on where the operator dumped in the previous rounds of the cycle. Very little work has been carried out in terms of optimizing the dumping phase, especially over time. This step is important as a correctly loaded dump truck is able to safely carry more material at a time. An important consequence of an underloaded dump truck is that the overall efficiency of the entire process decreases considerably.