# Automating the Short-Loading Cycle: Survey and Integration Framework
## 1 Introduction
The leftmost vehicle is a stationary dump truck ready to have material placed in its tipping body. The rightmost vehicle is the wheel loader starting position ready to navigate towards the pile. The numbers indicate the steps of the short-loading cycle as defined in [4]. These steps include the following: (1) Approach the pile, (2) Loading, (3) Retract from the pile, (4) Approach the dumper, (5) Dumping, (6) Retract from the dumper. The red dot shows the reversal point. The lack of progress outlined above in the domain of the automation of the short-loading cycle is surprising as the potential benefits of an autonomous solution are high. This serves as motivation to explore the reason behind the slow progress and from this identify a way forward towards a plausible defined minimal viable product (MVP) [12]. An MVP is the idea of creating a product with a minimal amount of features to solve a given problem, allowing for early adopters to provide feedback for further development. In this case, an MVP should be comprised of a set of automation solutions for individual steps in the short-cycle loading operation that are in reach for development and can be combined into a coherent system capable of performing the entire cycle without human intervention. The main objective of this work is to examine the following question: \"From the current status of the automation of the short-loading cycle, what is a reasonably defined MVP and what challenges are currently hindering the realization of an MVP?\", with a secondary question being the following: \"Which challenges facing the automation of the short-loading cycle can benefit from the use of deep learning methods?\". In this work, identification and discussion of a set of automation challenges hindering automating the short-loading cycle are presented. A systematic survey of relevant literature is conducted in terms of the previously identified steps to structure and reason about published solutions for automating the complete cycle. An MVP for automating the short-loading cycle is proposed based on the literature presented in the systematic review. A conceptual automation framework is introduced, leveraging finite-state machines (FSMs) as a model to organize individual automation into a system for experimentation with various solutions for the different steps, contributing to the realization of the proposed MVP. Lastly, the literature synthesis prompts a discussion on future challenges for automating the short-loading cycle. The main contributions of this paper are as follows:  
1. MVP identification based on the currently published literature together with best practices performing the short-loading cycle. 2. An FSM-based high-level framework as a model for combining individual automation solutions into a complete system and facilitating experiments on the automation of the short-loading cycle. 3. Identification of open issues and gaps facing the automation of the short-loading cycle and the realization of a reasonably defined MVP. The identified gaps relate to the abstraction level due to rule-based interfaces, enforcement of safe behavior, the effect of assumptions, and the considerations required when using data-driven and classical control solutions in conjunction. The remainder of the paper is structured as follows: Section 2 identifies and discusses some of the challenges that are required to overcome to automate the short-loading cycle. Section 3 presents the survey methodology, survey results, and a qualitative analysis of said results. Section 4 proposes a set of requirements for an MVP capable of performing the short-loading cycle autonomously. Section 5 presents a framework aimed to facilitate experiments for the automation of the short-loading cycle. Section 6 discusses some of the future challenges that need to be overcome to realize the defined MVP. Section 7 concludes the paper and presents a clear direction for future research.