# Automating the Short-Loading Cycle: Survey and Integration Framework
## 1 Introduction  
Over the last few decades, automation attempts in the construction industry have increased in frequency. This is because automation of, for example, the used heavy-duty machinery can bring with it a wide set of potential benefits. These benefits include but are not limited to avoiding having humans working in harsh environments [1], more predictable performance and efficiency in operating the machine [2], and redefinition of wheel loader geometry [3]. One of the processes employed in the construction industry is the short-loading cycle where a wheel loader moves material such as rocks, gravel, or dirt from a nearby pile and transports that material to the tipping body of a stationary dump truck. The material is then transported longer distances by the dump truck operator. In the literature, the short-loading cycle is commonly divided into six different steps [4]. These steps include Approaching the pile (1), Loading (2), Retracting from the pile (3), Approaching the dumper (4), Dumping (5), and Retracting from the dumper (6), as seen in Figure 1.  
The short-loading cycle is often performed in, for example, an open pit mine, where material continuously flows from a crusher or conveyor belt into a pile, resulting in a reverending process where there is always material to be scooped. This material is then moved elsewhere for further refinement. Thus, the short-loading cycle is typically performed early on in a larger process, resulting in the need of high performance as any inefficiency has a cascading effect. The short-loading cycle is a repetitive task performed in high quantities by human operators, making it a great option for automation. The short-loading cycle is also highly representative of the type of work for which wheel loaders will be used for [5]. This includes but is not limited to scooping, dumping, and transporting smaller amounts of materials. The experience from the short-loading cycle can potentially be used while attempting to automate other tasks relevant to wheel loaders, but also similar tasks in other domains. Due to the highly repetitive nature of the short-loading cycle, the work can be considered dull for the operators. Operators handle wheel loaders in dirty and dangerous environments that can often be unsuitable for humans, such as underground mines. Automation of wheel loaders could alleviate these negatives while also allowing for enhanced operator engagement where operators can focus on performing more complex tasks. Furthermore, a well-designed driver assistance or automated system can lead to less variance in the efficiency of the cycle [2]. Lastly, automation can allow for a complete transformation of the geometry of the wheel loader, which in turn allows the vehicle easy access deep into, for example, mines and also increases the number of types of environments in which the machine can be used. Even with decades worth of research, there exists no general autonomous solution that can perform the short-loading cycle task to an acceptable level. The short-loading cycle has been demonstrated in full by a rule-based system [6]; however, very low productivity was demonstrated, where data-driven approaches have successfully addressed some sub-steps such as scooping [7]. Previous standalone surveys have focused on a single type of technology, such as computer vision [8], a single type of problem, such as examining how we can assure safety for autonomous solutions [9], how the construction industry could be automated in general [10], or examining the problem of the lack of adoption of automation in the construction industry [11]. Most of these works have examined the challenges of automation of different types of construction equipment or relevant technology assuming a longer time horizon to realize an autonomous system. In addition, only a handful of survey articles examined the automation of specifically wheel loaders in the short-loading task, and many works focused on a diverse set of construction equipment. Figure 1: An overview of the short-loading cycle.