# Autonomous Loading System for Load-Haul-Dump (LHD) Machines Used in Underground Mining
## 1 Introduction  
The automation of mining equipment is an important requirement in the mining industry. This is because mining operations need to increase the safety of the workers, as well as to augment the productivity, efficiency, and predictability of the processes. Safety is, without doubt, a key factor, and has been the top priority of mining companies during the past decades. This is particularly true for underground operations, with hazardous environments where workers are exposed to constant risks of rock falls, rock bursts, and mud rushes, and where the presence of dust in the air can result in a number of associated occupational diseases in workers [1]. All of these hazards have been steadily increasing as mine operations have gone deeper, and geomechanical conditions therefore become more extreme. As a consequence, great effort has been invested in increasing the automation level of underground mining machines, especially those that operate in high-risk areas [1], one of them being load-haul-dump (LHD) machines (also known as scoop trams). An LHD is a four-wheeled, center-articulated vehicle with a frontal bucket used to load and transport ore on the production levels of an underground mine (see Figure 1). The automation of the transport phase has been covered in many previous publications [2; 3; 4; 5; 6] and is currently commercially available. However, although the loading part has also been a subject of scientific research, [7; 8; 9; 10; 11; 12; 13; 14; 15; 16; 17], its application in mining environments is not as widely used as other types of automation solutions such as autonomous navigation, assisted tele-operation, or collision avoidance. The slower-paced progress in this area could be due to the complexity of the addressed problem: during excavation, the interaction between the bucket and the material is difficult to model, because the bucket-material interaction forces may vary greatly depending on the properties of the material (e.g., humidity, hardness, fragmentation), the rock pile geometry, and the LHD dynamics (mass, speed, etc.). For this reason, the development of autonomous loading solutions requires full-scale experiments under conditions that can be found only in real mining environments, since physical interactions between the machine and the fragmented rock cannot be easily reproduced elsewhere. This adds to the fact that loading maneuvers and techniques are mostly developed and perfected through the experiences of human operators. In this paper, a complete system for the automation of the loading process using LHDs is proposed. This system is designed to integrate all the relevant tasks required for ore loading: rock pile identification, the LHD's positioning in front of the ore pile, charging, excavation, pull back, and payload weighing (see Figure 2). Assuming that in some cases the loading may not be completed autonomously, the system can detect this situation and request the help of a human operator (by tele-operation). Thus, the proposed system falls under the shared autonomy paradigm. In the mining exploitation context, fleets of LHDs are normally supervised by humans, so providing assistance to an autonomous equipment is not an unusual requirement, especially considering that the most common practice is for LHD automation systems to rely on tele-operators to handle the loading task. It must be noticed that providing assistance on demand, i.e., only in the few cases that the autonomous system is not able to load, is much more efficient than the current semi-autonomous operation used in most commercial systems, where navigation is executed autonomously but loading is teleoperated. The core of the autonomous loading system is the excavation algorithm, which is based on the way that human operators excavate: the bucket is t  
Figure 1: Full-scale LHD machine used during the test of the excavation algorithm. Figure 2: Steps of the proposed autonomous loading system.