DEVELOPMENT OF AN AUTONOMOUS NAVIGATION SYSTEM FOR THE SHUTTLE CAR IN UNDERGROUND ROOM & PILLAR COAL MINES

In recent years, autonomous solutions in the multi-disciplinary field of the mining engineering have been an extremely popular applied research topic. The growing demand for mineral supplies combined with the steady decline in the available surface reserves has driven the mining industry to mine deeper underground deposits. These deposits are difficult to access, and the environment may be hazardous to mine personnel (e.g., increased heat, difficult ventilation conditions, etc.). Moreover, current mining methods expose the miners to numerous occupational hazards such as working in the proximity of heavy mining equipment, possible roof falls, as well as noise and dust. As a result, the mining industry, in its efforts to modernize and advance its methods and techniques, is one of the many industries that has turned to autonomous systems. Vehicle automation in such complex working environments can play a critical role in improving worker safety and mine productivity. One of the most time-consuming tasks of the mining cycle is the transportation of the extracted ore from the face to the main haulage facility or to surface processing facilities. Although conveyor belts have long been the autonomous transportation means of choice, there are still many cases where a discrete transportation system is needed to transport materials from the face to the main haulage system. The current dissertation presents the development of a navigation system for an autonomous shuttle car (ASC) in underground room and pillar coal mines. By introducing autonomous shuttle cars, the operator can be relocated from the dusty, noisy, and potentially dangerous environment of the underground mine to the safer location of a control room. This dissertation focuses on the development and testing of an autonomous navigation system for an underground room and pillar coal mine. A simplified relative localization system which determines the location of the vehicle relatively to salient features derived from on-board 2D LiDAR scans was developed for a semi-autonomous laboratory-scale shuttle car prototype. This simplified relative localization system is heavily dependent on and at the same time leverages the room and pillar geometry. Instead of keeping track of a global position of the vehicle relatively to a fixed coordinates frame, the proposed custom localization technique requires information regarding only the immediate surroundings. The followed approach enables the prototype to navigate around the pillars in real-time using a deterministic Finite-State Machine which models the behavior of the vehicle in the room and pillar mine with only a few states. Also, a user centered GUI has been developed that allows for a human user to control and monitor the autonomous vehicle by implementing the proposed navigation system. Experimental tests have been conducted in a mock mine in order to evaluate the performance of the developed system. A number of different scenarios simulating common missions that a shuttle car needs to undertake in a room and pillar mine. The results show a minimum success ratio of 70%

Wearables for Industrial Work Safety: A Survey

Today, ensuring work safety is considered to be one of the top priorities for various industries. Workplace injuries, illnesses, and deaths often entail substantial production and financial losses, governmental checks, series of dismissals, and loss of reputation. Wearable devices are one of the technologies that flourished with the fourth industrial revolution or Industry 4.0, allowing employers to monitor and maintain safety at workplaces. The purpose of this article is to systematize knowledge in the field of industrial wearables’ safety to assess the relevance of their use in enterprises as the technology maintaining occupational safety, to correlate the benefits and costs of their implementation, and, by identifying research gaps, to outline promising directions for future work in this area. We categorize industrial wearable functions into four classes (monitoring, supporting, training, and tracking) and provide a classification of the metrics collected by wearables to better understand the potential role of wearable technology in preserving workplace safety. Furthermore, we discuss key communication technologies and localization techniques utilized in wearable-based work safety solutions. Finally, we analyze the main challenges that need to be addressed to further enable and support the use of wearable devices for industrial work safety

Deep IoT Energy Efficient Wireless Positioning System

Positioning systems in underground mines require an energy-efficient wireless data transmission network to send positioning data to the control centre at the surface. This thesis hypothesised that a hybrid network combining Bluetooth Low Energy (BLE) multi-hop and Long Range (LoRa) networks is an effective solution for the identified research problem. Furthermore, a novel cluster-based positioning system architecture that reduces the workload of the energy-constrained nodes is proposed in this thesis to optimise the power consumption of the hypothesised BLE and LoRa hybrid data transmission network. The evaluation of the proposed BLE and LoRa hybrid data transmission network was conducted using an OMNeT++ BLE Mesh network, which was implemented in this thesis in conjunction with the open-source OMNeT++ FLoRa simulation model. The simulation results obtained using the BLE and LoRa (OMNeT++) models indicate that the end-to-end delay and power consumption results of LoRa outperform BLE when evaluated under identical conditions. LoRa nodes consumed only 2.4 joules of nodal power, while BLE relay nodes consumed 95.7% more at 57 joules. Additionally, the average end-to-end delay for data packet transmission from a transmitter to a receiver up to 120 meters away was approximately 0.205 seconds for the LoRa network. In contrast, this value increased by 99% for the BLE multi-hop network. However, the simulation results obtained from a power-optimised BLE multi-hop network (where BLE relay nodes are integrated with Wake-Up Receivers (WuR)) were consistent with the thesis hypothesis. It reduced the BLE nodal power consumption to 0.205 joules and the end-to-end delay for data packet transmission by 99%. Furthermore, the analysis of end-to-end delay results indicated that LoRa outperforms an optimised BLE multi-hop network when the receiver node-to-transmitter distance exceeds 90 meters. In conclusion, this thesis established that the hypothesised BLE and LoRa hybrid approach effectively addresses the identified research problem when incorporating an optimisation mechanism into the BLE multi-hop network. These findings contribute significantly to the broader understanding of designing power-efficient networks in energy-constrained and challenging environments