Extraction of raw materials, especially in extremely harsh underground mine conditions, is irrevocably associated with high risk and probability of accidents. Natural hazards, the use of heavy-duty machines, and other technologies, even if all perfectly organized, may result in an accident. In such critical situations, rescue actions may require advanced technologies as autonomous mobile robot, various sensory system including gas detector, infrared thermography, image acquisition, advanced analytics, etc. In the paper, we describe several scenarios related to rescue action in underground mines with the assumption that searching for sufferers should be done considering potential hazards such as seismic, gas, high temperature, etc. Thus, possibilities of rescue team activities in such areas may be highly risky. This work reports the results of testing of a UGV robotic system in an underground mine developed in the frame of the AMICOS project. The system consists of UGV with a sensory system and image processing module that are based on an adaptation of You Only Look Once (YOLO) and Histogram of Oriented Gradients (HOG) algorithms. The experiment was very successful; human detection efficiency was very promising. Future work will be related to test the AMICOS technology in deep copper ore mines.

Tumbling mills have been widely implemented in many industrial sectors for the grinding of bulk materials. They have been used for decades in the production of fines and in the final stages of ore comminution, where optimal levels for the enrichment particles’ sizes are obtained. Even though these ubiquitous machines of relatively simple construction have been subjected to extensive studies, the industry still struggles with very low energy efficiency of the comminution process. Moreover, obtaining an optimal size for the grinding product particles is crucial for the effectiveness of the following processes and waste production reduction. New, innovative processing methods and machines are being developed to tackle the problem; however, tumbling mills are still most commonly used in all ranges of the industry. Since heavy equipment retrofitting is the most costly approach, process optimization with dedicated models and control systems is the most preferable solution for energy consumption reduction. While the classic technological measurements in mineral processing are well adopted by the industry, nowadays research focuses on new methods of the mill’s internal dynamics analysis and control. This paper presents a retrospective overview of the existing models of internal load motion, an overview of the innovations in process control, and some recent research and industrial approaches from the energy consumption reduction point of view.

The possibility of the application of an unmanned aerial vehicle (UAV) in search and rescue activities in a deep underground mine has been investigated. In the presented case study, a UAV is searching for a lost or injured human who is able to call for help but is not able to move or use any communication device. A UAV capturing acoustic data while flying through underground corridors is used. The acoustic signal is very noisy since during the flight the UAV contributes high-energetic emission. The main goal of the paper is to present an automatic signal processing procedure for detection of a specific sound (supposed to contain voice activity) in presence of heavy, time-varying noise from UAV. The proposed acoustic signal processing technique is based on time-frequency representation and Euclidean distance measurement between reference spectrum (UAV noise only) and captured data. As both the UAV and “injured” person were equipped with synchronized microphones during the experiment, validation has been performed. Two experiments carried out in lab conditions, as well as one in an underground mine, provided very satisfactory results.

The monitoring of drilling processes is a well-known topic in the mining industry. It is widely used for rock mass characterization, bit wear monitoring and drilling process assessment. However on-board monitoring systems used for this purpose are installed only on a limited number of machines, and breakdowns are possible. There is a need for a data acquisition system that can be used on different drilling rigs and for an automatic data analysis procedure. In this paper, we focused on the automatic detection of drilling cycles, presenting a simple yet reliable system to be universally installed on drilling rigs. The proposed solution covers hardware and software. It is based on the measurement of electric current and acoustic signals. The signal processing methods include threshold-based segmentation, a short-time envelope spectrum and a spectrum for the representation of results. The results of the research have been verified on a real drilling rig within the testing site of its manufacturer by comparing the results with the data of the on-board monitoring system installed on the machine. Novel aspects of our approach include the detection of the pre-boring stage, which has an intermediate amplitude that masks the real drilling cycles, and the use of the percussion instantaneous frequency, which is estimated by acoustic recordings.

Locating an inspection robot is an essential task for inspection missions and spatial data acquisition. Giving a spatial reference to measurements, especially those concerning environmental parameters, e.g., gas concentrations may make them more valuable by enabling more insightful analyses. Thus, an accurate estimation of sensor position and orientation is a significant topic in mobile measurement systems used in robotics, remote sensing, or autonomous vehicles. Those systems often work in urban or underground conditions, which are lowering or disabling the possibility of using Global Navigation Satellite Systems (GNSS) for this purpose. Alternative solutions vary significantly in sensor configuration requirements, positioning accuracy, and computational complexity. The selection of the optimal solution is difficult. The focus here is put on the assessment, using the criterion of the positioning accuracy of the mobile robot with no use of GNSS signals. Automated geodetic surveying equipment is utilized for acquiring precise ground truth data of the robot’s movement. The results obtained, with the use of several methods, compared: Wheel odometry, inertial measurement-based dead-reckoning, visual odometry, and trilateration of ultra-wideband signals. The suitability, pros, and cons of each method are discussed in the context of their application in autonomous robotic systems, operating in an underground mine environment.

The rising automation level and development of the Industry 4.0 concept in the mining sector increase the risk of cyber-attacks. As a result, this article focuses on developing a risk analysis method that integrates Kaplan’s and Garrick’s approach and fuzzy theory. The proposed approach takes into account the level of automation of the operating mining processes. Moreover, it follows five main steps, including identifying the automation level in a selected mine, definition of cyber-attack targets, identification of cyber-attack techniques, definition of cyber-attack consequences, and risk ratio assessment. The proposed risk assessment procedure was performed according to three cyber-attack targets (databases, internal networks, machinery) and seven selected types of cyber-attack techniques. The fuzzy theory is implemented in risk parameter estimation for cyber-attack scenario occurrence in the mining industry. To illustrate the given method’s applicability, seven scenarios for three levels of mine automation are analyzed. The proposed method may be used to reveal the current cybersecurity status of the mine. Moreover, it will be a valuable guide for mines in which automation is planned in the near future.