A Comprehensive Review of Applications of Drone Technology in the Mining Industry

Drone's Angle-of-Arrival Estimation Using a Switched-Beam Antenna and Single-Channel Receiver

In this study, we propose a method to estimate the Angle-of-Arrival (AoA) of OFDM-based drone signals with wideband and burst characteristics using only a single-channel receiver and a switched-beam antenna. First, six circularly arranged directional antennas are time-division controlled using RF switches to measure the received power of each antenna. Next, the maximum beam pattern and the measured power of each antenna are synthesized in vector form, and the direction of the synthesized vector becomes the angle of arrival of the drone signal. To verify the proposed method, an experiment was conducted using the video signal of DJI Phantom 4 Pro with a bandwidth of 10 MHz. As a result, it was confirmed that stable angle-of-arrival estimation of drone video signals was possible with an average error of less than 5°. The proposed system has the advantage of being able to estimate the AoA of a drone with only a single receiver without the need for synchronization. Therefore, the proposed system is expected to be used as a low-cost, compact, and highly portable anti-drone system.

Multimodal dataset for indoor 3D drone tracking

The subject of the paper is a multimodal dataset (DPJAIT) containing drone flights prepared in two variants - simulation-based and with real measurements captured by the gold standard Vicon system. It contains video sequences registered by the synchronized and calibrated multicamera set as well as reference 3D drone positions in successive time instants obtained from simulation procedure or using the motion capture technique. Moreover, there are scenarios with ArUco markers in the scene with known 3D positions and RGB cameras mounted on drones for which internal parameters are given. Three applications of 3D tracking are demonstrated. They are based on the overdetermined set of linear equations describing camera projection, particle swarm optimization, and the determination of the extrinsic matrix of the camera attached to the drone utilizing recognized ArUco markers.

Deep Reinforcement Learning-Based Multirestricted Dynamic-Request Transportation Framework

Unmanned aerial vehicles (UAVs) are used in many areas where their usage is increasing constantly. Their popularity, therefore, maintains its importance in the technology world. Parallel to the development of technology, human standards, and surroundings should also improve equally. This study is developed based on the possibility of timely delivery of urgent medical requests in emergency situations. Using UAVs for delivering urgent medical requests will be very effective due to their flexible maneuverability and low costs. However, off-the-shelf UAVs suffer from limited payload capacity and battery constraints. In addition, urgent requests may be requested at an uncertain time, and delivering in a short time may be crucial. To address this issue, we proposed a novel framework that considers the limitations of the UAVs and dynamically requested packages. These previously unknown packages have source-destination pairs and delivery time intervals. Furthermore, we utilize deep reinforcement learning (DRL) algorithms, deep Q-network (DQN), proximal policy optimization (PPO), and advantage actor-critic (A2C) to overcome this unknown environment and requests. The comprehensive experimental results demonstrate that the PPO algorithm has a faster and more stable training performance than the other DRL algorithms in two different environmental setups. Also, we implemented an extension version of a Brute-force (BF) algorithm, assuming that all requests and environments are known in advance. The PPO algorithm performs very close to the success rate of the BF algorithm.

Risks of Drone Use in Light of Literature Studies

This article aims to present the results of a bibliometric analysis of relevant literature and discuss the main research streams related to the topic of risks in drone applications. The methodology of the conducted research consisted of five procedural steps, including the planning of the research, conducting a systematic review of the literature, proposing a classification framework corresponding to contemporary research trends related to the risk of drone applications, and compiling the characteristics of the publications assigned to each of the highlighted thematic groups. This systematic literature review used the PRISMA method. A total of 257 documents comprising articles and conference proceedings were analysed. On this basis, eight thematic categories related to the use of drones and the risks associated with their operation were distinguished. Due to the high content within two of these categories, a further division into subcategories was proposed to illustrate the research topics better. The conducted investigation made it possible to identify the current research trends related to the risk of drone use and pointed out the existing research gaps, both in the area of risk assessment methodology and in its application areas. The results obtained from the analysis can provide interesting material for both industry and academia.

Image Mapping Accuracy Evaluation Using UAV with Standalone, Differential (RTK), and PPP GNSS Positioning Techniques in an Abandoned Mine Site

Global navigation satellite systems (GNSSs) provide a common positioning method that utilizes satellite signals to determine the spatial location of a receiver. However, there are several error factors in standalone GNSS positioning due to instrumental, procedural, and environmental factors that arise during the signal transmission process, and the final positioning error can be up to several meters or greater in length. Thus, real-time kinematic (RTK) correction and post-mission precise point positioning (PPP) processing technologies are proposed to improve accuracy and accomplish precise position measurements. To evaluate the geolocation accuracy of mosaicked UAV images of an abandoned mine site, we compared each orthomosaic image and digital elevation model obtained using standalone GNSS positioning, differential (RTK) GNSS positioning, and post-mission PPP processing techniques. In the three types of error evaluation measure (i.e., relative camera location error, ground control points-based absolute image mapping error, and volumetric difference of mine tailings), we found that the RTK GNSS positioning method obtained the best performance in terms of the relative camera location error and the absolute image mapping error evaluations, and the PPP post-processing correction effectively reduced the error (69.5% of the average total relative camera location error and 59.3% of the average total absolute image mapping error) relative to the standalone GNSS positioning method. Although differential (RTK) GNSS positioning is widely used in positioning applications that require very high accuracy, post-mission PPP processing can also be used in various fields in which it is either not feasible to operate expensive equipment to receive RTK GNSS signals or network RTK services are unavailable.

Towards the portability of knowledge in reinforcement learning-based systems for automatic drone navigation

In the field of artificial intelligence (AI) one of the main challenges today is to make the knowledge acquired when performing a certain task in a given scenario applicable to similar yet different tasks to be performed with a certain degree of precision in other environments. This idea of knowledge portability is of great use in Cyber-Physical Systems (CPS) that face important challenges in terms of reliability and autonomy. This article presents a CPS where unmanned vehicles (drones) are equipped with a reinforcement learning system so they may automatically learn to perform various navigation tasks in environments with physical obstacles. The implemented system is capable of isolating the agents' knowledge and transferring it to other agents that do not have prior knowledge of their environment so they may successfully navigate environments with obstacles. A complete study has been performed to ascertain the degree to which the knowledge obtained by an agent in a scenario may be successfully transferred to other agents in order to perform tasks in other scenarios without prior knowledge of the same, obtaining positive results in terms of the success rate and learning time required to complete the task set in each case. In particular, those two indicators showed better results (higher success rate and lower learning time) with our proposal compared to the baseline in 47 out of the 60 tests conducted (78.3%).

The Future of Mine Safety: A Comprehensive Review of Anti-Collision Systems Based on Computer Vision in Underground Mines

Underground mining operations present critical safety hazards due to limited visibility and blind areas, which can lead to collisions between mobile machines and vehicles or persons, causing accidents and fatalities. This paper aims to survey the existing literature on anti-collision systems based on computer vision for pedestrian detection in underground mines, categorize them based on the types of sensors used, and evaluate their effectiveness in deep underground environments. A systematic review of the literature was conducted following the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines to identify relevant research work on anti-collision systems for underground mining. The selected studies were analyzed and categorized based on the types of sensors used and their advantages and limitations in deep underground environments. This study provides an overview of the anti-collision systems used in underground mining, including cameras and lidar sensors, and their effectiveness in detecting pedestrians in deep underground environments. Anti-collision systems based on computer vision are effective in reducing accidents and fatalities in underground mining operations. However, their performance is influenced by factors, such as lighting conditions, sensor placement, and sensor range. The findings of this study have significant implications for the mining industry and could help improve safety in underground mining operations. This review and analysis of existing anti-collision systems can guide mining companies in selecting the most suitable system for their specific needs, ultimately reducing the risk of accidents and fatalities.

Drone Delivery of Dehydro-Sulfurization Utilizing Doubly-Charged Negative Ions of Nanoscale Catalysts Inspired by the Biomimicry of Bee Species’ Bio-Catalysis of Pollen Conversion to Organic Honey

The sulfur dioxide (SO2) compound is a primary environmental pollutant worldwide, whereas elemental sulfur (S) is a global commodity possessing a variety of industrial as well as commercial functions. The chemical relationship between poisonous SO2 and commercially viable elemental S has motivated this investigation using the Density Functional Theory calculation of the relative transition state barriers for the two-step dehydro-sulfurization oxidation–reduction reaction. Additionally, doubly-charged nanoscale platelet molybdenum disulfide (MoS2), armchair (6,6) carbon nanotube, 28-atom graphene nanoflake (GR-28), and fullerene C-60 are utilized as catalysts. The optimal heterogeneous and homogeneous catalysis pathways of the two-step oxidation–reduction from SO2 to elemental S are further inspired by the biomimicry of the honeybee species’ multi-step bio-catalysis of pollen conversion to organic honey. Potential applications include environmental depollution, the mining of elemental sulfur, and the functionalization of novel technologies such as the recently patented aerial and amphibious LynchpinTM drones.

3D Global Path Planning Optimization for Cellular-Connected UAVs under Link Reliability Constraint

This paper proposes an effective global path planning technique for cellular-connected UAVs to enhance the reliability of unmanned aerial vehicles' (UAVs) flights operating beyond the visual line of sight (BVLOS). Cellular networks are considered one of the leading enabler technologies to provide a ubiquitous and reliable communication link for UAVs. First, this paper investigates a reliable aerial zone based on an extensive aerial drive test in a 4G network within a suburban environment. Then, the path planning problem for the cellular-connected UAVs is formulated under communication link reliability and power consumption constraints. To provide a realistic optimization solution, all constraints of the optimization problem are defined based on real-world scenarios; in addition, the presence of static obstacles and no-fly zones is considered in the path planning problem. Two powerful intelligent optimization algorithms, the genetic algorithm (GA) and the particle swarm optimization (PSO) algorithm, are used to solve the defined optimization problem. Moreover, a combination of both algorithms, referred to as PSO-GA, is used to overcome the inherent shortcomings of the algorithms. The performances of the algorithms are compared under different scenarios in simulation environments. According to the statistical analysis of the aerial drive test, existing 4G base stations are able to provide reliable aerial coverage up to a radius of 500 m and a height of 85 m. The statistical analysis of the optimization results shows that PSO-GA is a more stable and effective algorithm to rapidly converge to a feasible solution for UAV path planning problems, with a far faster execution time compared with PSO and GA, about two times. To validate the performance of the proposed solution, the simulation results are compared with the real-world aerial drive test results. The results comparison proves the effectiveness of the proposed path planning method in suburban environments with 4G coverage. The proposed method can be extended by identifying the aerial link reliability of 5G networks to solve the UAV global path planning problem in the current 5G deployment.

Gamma ray detection performance of newly developed MAPD-3NM-II photosensor with LaBr3(Ce) crystal

This paper presents the gamma-ray detection performance of the newly developed MAPD-3NM-II type SiPM sensor array (4 [Formula: see text] 4) with [Formula: see text](Ce) scintillator. The gamma-ray spectra of various sources have been measured in the energy range from 26 keV up to 1332 keV. The newly developed array based on MAPD-3NM-II sensors proved [Formula: see text] 22% enhancement in energy resolution in comparison to the former MAPD-3NM-I based array. The energy resolution of 662 keV gamma-rays measured by MAPD-3NM-II was 3.3% while clearly surpassing 4.25% resolution of MAPD-3NM-I predecessor. The enhancement is related to the high PDE of the new MAPD-3NM-II. Obtained results show that the new MAPD-3NM-II demonstrated good energy resolution and linearity in the studied energy region. The energy resolution of the new detector developed based on MAPD-3NM-II was better than all previous products of MAPD.

UAV remote sensing applications in marine monitoring: Knowledge visualization and review

With the booming development of information technology and the growing demand for remote sensing data, unmanned aerial vehicle (UAV) remote sensing technology has emerged. In recent years, UAV remote sensing technology has developed rapidly and has been widely used in the fields of military defense, agricultural monitoring, surveying and mapping management, and disaster and emergency response and management. Currently, increasingly serious marine biological and environmental problems are raising the need for effective and timely monitoring. Compared with traditional marine monitoring technologies, UAV remote sensing is becoming an important means for marine monitoring thanks to its flexibility, efficiency and low cost, while still producing systematic data with high spatial and temporal resolutions. This study visualizes the knowledge domain of the application and research advances of UAV remote sensing in marine monitoring by analyzing 1130 articles (from 1993 to early 2022) using a bibliometric approach and provides a review of the application of UAVs in marine management mapping, marine disaster and environmental monitoring, and marine wildlife monitoring. It aims to promote the extensive application of UAV remote sensing in the field of marine research.

Reliable Aerial Mobile Communications with RSRP & RSRQ Prediction Models for the Internet of Drones: A Machine Learning Approach

The unmanned aerial vehicle (UAV) industry is moving toward beyond visual line of sight (BVLOS) operations to unlock future internet of drones applications, including unmanned environmental monitoring and long-range delivery services. A reliable and ubiquitous mobile communication link plays a vital role in ensuring flight safety. Cellular networks are considered one of the main enablers of BVLOS operations. However, the existing cellular networks are designed and optimized for terrestrial use cases. To investigate the reliability of provided aerial coverage by the terrestrial cellular base stations (BSs), this article proposes six machine learning-based models to predict reference signal received power (RSRP) and reference signal received quality (RSRQ) based on the multiple linear regression, polynomial, and logarithmic methods. In this regard, first, a UAV-to-BS measurement campaign was conducted in a 4G LTE network within a suburban environment. Then, the aerial coverage was statistically analyzed and the prediction methods were developed as a function of distance and elevation angle. The results reveal the capability of terrestrial BSs in providing aerial coverage under some circumstances, which mainly depends on the distance between the UAV and BS and flight height. The performance evaluation shows that the proposed RSRP and RSRQ models achieved RMSE of 4.37 dBm and 2.71 dB for testing samples, respectively.

Computing in the Sky: A Survey on Intelligent Ubiquitous Computing for UAV-Assisted 6G Networks and Industry 4.0/5.0

Unmanned Aerial Vehicles (UAVs) are increasingly being used in a high-computation paradigm enabled with smart applications in the Beyond Fifth Generation (B5G) wireless communication networks. These networks have an avenue for generating a considerable amount of heterogeneous data by the expanding number of Internet of Things (IoT) devices in smart environments. However, storing and processing massive data with limited computational capability and energy availability at local nodes in the IoT network has been a significant difficulty, mainly when deploying Artificial Intelligence (AI) techniques to extract discriminatory information from the massive amount of data for different tasks.Therefore, Mobile Edge Computing (MEC) has evolved as a promising computing paradigm leveraged with efficient technology to improve the quality of services of edge devices and network performance better than cloud computing networks, addressing challenging problems of latency and computation-intensive offloading in a UAV-assisted framework. This paper provides a comprehensive review of intelligent UAV computing technology to enable 6G networks over smart environments. We highlight the utility of UAV computing and the critical role of Federated Learning (FL) in meeting the challenges related to energy, security, task offloading, and latency of IoT data in smart environments. We present the reader with an insight into UAV computing, advantages, applications, and challenges that can provide helpful guidance for future research.

Research Trends in Collaborative Drones

The last decade has seen an explosion of interest in drones-introducing new networking technologies, such as 5G wireless connectivity and cloud computing. The resulting advancements in communication capabilities are already expanding the ubiquitous role of drones as primary solution enablers, from search and rescue missions to information gathering and parcel delivery. Their numerous applications encompass all aspects of everyday life. Our focus is on networked and collaborative drones. The available research literature on this topic is vast. No single survey article could do justice to all critical issues. Our goal in this article is not to cover everything and include everybody but rather to offer a personal perspective on a few selected research topics that might lead to fruitful future investigations that could play an essential role in developing drone technologies. The topics we address include distributed computing with drones for the management of anonymity, countering threats posed by drones, target recognition, navigation under uncertainty, risk avoidance, and cellular technologies. Our approach is selective. Every topic includes an explanation of the problem, a discussion of a potential research methodology, and ideas for future research.

Detection of Multiple Drones in a Time-Varying Scenario Using Acoustic Signals

Detection of unauthorized drones is mandatory for defense organizations and also for human life protection. Currently, detection methods based on thermal, video, radio frequency (RF) and acoustic signals exist. In previous research, we presented an acoustic signals-based multiple drones detection technique utilizing independent component analysis (ICA) in the presence of interfering sources. In this paper, a method is proposed in which the mixed signals are first separated taking the ICA technique into account. After extracting the features, the support vector machines (SVM) and the k-nearest neighbors (KNN) are used to identify multiple drones in the field. This technique can detect multiple drones in static and quasi-static mixing scenarios, while failing in time-varying scenarios. In this paper, a time-varying drone detection technique (TVDDT) is proposed that first stores a data set of the mixed signals in a time-varying scenario, where time variations occur within the processing data blocks. After estimating the mixing matrices, we developed a technique to track variations in the channel. This technique is based on variations in the mixing coefficients. The proposed channel tracking technique performs classification and detection based on minimum variation criteria in the channel. The proposed TVDDT technique is evaluated through simulations and its superior performance is observed.

Leveraging UAV Capabilities for Vehicle Tracking and Collision Risk Assessment at Road Intersections

Transportation agencies continue to pursue crash reduction. Initiatives include the design of safer facilities, promotion of safe behaviors, and assessments of collision risk as a precursor to the identification of proactive countermeasures. Collision risk assessment includes reliable prediction of vehicle trajectories. Unfortunately, in using traditional tracking equipment, such prediction can be impaired by occlusion. It has been suggested in recent literature that unmanned aerial vehicles (UAVs) can be deployed to address this issue successfully, given their wide visual field and movement flexibility. This paper presents a methodology that integrates UAVs to track the movement of road users and to assess potential collisions at intersections. The proposed methodology includes an existing deep-learning-based algorithm to identify road users, extract trajectories, and calculate collision risk. The methodology was applied using a case study, and the results show that the methodology can provide beneficial information for the purpose of measuring and analyzing the infrastructure performance. Based on vehicle movements it observes, the UAV can communicate its collision risk to each vehicle so that the vehicle can undertake proactive driving decisions. Finally, the proposed framework can serve as a valuable tool for urban road agencies to develop measures to reduce crash risks.

Analysis of Mine Change Using 3D Spatial Information Based on Drone Image

Mine development requires continuous management because it causes rapid topographic changes and environmental damage. Drones can be used to produce three-dimensional spatial information by quickly and accurately photographing areas that are difficult or dangerous for humans to approach. In this study, we investigated the possibility of using drone photogrammetry for determining changes and recovery in mines. The accuracy of the drone photogrammetry results was analyzed using checkpoints, and the earthwork volume was calculated and compared with that obtained through a field survey. We determined whether the results were consistent with the mountain recovery plan using drone images. The RMSE was 0.085–0.091 m in the plane and 0.121–0.128 m along the elevation, as determined by analyzing the checkpoint accuracy by creating an orthoimage and a digital surface model based on the drone images; these results satisfy the tolerance range of the 1/1000 digital map descriptions. The drone photogrammetry generated an average error of 11.9% using the conventional measurement method. The possibility of use was proved by confirming the vegetation and rock prevention nets using photographed images. The usability of drone photogrammetry in mines is expected to increase if substantial spatial information is produced and analyzed in the future.

Design and Computational Analyses of Nature Inspired Unmanned Amphibious Vehicle for Deep Sea Mining

This paper presents the design calculations, implementations, and multi-engineering based computational constructions of an unmanned amphibious vehicle (UAmV) which efficiently travels underwater to detect and collect deep-sea minerals for investigations, as well as creative usage purposes. The UAmV is expected to operate at a 300 m depth from the water surface. The UAmV is deployed above the water surface near to the approximate target location and swims underwater, checking the presence of various mining, then extracts them using a unique mechanism and stores them in an inimitable fuselage location. Since this proposed UAmV survives in deep-sea regions, the design construction of this UAmV is inspired by hydrodynamic efficient design-based fish, i.e., Rhinaancylostoma. Additionally, standard analytical approaches are followed and, subsequently, the inimitable components such as wing, stabilizers, propellers, and mining storage focused fuselage are calculated. The computational analyses such as hydrodynamic investigations and vibrational investigations were carried out with the help of ANSYS Workbench. The hydrodynamic pressures at various deployment regions were estimated and thereafter the vibrational outcomes of UAmVs were captured for various lightweight materials. The computed outcomes were imposed in the analytical approach and thereby the electrical energy generations by the UAmV’s components were calculated. Finally, the hydrodynamic efficient design and best material were picked, which provided a path to further works on the execution of the focused mission. Based on the low drag generating design profile and high electrical energy induction factors, the optimizations were executed on this work, and thus the needful, as well as suitable UAmV, was finalized for targeted real-time applications.

A combined approach utilizing UAV 3D imaging methods, in-situ measurements, and laboratory experiments to assess water evaporation and trace element uptake by tree species growing in a red gypsum landfill

The extractive industry is increasingly faced with problems of managing contaminated sites. The red gypsum landfill at the Ochsenfeld site is representative of the typology byproduct storage of the Ti-extraction activity. The management of the elemental content and the water body are the issues at this site. The aim of this study was to evaluate the canopy conductance (g_cmax) of various tree species and the content of elements in the leaves, utilizing the opportunity of a demonstration plantation setup in 2014 with sixteen tree species, combined with a growth chamber experiment. We combined the gas exchange measurements with the data from two multispectral cameras with RGB and NIR bands embarked on an unmanned aerial vehicle (UAV). In the field, Ostrya carpinifolia, Maclura pomifera, and Rhus copallina had the highest g_cmax of all planted tree species, and the high transpiration rate in O. carpinifolia was confirmed in a pot-based controlled experiment. Except R. copallina, the species with a high Mn content (O. carpinifolia, Betula pendula, and Salix aquatica grandis) had high stomatal conductance. O. carpinifolia could therefore be a species to exploit in the management of landfill leachates, especially in the context of climate change since this species is well adapted to dry environments.

Drone-Based Identification of Erosive Processes in Open-Pit Mining Restored Areas

Unmanned Aerial Systems, or drones, are very helpful tools for managing open-pit mining operations and developing ecological restoration activities. This article presents a method for identifying water erosion processes in active quarries by means of drone imagery remote sensing, in the absence of pre-existing imagery or mapping for comparison. A Digital Elevation Model (DEM) with a spatial resolution (SR) >10 cm and an orthophoto with an SR >2.5 cm were generated from images captured with a drone and their subsequent photogrammetric processing. By using Geographical Information Systems tools to process the DEM, a detailed drainage network was obtained, the areas of detected water erosion were separated, and the watersheds in the gullies identified. Subsequently, an estimated DEM before the erosive processes was reconstructed by interpolating the gully ridges; this DEM serves as a reference for the relief before the erosion. To calculate the volume of eroded material, the DEM of Differences was calculated, which estimates the volume difference between the previously estimated DEM and the current DEM. Additionally, we calculated the material necessary for the geomorphological adaptation of the quarry and the slope map, which are two valuable factors closely related to the monitoring of erosive processes. The results obtained allowed us to identify the erosion factors quickly and accurately in this type of mining. In the case of water-filled quarries, it would be important to characterize the subsurface relief. Essentially, the presented method can be applied with affordable and non-invasive materials to create digital grid maps at 10 cm resolution, obtaining data ready for 3D metrics, being a very practical landscape modelling tool for characterizing the restoration evolution of open-pit mining spaces.

Mining Exploration with UAV, Low-Cost Thermal Cameras and GIS Tools—Application to the Specific Case of the Complex Sulfides Hosted in Carbonates of Udías (Cantabria, Spain)

The depletion of natural resources implies the need for a constant search for new reserves to satisfy demand. In the mining sector, Unmanned Aerial Vehicles (UAVs) have revolutionised geo-information capture and modelling to allow the use of low-cost sensors for prospecting and exploration for potentially exploitable resources. A very powerful alternative for managing the huge volume of data is the Geographic Information System (GIS), which allows storage, visualisation, analysis, processing and map creation. The research in this paper validates a new quasi-automatic identification of mining resources using GIS thermal-image analysis obtained from UAVs and low-cost sensors. It was tested in a case that differentiated limestone from dolostone with varying iron content, and different thermal behaviour from solar radiation, thereby ensuring that the thermal image recorded these differences. The objective is to discriminate differences in an image in a quasi-automatic way using GIS tools and ultimately to determine outcrops that could contain mineralisation. The comparison between the proposed method with traditional precision alternatives offered differences of only 4.57%, a very small deviation at this early stage of exploration. Hence, it can be considered very suitable.

A Mobile Robot-Based System for Automatic Inspection of Belt Conveyors in Mining Industry

Mechanical systems (as belt conveyors) used in the mining industry, especially in deep underground mines, must be supervised on a regular basis. Unfortunately, they require high power and are spatially distributed over a large area. Till now, some elements of the conveyor (drive units) have been monitored 24 h/day using SCADA systems. The rest of the conveyor is inspected by maintenance staff. To minimize the presence of humans in harsh environments, we propose a mobile inspection platform based on autonomous UGV. It is equipped with various sensors, and in practice it is capable of collecting almost the same information as maintenance inspectors (RGB image, sound, gas sensor, etc.). Till now such experiments have been performed in the lab or in the mine, but the robot was controlled by the operator. In such a scenario the robot is able to record data, process them and detect, for example, an overheated idler. In this paper we will introduce the general concept of an automatic robot-based inspection for underground mining applications. A framework of how to deploy the inspection robot for automatic inspection (3D model of the tunnel, path planing, etc.) are defined and some first results from automatic inspection tested in lab conditions are presented. Differences between the planned and actual path are evaluated. We also point out some challenges for further research.

Applications of drone in disaster management: A scoping review

The use of drones has rapidly evolved over the past decade involving a variety of fields ranging from agriculture, commercial and becoming increasingly used in disaster management or humanitarian aid. Unfortunately, the evidence of its use in mass disasters is still unclear and scarce. This article aims to evaluate the current drone feasibility projects and to discuss a number of challenges related to the deployment of drones in mass disasters in the hopes of empowering and inspiring possible future work. This research follows Arksey and O'Malley framework and updated by Joanna Briggs Institute Framework for Scoping Reviews methodology to summarise the results of 52 research papers over the past ten years, from 2009 to 2020, outlining the research trend of drone application in disaster. A literature search was performed in Medline, CINAHL, Scopus, individual journals, grey literature and google search with assessment based on their content and significance. Potential application of drones in disaster are broad. Based on articles identified, drone application in disasters are classified into four categories; (1) mapping or disaster management which has shown the highest contribution, (2) search and rescue, (3) transportation and (4) training. Although there is a significant increase in the number of publications on use of drone in disaster within the last five years, there is however limited discussion to address post-disaster healthcare situation especially with regards to disaster victim identification. It is evident that drone applications need to be further explored; to focus more on drone assistance to humans especially in victim identification. It is envisaged that with sufficient development, the application of drones appears to be promising and will improve their effectiveness especially in disaster management.

Drone Magnetometry in Mining Research. An Application in the Study of Triassic Cu–Co–Ni Mineralizations in the Estancias Mountain Range, Almería (Spain)

The use of drones in mining and geological exploration is under rapid development, especially in the field of magnetic field prospection. In part, this is related to the advantages presented for over ground surveys, allowing for high-density data acquisition with low loss of resolution, while being particularly useful in scenarios where vegetation, topography, and access are limiting factors. This work analyzes results of a drone magnetic survey acquired across the old mines of Don Jacobo, where Copper-Cobalt-Nickel stratabound mineralizations were exploited in the Estancias mountain range of the Betic Cordillera, Spain. The survey carried out used a vapor magnetometer installed on a Matrice 600 Pro Hexacopter. Twenty-four parallel survey lines were flown with a speed of 5 m/s, orthogonal to the regional strike of the geological structure, and mineralization with 50 m line separation and 20 m flight height over the ground was studied. The interpretation of the magnetic data allows us to reveal and model two high magnetic susceptibility bodies with residual magnetization, close to the old mines and surface mineral shows. These bodies could be related to potential unexploited mineralized areas whose formation may be related to a normal fault placed to the south of the survey area. Our geophysical survey provides essential data to improve the geological and mining potential of the area, allowing to design future research activities.

UAV-Enabled Mobile Edge-Computing for IoT Based on AI: A Comprehensive Review

Unmanned aerial vehicles (UAVs) are becoming integrated into a wide range of modern IoT applications. The growing number of networked IoT devices generates a large amount of data. However, processing and memorizing this massive volume of data at local nodes have been deemed critical challenges, especially when using artificial intelligence (AI) systems to extract and exploit valuable information. In this context, mobile edge computing (MEC) has emerged as a way to bring cloud computing (CC) processes within reach of users, to address computation-intensive offloading and latency issues. This paper provides a comprehensive review of the most relevant research works related to UAV technology applications in terms of enabled or assisted MEC architectures. It details the utility of UAV-enabled MEC architecture regarding emerging IoT applications and the role of both deep learning (DL) and machine learning (ML) in meeting various limitations related to latency, task offloading, energy demand, and security. Furthermore, throughout this article, the reader gains an insight into the future of UAV-enabled MEC, the advantages and the critical challenges to be tackled when using AI.

Blockchain-Aware Distributed Dynamic Monitoring: A Smart Contract for Fog-Based Drone Management in Land Surface Changes

In this paper, we propose a secure blockchain-aware framework for distributed data management and monitoring. Indeed, images-based data are captured through drones and transmitted to the fog nodes. The main objective here is to enable process and schedule, to investigate individual captured entity (records) and to analyze changes in the blockchain storage with a secure hash-encrypted (SH-256) consortium peer-to-peer (P2P) network. The proposed blockchain mechanism is also investigated for analyzing the fog-cloud-based stored information, which is referred to as smart contracts. These contracts are designed and deployed to automate the overall distributed monitoring system. They include the registration of UAVs (drones), the day-to-day dynamic captured drone-based images, and the update transactions in the immutable storage for future investigations. The simulation results show the merit of our framework. Indeed, through extensive experiments, the developed system provides good performances regarding monitoring and management tasks.

A Practical Validation of Uncooled Thermal Imagers for Small RPAS

Uncooled thermal imaging sensors in the LWIR (7.5 μm to 14 μm) have recently been developed for use with small RPAS. This study derives a new thermal imaging validation methodology via the use of a blackbody source (indoors) and real-world field conditions (outdoors). We have demonstrated this method with three popular LWIR cameras by DJI (Zenmuse XT-R, Zenmuse XT2 and, the M2EA) operated by three different popular DJI RPAS platforms (Matrice 600 Pro, M300 RTK and, the Mavic 2 Enterprise Advanced). Results from the blackbody work show that each camera has a highly linearized response (R2 > 0.99) in the temperature range 5–40 °C as well as a small (<2 °C) temperature bias that is less than the stated accuracy of the cameras. Field validation was accomplished by imaging vegetation and concrete targets (outdoors and at night), that were instrumented with surface temperature sensors. Environmental parameters (air temperature, humidity, pressure and, wind and gusting) were measured for several hours prior to imaging data collection and found to either not be a factor, or were constant, during the ~30 min data collection period. In-field results from imagery at five heights between 10 m and 50 m show absolute temperature retrievals of the concrete and two vegetation sites were within the specifications of the cameras. The methodology has been developed with consideration of active RPAS operational requirements.

Using unmanned aerial systems and deep learning for agriculture mapping in Dubai

As part of the sustainable future vision, sustainable agriculture has become an essential pillar of the food security strategies formulated by the Dubai Government. Therefore, the Dubai Emirate began relying on new technology to increase productivity and efficiency. Agriculture applications also depend on accurate land monitoring for timely food security control and support actions. However, traditional monitoring requires field surveys to be performed by experts, which is costly, slow, and rare. Agriculture monitoring systems must be furnished with sustainable land use monitoring solutions, starting with remote sensing using drone surveys for affordable, efficient, and time-sensitive agriculture mapping. Hence, the Dubai Municipality is currently using Unmanned Aerial Vehicles (UAVs) to map the farming areas all over the Emirate, support locating lands conducive to cultivation, and create an accurate agriculture database contributing to the decision-making process in determining areas suitable for crop growth. This study used a novel object detection method coupled with geospatial analysis as an integrated workflow to detect individual crops. The UAV flights were executed using a Trimble UX5 (HP) over twelve communities across the Dubai Emirate for six months. Detection methods were applied to high-resolution drone images, consisting of RGB and near-infrared (NIR) bands. Advanced geoprocessing tools were also used to analyze, evaluate, and enhance the results. The performance of detection of the selected deep learning models are discussed (vegetation cover accuracy = 85.4%, F1-scores for date palms and ghaf trees = 96.03% and 94.54% respectively, with respect to visual interpretation ground truth); moreover, sample images from the datasets are used for demonstrations. The main aim is to offer specialists a solution for measuring and assessing living green vegetation cover derived from the processed images that is integrated. The results provide insight into using UAS and deep learning algorithms as a solution for sustainable agricultural mapping on a large scale.

Application of UAV in Search and Rescue Actions in Underground Mine—A Specific Sound Detection in Noisy Acoustic Signal

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.

Modeling Mine Workforce Fatigue: Finding Leading Indicators of Fatigue in Operational Data Sets

Mine workers operate heavy equipment while experiencing varying psychological and physiological impacts caused by fatigue. These impacts vary in scope and severity across operators and unique mine operations. Previous studies show the impact of fatigue on individuals, raising substantial concerns about the safety of operation. Unfortunately, while data exist to illustrate the risks, the mechanisms and complex pattern of contributors to fatigue are not understood sufficiently, illustrating the need for new methods to model and manage the severity of fatigue’s impact on performance and safety. Modern technology and computational intelligence can provide tools to improve practitioners’ understanding of workforce fatigue. Many mines have invested in fatigue monitoring technology (PERCLOS, EEG caps, etc.) as a part of their health and safety control system. Unfortunately, these systems provide “lagging indicators” of fatigue and, in many instances, only provide fatigue alerts too late in the worker fatigue cycle. Thus, the following question arises: can other operational technology systems provide leading indicators that managers and front-line supervisors can use to help their operators to cope with fatigue levels? This paper explores common data sets available at most modern mines and how these operational data sets can be used to model fatigue. The available data sets include operational, health and safety, equipment health, fatigue monitoring and weather data. A machine learning (ML) algorithm is presented as a tool to process and model complex issues such as fatigue. Thus, ML is used in this study to identify potential leading indicators that can help management to make better decisions. Initial findings confirm existing knowledge tying fatigue to time of day and hours worked. These are the first generation of models and future models will be forthcoming.

Mobile Network Performance and Technical Feasibility of LTE-Powered Unmanned Aerial Vehicle

Conventional and license-free radio-controlled drone activities are limited to a line-of-sight (LoS) operational range. One of the alternatives to operate the drones beyond the visual line-of-sight (BVLoS) range is replacing the drone wireless communications system from the conventional industrial, scientific, and medical (ISM) radio band to a licensed cellular-connected system. The Long Term Evolution (LTE) technology that has been established for the terrestrial area allows command-and-control and payload communications between drone and ground station in real-time. However, with increasing height above the ground, the radio environment changes, and utilizing terrestrial cellular networks for drone communications may face new challenges. In this regard, this paper aims to develop an LTE-based control system prototype for low altitude small drones and investigate the feasibility and performance of drone cellular connectivity at different altitudes with measuring parameters such as latency, handover, and signal strength. The measurement results have shown that by increasing flight height from ground to 170 m the received signal power and the signal quality levels were reduced by 20 dBm and 10 dB respectively, the downlink data rate decreased to 70%, and latency increased up to 94 ms. It is concluded that although the existing LTE network can provide a minimum requirement for drone cellular connectivity, further improvements are still needed to enhance aerial coverage, eliminate interference, and reduce network latency.