Monitoring of Inaccessible Areas in GPS-Denied Underground Mines using a Fully Autonomous Encased Safety Inspection Drone

Shahmoradi, Javad; Mirzaeinia, Amir; Roghanchi, Pedram; Hassanalian, Mostafa

doi:10.2514/6.2020-1961

Cited by 16 publications

(8 citation statements)

References 34 publications

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“…Drones have a variety of applications in our daily life that have attracted the attention of many researchers around the world [1][2][3][4][5][6] . Therefore, everyday novel concepts of the drones that are able to fly and perform various missions autonomously in different environments are being developed [7][8][9] . Over recent years, drones or Unmanned Aerial Vehicles (UAVs) are increasingly applied in assisting 5G cellular networks to extend the coverage and service quality 10 .…”

Section: Introductionmentioning

confidence: 99%

Placement of UAV-Mounted Mobile Base Station through User Load-Feature K-means Clustering

Mirzaeinia

Shekaramiz

et al. 2020

Aiaa Aviation 2020 Forum

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Temporary high traffic requests in cellular networks is a challenging problem to address. Recent advances in Unmanned Aerial Vehicles applied to cover these types of traffics. UAV-Mounted Mobile Base Stations placement is a challenging problem to achieve high performance. Different approaches have been proposed; however, user required traffic is not considered in UAV placement. We propose a new feature to apply to K-means clustering to find the optimum clusters. User required traffic is defined as a new feature to assign users to the UAVs. Our simulation results show that UAVs could be placed closer to the high traffic users to achieve higher performance.

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Section: Introductionmentioning

confidence: 99%

Placement of UAV-Mounted Mobile Base Station through User Load-Feature K-means Clustering

Mirzaeinia

Shekaramiz

et al. 2020

Aiaa Aviation 2020 Forum

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“…The paper [74] attempts to analyze the potential for creating a completely autonomous enclosed drone-based remote monitoring system to survey inaccessible zones in underground mines. Flying a drone underground is highly challenging because of the tight quarters, poor sight, air velocity, dense concentration of dust, absence of a wireless connection system, and other factors.…”

Section: Dronesmentioning

confidence: 99%

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

Imam

Bäına

Tabii

et al. 2023

Sensors

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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.

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“…Data fusion uses Kalman filtering and graphical optimization techniques for data with different coordinate systems, data formats, acquisition frequencies and confidence levels. In addition to the extra sensors, one type of solution is encased UAV for anti‐collision protection (Shahmoradi et al, 2020).…”

Section: Underground Uav Inspection and Challengesmentioning

confidence: 99%

“…Özaslan et al (2015) and Dave et al (2020) developed drones with LiDAR and customized cages for collision‐tolerate flight. Shahmoradi et al (2020) used an encased drone to inspect in prior mines. Researchers also suggested contact‐based UAV inspection for tunnels wall (Kocer et al, 2019), ceilings (Song et al, 2021) pipelines (Moss, 2016) and bridges (Higashi & Akahori, 2016; Sanchez‐Cuevas et al, 2019).…”

Section: Path Planning For Uav Inspectionmentioning

confidence: 99%

Unmanned aerial vehicle navigation in underground structure inspection: A review

Zhang,

Hao,

Zhang

et al. 2023

Geological Journal

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Many years after construction, a number of existing old tunnels and underground structures are deteriorating with time as evidenced by cracks, large deformations, water leakage and so forth, which usually require regular site inspections to record their structural deterioration by taking high‐pixel, high‐overlap images along miles of a tunnel network. For complex underground structures (e.g., long tunnels and large caves), unmanned aerial vehicles (UAVs) may be adaptive in acquiring images at multiple heights and angles with low operational costs. So far, UAV underground structural health monitoring has become mature for open‐air surveying with rapid developments in robotic software and hardware. However, the UAV image acquisition for underground working conditions still faces a number of key challenges. This paper aims to provide an overview of UAV navigation techniques in confined dark spaces for geotechnical engineers, geologists, drone developers and other interdisciplinary researchers & professionals in the structural health monitoring field. It specifies the challenges for UAV application in underground space, mainly including lack of Global Navigation Satellite System (GNSS) signals, poor lighting conditions, weak features and obstacle avoidance and then followed by strategic solutions. For example, in light of poor GNSS signals, the fusion of multi‐sensors (e.g., laser imaging, detection and ranging (LiDAR) and multi‐cameras) can enhance localization accuracy in low‐luminance underground conditions. To address obstacle avoidance, computer vision (CV)‐based navigation algorithms (e.g., deep reinforced learning [DRL]) enable effective navigation in complex 3D spaces, but their adaptability is limited by arithmetic power and pre‐training needs. The review of relevant previous studies concludes that further development for UAVs in underground space inspection may focus on operation in large‐scale geometric inspection environments, obstacle avoidance, features and semantic recognition.

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Monitoring of Inaccessible Areas in GPS-Denied Underground Mines using a Fully Autonomous Encased Safety Inspection Drone

Cited by 16 publications

References 34 publications

Placement of UAV-Mounted Mobile Base Station through User Load-Feature K-means Clustering

Placement of UAV-Mounted Mobile Base Station through User Load-Feature K-means Clustering

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

Unmanned aerial vehicle navigation in underground structure inspection: A review

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