In the last two decades, significant research has been conducted in the field of automated extraction of rock mass discontinuity characteristics from three-dimensional (3D) models. This provides several methodologies for acquiring discontinuity measurements from 3D models, such as point clouds generated using laser scanning or photogrammetry. However, even with numerous automated and semi-automated methods presented in the literature, there is not one single method that can automatically characterize discontinuities accurately in a minimum of time. In this paper, we critically review all the existing methods proposed in the literature for the extraction of discontinuity characteristics such as joint sets and orientations, persistence, joint spacing, roughness and block size using point clouds, digital elevation maps, or meshes. As a result of this review, we identify the strengths and drawbacks of each method used for extracting those characteristics. We found that the approaches based on voxels and region growing are superior in extracting joint planes from 3D point clouds. Normal tensor voting with trace growth algorithm is a robust method for measuring joint trace length from 3D meshes. Spacing is estimated by calculating the perpendicular distance between joint planes. Several independent roughness indices are presented to quantify roughness from 3D surface models, but there is a need to incorporate these indices into automated methodologies. There is a lack of efficient algorithms for direct computation of block size from 3D rock mass surface models.

A state-of-the-art review of automated extraction of rock mass discontinuity characteristics using three-dimensional surface models

The integration of computer-based technologies interacting with industrial machines or home appliances through an interconnected network, for teleoperation, workflow control, switching to autonomous mode, or collecting data automatically using a variety of sensors, is known as Internet of Things (IoT). When applied inside an industrial context, it is possible to immediately benefit from the analytics obtained, contributing to process optimization, machine health, the safety of workers and asset management. IoT can assist real-time platforms in remotely monitoring and operating a complex production system with minimal intervention of humans. Hence it can be beneficial for hazardous industries, such as mining, by increasing the safety of personnel and equipment while reducing operation costs. An ideal smart automated mine could potentially be achievable by gradually taking advantage of IoT. Currently, different sensors are used in mine-related activities, such as geophones in exploration and blast control, piezometers in dewatering and toxic gas detectors in working frontlines. However, a fully integrated automated system is challenging in practice due to infrastructural limitations in communication, data management and storage. Moreover, the tendency of mining companies to continue with traditional methods instead of relying on untested novel techniques decelerates this progress. In this study, the adaptability of the mining industry to IoT systems and its current development is reviewed. Significant challenges of this progress are investigated and recommendations to develop a comprehensive model suited for different mining sections such as exploration, operation and safety considering flexible technologies such as Wireless Sensor Networks and the introduction of Global Data Management.

/pdf/a-comprehensive-review-on-internet-of-things-iot-and-its-4j8ocquwxp.pdf

A Comprehensive Review on Internet of Things (IoT) and its Implications in the Mining Industry

Under harsh geographical conditions where manned flight is not possible, the ability of the unmanned aerial vehicle (UAV) to successfully carry out the payload hold–release mission by avoiding obstacles depends on the optimal path planning and tracking performance of the UAV. The ability of the UAV to plan and track the path with minimum energy and time consumption is possible by using the flight parameters. This study performs the optimum path planning and tracking using Harris hawk optimization (HHO)–grey wolf optimization (GWO), a hybrid metaheuristic optimization algorithm, to enable the UAV to actualize the payload hold–release mission avoiding obstacles. In the study, the hybrid HHO–GWO algorithm, which stands out with its avoidance of local minima and speed convergence, is used to successfully obtain the feasible and effective path. In addition, the effect of the mass change uncertainty of the UAV on optimal path planning and tracking performance is determined. The effectiveness of the proposed approach is tested by comparing it with the metaheuristic swarm optimization algorithms such as particle swarm optimization (PSO) and GWO. The experimental results obtained indicate that the proposed algorithm generates a fast and safe optimal path without becoming stuck with local minima, and the quadcopter tracks the generated path with minimum energy and time consumption.

/pdf/metaheuristic-optimization-based-path-planning-and-tracking-2enc1t9z.pdf

Metaheuristic Optimization-Based Path Planning and Tracking of Quadcopter for Payload Hold-Release Mission

The goal of cross-view image matching based on geo-localization is to determine the location of a given ground-view image (front view) by matching it with a group of satellite-view images (vertical view) with geographic tags. Due to the rapid development of unmanned aerial vehicle (UAV) technology in recent years, it has provided a real viewpoint close to 45 degrees (oblique view) to bridge the visual gap between views. However, existing methods ignore the direct geometric space correspondence of UAV-satellite views, and only use brute force for feature matching, leading to inferior performance. In this context, we propose an end-to-end cross-view matching method that integrates cross-view synthesis module and geo-localization module, which fully considers the spatial correspondence of UAV-satellite views and the surrounding area information. To be specific, the cross-view synthesis module includes two parts: the oblique view of UAV is first converted to the vertical view by perspective projection transformation (PPT), which makes the UAV image closer to the satellite image; then we use conditional generative adversarial nets (CGAN) to synthesize the UAV image with vertical view style, which is close to the real satellite image by learning the converted UAV as the input image and the real satellite image as the label. Geo-localization module refers to existing local pattern network (LPN), which explicitly considers the surrounding environment of the target building. These modules are integrated in a single architecture called PCL, which mutually reinforce each other. Our method is superior to the existing UAV-satellite cross-view methods, which improves by about 5%. 

UAV-Satellite View Synthesis for Cross-View Geo-Localization

High-resolution terrain models of open-pit mine highwalls and benches are essential in developing new automated slope monitoring systems for operational optimization. This paper presents several contributions to the field of remote sensing in surface mines providing a practical framework for generating high-resolution images using low-trim Unmanned Aerial Vehicles (UAVs). First, a novel mobile application was developed for autonomous drone flights to follow mine terrain and capture high-resolution images of the mine surface. In this article, case study is presented showcasing the ability of developed software to import area terrain, plan the flight accordingly, and finally execute the area mapping mission autonomously. Next, to model the drone’s battery performance, empirical studies were conducted considering various flight scenarios. A multivariate linear regression model for drone power consumption was derived from experimental data. The model has also been validated using data from a test flight. Finally, a genetic algorithm for solving the problem of flight planning and optimization has been employed. The developed power consumption model was used as the fitness function in the genetic algorithm. The designed algorithm was then validated using simulation studies. It is shown that the offered path optimization can reduce the time and energy of high-resolution imagery missions by over 50%. The current work provides a practical framework for stability monitoring of open-pit highwalls while achieving required energy optimization and imagery performance.

https://www.mdpi.com/2072-4292/12/14/2283/pdf

A Practical Methodology for Generating High-Resolution 3D Models of Open-Pit Slopes Using UAVs: Flight Path Planning and Optimization

Aerial imaging of an open-pit mine integrated with the visual analytics offers a novel approach for routine monitoring of tension cracks for mine safety. Tension cracks may occur on work- or catch-benches that are excavated according to a computer aided design (CAD) model. The size of the tension cracks, their locations, and evolutions is commonly used to predict slope failures and to assure the mine safety operations. The goal of this research was to replace the current manual interventions with an automated platform for routine report generations for the mine controller. First, a drone was flown on a preprogrammed flight trajectory at a constant elevation to generate a mosaic and a depth map image. Next, work-, catch-benches, and access roads were automatically identified and represented by their medial axes. Subsequently, the waypoints from each medial axis were sequentially uploaded into the drone for scanning the corresponding regions at high-resolution. These high-resolution images were then used to delineate tension cracks. The delineation of tension cracks was performed using steerable filters, ENet, and UNet deep learning models for comparison. The ENet model, with the leave-one-out cross-validation method, produced the best performance profile with an Aggregated Jaccard Index and F1-Score of 0.51 and 0.79, respectively.

Topographically Guided UAV for Identifying Tension Cracks Using Image-Based Analytics in Open-Pit Mines

High-resolution modeling of open-pit slopes using UAV and photogrammetry

Enhancement of explosive energy distribution using UAVs and machine learning

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Papers

A Practical Methodology for Generating High-Resolution 3D Models of Open-Pit Slopes Using UAVs: Flight Path Planning and Optimization

Topographically Guided UAV for Identifying Tension Cracks Using Image-Based Analytics in Open-Pit Mines

High-resolution modeling of open-pit slopes using UAV and photogrammetry

Enhancement of explosive energy distribution using UAVs and machine learning