Abstract
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.
Highlights
Unparalleled augmentation has been witnessed in the field of Unmanned Aerial Vehicles (UAVs) in the past decade
Results showed that the proposed power consumption model and Genetic algorithms (GAs)-computed UAV flights consumed 50% less time and battery power as compared to normal parallel flights for both regions
These study results show that high-resolution imaging using UAVs in open-pit mines can be optimized using battery power consumption modeling and the proposed genetic algorithm
Summary
Unparalleled augmentation has been witnessed in the field of Unmanned Aerial Vehicles (UAVs) in the past decade. Other applications, such as designed vs actual survey of ramps and benches [5], detecting the height of safety berms, blast fragmentation analysis and determining the accuracy of drill holes [6], and vegetation [7], and surface moisture [8] monitoring have been presented in the literature Completing all of these tasks with aerial missions can result in significant benefits for mining operations in terms of time and resource utilization; available technologies for using drone images by application of artificial intelligence, computer vision, and machine learning algorithms have not yet been developed to their highest capacities. Capital and installation costs associated with an SSR system are unaffordable for small- to medium-scale mining operations [9] It seems that UAV imaging would be an ideal tool for automation of this process; tension cracks are usually narrow geometrical features that are not detectable on low resolution images usually taken from a constant, high altitude (around 400 feet) by popular drones currently on the market. The technology can be used as an early-warning system that alerts geotechnical engineers about the location of new cracks, and subsequently the potential for related failures so that necessary safety steps can be executed to avoid them [10]
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