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

Details

• Personal Author:
  Abbasi B ; Battulwar R ; Naghadehi MZ ; Parvin B ; Peik B ; Sattarvand J ; Valencia J ; Winkelmaier G
• Description:
  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. [Description provided by NIOSH]
• Subjects:
  Occupational Health Safety Software
• Keywords:
  Author Keywords: Open-pit Slope Monitoring Computer Software Digital Elevation Map Genetic Algorithm Ground Monitoring High-resolution Imaging Imaging Techniques Mining Engineering Open Pit Mining Path Optimization Photogrammetry Remote Control Sensors Unmanned Aerial Vehicle

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• ISSN:
  2072-4292
• Document Type:
  Text
• Funding:
  Contract
• Genre:
  Journal Article
• Place as Subject:
  Nevada ; OSHA Region 9
• CIO:
  National Institute for Occupational Safety and Health (NIOSH)
• Topic:
  Workplace Safety & Health
• Location:
  North America
• Volume:
  12
• Issue:
  14
• NIOSHTIC Number:
  nn:20068349
• Citation:
  Remote Sens 2020 Jul; 12(14):2283
• Contact Point Address:
  Rushikesh Battulwar, Department of Mining and Metallurgical Engineering, Mackay School of Earth Sciences and Engineering, University of Nevada, Reno, NV 89557, USA
• Email:
  rbattulwar@nevada.unr.edu
• Federal Fiscal Year:
  2020
• Performing Organization:
  University of Nevada, Reno
• Peer Reviewed:
  True
• Start Date:
  20170828
• Source Full Name:
  Remote Sensing
• End Date:
  20190831
• Collection(s):
  National Institute for Occupational Safety and Health
• Main Document Checksum:
  urn:sha-512:34f670a6ae7f0bfea65dc356e39f57eca320837d4f6b3349eac4b1e1844cba27ec53c820e11a6bbf2c3d634db7a743d4f32703fce11d50217b1fc2b2abf4ece1
• Download URL:
  https://stacks.cdc.gov/view/cdc/230515/cdc_230515_DS1.pdf
• File Type:
  [PDF - 8.34 MB ]