Drone-Assisted Confined Space Inspection and Stockpile Volume Estimation

Alsayed, Ahmad; Yunusa-Kaltungo, Akilu; Quinn, Mark K.; Arvin, Farshad; Nabawy, Mostafa R.A.

Abstract

The accuracy of stockpile estimations is of immense criticality to process optimisation and overall financial decision making within manufacturing operations. Despite well-established correlations between inventory management and profitability, safe deployment of stockpile measurement and inspection activities remain challenging and labour-intensive. This is perhaps owing to a combination of size, shape irregularity as well as the health hazards of cement manufacturing raw materials and products. Through a combination of simulations and real-life assessment within a fully integrated cement plant, this study explores the potential of drones to safely enhance the accuracy of stockpile volume estimations. Different types of LiDAR sensors in combination with different flight trajectory options were fully assessed through simulation whilst mapping representative stockpiles placed in both open and fully confined areas. During the real-life assessment, a drone was equipped with GPS for localisation, in addition to a 1D LiDAR and a barometer for stockpile height estimation. The usefulness of the proposed approach was established based on mapping of a pile with unknown volume in an open area, as well as a pile with known volume within a semi-confined area. Visual inspection of the generated stockpile surface showed strong correlations with the actual pile within the open area, and the volume of the pile in the semi-confined area was accurately measured. Finally, a comparative analysis of cost and complexity of the proposed solution to several existing initiatives revealed its proficiency as a low-cost robotic system within confined spaces whereby visibility, air quality, humidity, and high temperature are unfavourable.

Citation

Alsayed, A., Yunusa-Kaltungo, A., Quinn, M. K., Arvin, F., & Nabawy, M. R. (2021). Drone-Assisted Confined Space Inspection and Stockpile Volume Estimation. Remote Sensing, 13(17), Article 3356. https://doi.org/10.3390/rs13173356

• Journal Article Type: Article
• Acceptance Date: Aug 18, 2021
• Online Publication Date: Aug 24, 2021
• Publication Date: 2021
• Deposit Date: May 27, 2022
• Journal: Remote Sensing
• Electronic ISSN: 2072-4292
• Publisher: MDPI
• Volume: 13
• Issue: 17
• Article Number: 3356
• DOI: https://doi.org/10.3390/rs13173356
• Keywords: drone; stockpile modelling; volume estimation; cement industry; confined space; process safety
• Public URL: https://durham-repository.worktribe.com/output/1202636