Ryan, Philippa Mary orcid.org/0000-0003-1307-5207, Badyal, Arjun, Sze, Samual et al. (4 more authors) (2024) Safety assurance challenges for autonomous drones in Underground Mining Environments. In: Huda, M.N., Wang, M. and Kalganova, T., (eds.) Towards Autonomous Robotic Systems. TAROS 2024. 25th Towards Autonomous Robotic Systems Conference, 21-23 Aug 2024 Springer, pp. 169-181.
Abstract
Autonomous drones have been proposed for many industrial inspection roles including building infrastructure, nuclear plants and mining. They have the benefit of accessing hazardous locations, without exposing human operators and other personnel to physical risk. Underground mines are extremely challenging for autonomous drones as there is limited infrastructure for Simultaneous Localisation and Mapping (SLAM), for the drone to navigate. For example, there is no Global Navigation Satellite System (GNSS), poor lighting, and few distinguishing landmarks. Additionally, the physical environment is extremely harsh, affecting the reliability of the drone. This paper describes the impact of these challenges in designing for, and assuring, safety. We illustrate with experience from developing an autonomous Return To Home (RTH) function for an inspection drone. This is initiated when the drone suffers a communications loss whilst surveying newly excavated corridors that are unsafe for personnel. We present some of the key safety assurance challenges we faced, including design constraints and difficulties using simulations for validation and verification.
Metadata
| Item Type: | Proceedings Paper |
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| Authors/Creators: |
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| Editors: |
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| Copyright, Publisher and Additional Information: | This is an author-produced version of the published paper. Uploaded in accordance with the University’s Research Publications and Open Access policy. |
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| Institution: | The University of York |
| Academic Units: | The University of York > Faculty of Sciences (York) > Computer Science (York) |
| Date Deposited: | 27 Feb 2026 14:00 |
| Last Modified: | 27 Feb 2026 14:00 |
| Published Version: | https://doi.org/10.1007/978-3-031-72059-8_15 |
| Status: | Published |
| Publisher: | Springer |
| Identification Number: | 10.1007/978-3-031-72059-8_15 |
| Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:238452 |

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