Abstract
Project nautilUS is a one-million-pound Innovate UK-funded project, which has managed to develop a small, intrinsically safe and potentially ATEX-certified robotic system in order to carry out in-service inspection on above-ground storage tanks. Storage tanks are subject to corrosion over time with potential environmental consequences and it is expected that application of this robotic technology contributes to the prevention of corrosion more efficiently and with less risk considering the significant cost and health & safety risks associated with manual inspection of these tanks.
The current nautilUS robotic system is semi-autonomous. It has the capability of moving around a tank floor and making measurements of the floor thinning using an ultrasound probe attached to it. The measurements along with location data will then be recorded for post-processing after the robot is retrieved. However, this system is still dependent upon human/technician involvement, particularly when it comes to navigating the robot while it is inside the tank and also analysis of floor thinning data once the data are collected and stored.
The Project nautilUS + is currently at the proposal stage and it aims at focusing on the "smart" element. The initial evaluations show that decision-making (DM) techniques and machine learning (ML) algorithms can be used together innovatively in order to contribute to the intelligence of the nautilUS robotic system with the potential of making it fully autonomous.
The current nautilUS robotic system is semi-autonomous. It has the capability of moving around a tank floor and making measurements of the floor thinning using an ultrasound probe attached to it. The measurements along with location data will then be recorded for post-processing after the robot is retrieved. However, this system is still dependent upon human/technician involvement, particularly when it comes to navigating the robot while it is inside the tank and also analysis of floor thinning data once the data are collected and stored.
The Project nautilUS + is currently at the proposal stage and it aims at focusing on the "smart" element. The initial evaluations show that decision-making (DM) techniques and machine learning (ML) algorithms can be used together innovatively in order to contribute to the intelligence of the nautilUS robotic system with the potential of making it fully autonomous.
| Original language | English |
|---|---|
| Publication status | Published - 10 Jun 2022 |
| Event | The 18th International Conference on Condition Monitoring and Asset Management (BiNDT - CM2022) - Radisson Hotel and Conference Centre, Heathrow , London, United Kingdom Duration: 7 Jun 2022 → 9 Jun 2022 https://www.bindt.org/events/cm-2022/programme/ |
Conference
| Conference | The 18th International Conference on Condition Monitoring and Asset Management (BiNDT - CM2022) |
|---|---|
| Country/Territory | United Kingdom |
| City | London |
| Period | 7/06/22 → 9/06/22 |
| Internet address |
Keywords
- Above Storage Tanks (AST)
- Maintenance Engineering and Asset Management
- Asset Integrity Management
- Fully-autonomous Robotic Technology for Asset Inspection
- Machine Learning (ML)
- Decision Making (DM)
