Legged Robots for Autonomous Inspection and Monitoring of Offshore Assets
- Milad Ramezani (Oxford Robotics Institute, University of Oxford) | Martim Brandao (Oxford Robotics Institute, University of Oxford) | Benoit Casseau (Oxford Robotics Institute, University of Oxford) | Ioannis Havoutis (Oxford Robotics Institute, University of Oxford) | Maurice Fallon (Oxford Robotics Institute, University of Oxford)
- Document ID
- Offshore Technology Conference
- Offshore Technology Conference, 4-7 May, Houston, Texas, USA
- Publication Date
- Document Type
- Conference Paper
- 2020. Offshore Technology Conference
- Localization and mapping, Robotics, Path planning, Inspection and monitoring, Locomotion
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- 97 since 2007
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Inspection and monitoring of assets are repetitive and expensive tasks and have higher risk when facilities are located offshore. Robotics holds the promise of improving the efficiency and safety of such platforms by allowing inspection and continuous remote monitoring of difficult-to-access facilities.
Legged robots, such as quadrupedal robots, are promising machines to achieve this goal: they have high maneuverability both indoors and outdoors, they are designed for accessing and navigating facilities that are built for humans (e.g. stairs, step-over piping, narrow passageways) and can carry a variety of sensors targeted at inspection and monitoring tasks.
In this paper we introduce our approach for autonomous inspection of oil & gas platforms using legged robots. Our approach is being developed as part of the ORCA Hub (Offshore Robotics for Certification of Assets), a UK robotics research hub. We envision a highly autonomous robotic system that conducts inspections with minimal intervention by human operators. The robot can navigate through facilities, as shown in Figure 1, accomplishing crucial tasks such as 3D mapping, monitoring of thermal build-up using thermal cameras, pressure sensing and also using color cameras to detect people and to carry out general visual inspection. We demonstrate and evaluate the system's perception, locomotion and inspection capabilities on a training facility that realistically simulates an oil rig at the Fire Service College, Moreton-in-Marsh, UK and an industrial area in the Offshore Renewable Energy Catapult Facility, Blyth, UK. We show the result of both autonomous and real-time teleoperated missions, and analyze the accuracy and efficiency of the system.
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