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A Bayesian Approach to Risk-Based Autonomy, with Applications to Contact-Based Drone Inspections

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posted on 04.05.2022, 21:45 by Sverre Velten RothmundSverre Velten Rothmund, Christoph Alexander Thieme, Ingrid Bouwer Utne, Tor Arne Johansen

Enabling higher levels of autonomy requires an increased ability to identify and handle internal faults and unforeseen changes in the environment. This article presents an approach to improve this ability for a robotic system executing a series of independent tasks by using a dynamic decision network (DDN). A case study of an industrial inspection drone performing contact-based inspection is used to demonstrate the capabilities of the resulting system. The case study demonstrates that the system is able to infer the presence of internal faults and the state of the environment by fusing information over time. This information is used to make risk-informed decisions enabling the system to proactively avoid failure and to minimize the consequence of faults. Lastly, the case study demonstrates that evaluating past states with new information enables the system to identify and counteract previous sub-optimal actions. 

Funding

Unlocking the potential of autonomous systems and operations through supervisory risk control

The Research Council of Norway

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Centre for Autonomous Marine Operations and Systems (AMOS)

The Research Council of Norway

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History

Email Address of Submitting Author

sverre.v.rothmund@ntnu.no

ORCID of Submitting Author

0000-0002-7659-7881

Submitting Author's Institution

Norwegian University of Science and Technology

Submitting Author's Country

Norway