A Bayesian Approach to Risk-Based Autonomy, with Applications to Contact-Based Drone Inspections
Journal article, Peer reviewed
Published version
Permanent lenke
https://hdl.handle.net/11250/3118656Utgivelsesdato
2023Metadata
Vis full innførselSamlinger
- Institutt for marin teknikk [3448]
- Institutt for teknisk kybernetikk [3758]
- Publikasjoner fra CRIStin - NTNU [38484]
Sammendrag
Enabling higher levels of autonomy while ensuring safety 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 simulation 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.