Abstract
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.
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Data Availability
The code used for simulation will made available upon request. Due to the use of a commercial third-party library restrictions apply preventing sharing of the complete code. No data set was produced in this study beyond what is shown in the figures.
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Acknowledgements
We would like to thank ScoutDI for cooperation on the case study. Specifically we would like to thank Morten Fyhn Amundsen for discussion regarding the quantification and Kristian Klausen for general discussions on how the system works and potential failure causes.
Funding
Open access funding provided by NTNU Norwegian University of Science and Technology (incl St. Olavs Hospital - Trondheim University Hospital). The work is sponsored by the Research Council of Norway through the UNLOCK project, project number 274441, and through the Centre of Excellence funding scheme, project number 223254, AMOS.
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S. Rothmund and C. Thieme developed the method and case-study with supervision from I. Utne and T. Johansen. Software and simulations were done by S. Rothmund. The first draft of the manuscript was written by S. Rothmund. C. Thieme, I. Utne, and T. Johansen revised the manuscript. Funding acquisition and project management was performed by I. Utne and T. Johansen.
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T. Johansen is a shareholder and board member in ScoutDI. S. Rothmund has had a part-time position at ScoutDI. C. Thieme and I. Utne have no financial or non-financal interests to disclose.
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Rothmund, S.V., Thieme, C.A., Utne, I.B. et al. A Bayesian Approach to Risk-Based Autonomy, with Applications to Contact-Based Drone Inspections. J Intell Robot Syst 109, 31 (2023). https://doi.org/10.1007/s10846-023-01934-y
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DOI: https://doi.org/10.1007/s10846-023-01934-y