This paper reports the Sentry autonomous underwater vehicle and its deployment on two cruises in response to the Deepwater Horizon oil spill. The first cruise, in June 2010, coupled Sentry with the TETHYS mass spectrometer to track and localize a subsea hydrocarbon plume at a depth of approximately 1100m going at least 30km from the oil spill site. In December 2010, Sentry mapped and photographed deep-sea biological communities for follow-up observations and sampling with the Alvin manned submersible. These cruises demonstrate how robots and novel sensing technologies contributed to the oil spill assessment and the broader impact of technologies developed for basic research.
With the purpose of delivering more robust systems, this paper revisits the problem of Inverse Uncertainty Quantification that is related to the discrepancy between the measured data at runtime (while the system executes) and the formal specification (i.e., a mathematical model) of the system under consideration, and the value calibration of unknown parameters in the model. We foster an approach to quantify and mitigate system uncertainty during the development cycle by combining Bayesian reasoning and online Model-based testing.
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