Online Data-Driven Safety Certification for Systems Subject to Unknown Disturbances

Deploying autonomous systems in safety critical settings necessitates methods to verify their safety properties. This is challenging because real-world systems may be subject to disturbances that affect their performance, but are unknown a priori. This work develops a safety-verification strategy wherein data is collected online and incorporated into a reachability analysis approach to check in real-time that the system avoids dangerous regions of the state space. Specifically, we employ an optimization-based moving horizon estimator (MHE) to characterize the disturbance affecting the system, which is incorporated into an online reachability calculation. Reachable sets are calculated using a computational graph analysis tool to predict the possible future states of the system and verify that they satisfy safety constraints. We include theoretical arguments proving our approach generates reachable sets that bound the future states of the system, as well as numerical results demonstrating how it can be used for safety verification. Finally, we present results from hardware experiments demonstrating our approach's ability to perform online reachability calculations for an unmanned surface vehicle subject to currents and actuator failures.Comment: 6 pages, 7 figure

Airway Mucin Concentration as a Marker of Chronic Bronchitis

Chronic obstructive pulmonary disease (COPD) is characterized by chronic bronchitic and emphysematous components. In one biophysical model, the concentration of mucin on the airway surfaces is hypothesized to be a key variable that controls mucus transport in healthy persons versus cessation of transport in persons with muco-obstructive lung diseases. Under this model, it is postulated that a high mucin concentration produces the sputum and disease progression that are characteristic of chronic bronchitis