Increasing resilience of robotic systems through quickest change detection technology

Future robotics systems are likely to benefit from having an ability to self-diagnose self-failure or the presence of anomalous situations (so that they can switch to fallback or fail-safe modes). Example situations include subtle sensor or actuator failure and cyber security or physical intruder detection.

Such low signal-to-noise anomaly detection or self-diagnose problems can be understood using powerful mathematical and statistical tools which QCR has a rich history of advancing through collaboration with industry partners and publication in premium international venues.

These PhD projects suit students interested in working on novel statistical and information theory tools. Potentially involves learning about dynamic programming on infinite dimensional spaces to understanding challenging optimal stopping problems (a type of decision problem), and practical implementation of non-linear filtering computations.

Chief Investigators