Theoretical, computational, and experimental methods and materials developed in this work enhance situational awareness and enables autonomous risk-aware decision-making in unstructured and uncertain hazardous environments. UAS path planning will formulate and solve novel resource chance-constrained optimization problems. UAS will bypass computational heavy lifting to generate in-time micro-level local conditions by enabling physics-informed learning through Koopman operator theory. New sensor belief functions will be designed that accurately reflect sensing ignorance contained in hypotheses related to the fire environment. Evidential information fusion will effectively handle sensor epistemic uncertainty and allow reliable integration in an environment where not all data is trustworthy. Data-driven control will enable efficient and reliable operation of autonomous vehicles with uncertain dynamics in real time by using available knowledge of applied inputs and observed outputs, to learn the unknown inputs even without prior training data or persistent excitation. Real-time estimates of disturbance forces and torques acting on an UAS obtained by the disturbance observer will provide information on the turbulence and air flow around a wildland fire region.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.