ERI: Towards Safe Aviation Autonomy: A Risk-bounded Planning Framework for Dynamical Systems under Uncertainties

This award is funded in whole or in part under the American Rescue Plan Act of 2021 (Public Law 117-2).

This Engineering Research Initiation (ERI) award supports research to enable safe operations in the increasingly autonomous environment that comprises the national airspace system (NAS). Development and adoption of drone delivery systems, urban air mobility, and commercial space transportation make it critical to integrate these modes into the current NAS.

Unsegregated and efficient operations are very challenging when congested airspace is shared by high volume drone traffic and reserve aircraft hazard areas during space launches. Increasing autonomy in the NAS can help relieve congestion by increasing traffic density in limited airspace but demands high assurance of safe operations. This project will study risk-bounded planning and operations for the integration of these multiple air traffic modes and will provide the autonomous systems community with an underlying theory, set of models, and solution algorithms for risk-bounded planning in the dynamical and uncertain environment that comprises the NAS.

The project will develop the methodological foundations for risk-bounded planning of dynamical systems under uncertain environments. Within this framework, the project will study risk-bounded planning algorithms and a distributed computing approach to integrate autonomous operations within the NAS. The project will deliver three innovative techniques to support planning and operations in the NAS environment: (I) a data-driven method for efficient characterization of uncertainty with dynamical data; (II) a suite of risk-bounded planning algorithms based on chance constraints and convex approximation for dynamical systems under uncertainties; (III) a distributed computing framework with a multi-scale approach that can efficiently solve the chance-constrained models accounting for generic uncertainties.

These methods will be employed to develop risk-bounded planning algorithms and a dynamic, probabilistic geofence to support autonomous air traffic integration in the NAS. The project will support graduate students and enhance the graduate program at San Diego State University, a Hispanic Serving Higher Education Institution. The PI will also use a simulation platform to engage K-12 and undergraduate students, especially those from underrepresented groups, in the areas of aerospace engineering, information science, and artificial intelligence.

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.