The vision for Advanced Air Mobility (AAM) or formerly Urban Air Mobility (UAM) is to enable an air transportation system that moves people and cargo between places previously underserved by the current aviation market (local, regional, intraregional, urban) using revolutionary new electric vertical take-off and landing (eVTOL) aircraft. AAM has received significant attention from federal agencies. Companies around the globe are competing to build and test eVTOL aircraft to ensure the AAM will become an integral part of people?s daily life. The AAM has enormous economic potential and societal impact, but its success will depend on its ability to scale the operations to the expected high demand with safety guarantee.

This project lays the foundation of safe and scalable learning-based planning and control for autonomous air mobility. Concretely, the project will (i) focus on algorithmic advances of scalable multi-agent aircraft autonomy for real-time separation assurance to increase the airspace capacity; (ii) develop and integrate the online safety guard and offline adaptive stress testing model to provide safety enhancement for the multi-agent aircraft autonomy; (iii) design the collaborative traffic flow planning framework for flight operators and the airspace service provider to improve safety and efficiency when facing demand and capacity uncertainties on the AAM network; and (iv) integrate the developed models and algorithms to build an autonomous AAM ecosystem testbed to perform simulation/flight tests and system level validation. The multidisciplinary approach is based on multi-agent reinforcement learning, safe reinforcement learning, multi-agent stochastic game, and bi-level robust optimization. The proposed effort has transformative impacts to enable safe and scalable advanced air mobility. It could have impact in the way that other CPS tools are designed and implemented to support increasing autonomy and unmanned operations in civil aviation, autonomous cars/trucks, and robotics. The project has an integrated education plan in (i) student innovation competitions, teams and clubs; (ii) interdisciplinary curriculum development and improvement for AI and autonomy in aerospace; (iii) bringing industry experts to students in classroom; and (iv) international student research exchange. The project will engage elected officials and policy makers in AI and machine learning via podcast series, which will provide basic knowledge and insights on legal, ethical, and societal implications of AI. The project will establish a workforce pipeline from high school to postdoc for women in in aerospace via AI and computing.