EAGER: TaskDCL: Enhancing Airport Safety and Efficiency with Integrated Cognition-aware UAS-Manned Aircraft Motion Planning

This EArly-concept Grants for Exploratory Research (EAGER) award supports research to address the challenge of integrating unmanned aircraft systems (UAS) into the congested airspace near airports. The research will focus on developing advanced motion planning algorithms to coordinate the navigation and control of multiple manned and unmanned aircraft during landing sequences, specifically targeting the initial approach and holding pattern navigation.

A key aspect of this research is to enhance pilot comfort by managing cognitive load during the critical phase of integrating UAS with manned aircraft. This includes ensuring pilots maintain high situational awareness without excessive stress due to UAS proximity. Additionally, the project seeks to increase operational efficiency, which can be measured by the number of aircraft landings per time unit or reduced wait times for landing clearances.

The ultimate goal is to develop a landing strategy for mixed traffic that demonstrates higher efficiency than scenarios involving only manned aircraft.

To achieve these objectives, the project will focus on three main areas of research: developing a numerical solver for optimal control, mathematical modeling of informal navigation rules, and integrating cognitive state analysis into UAS motion planning. The numerical solver aims to manage complex cost functions and constraints in densely pilot-populated UAS environments near airports.

The mathematical modeling will translate the informal rules that guide pilot behavior into algorithms that enable UAS to mimic and anticipate these behaviors. Integrating cognitive state analysis into UAS motion planning involves evaluating pilots’ cognitive states through physiological measures and using this data to refine UAS navigation strategies.

The anticipated outcomes include the integration of UAS into airspace near airports, significantly benefiting air traffic management, national security, and smart transportation systems. This project also aims to foster innovation in autonomous systems research and education, providing a multidisciplinary research platform for students and contributing to the broader acceptance and utilization of UAS technologies.

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.