This project seeks to enhance air traffic safety and efficiency by developing predictive algorithms for the integration of Unmanned Aircraft Systems (UAS) and manned aircraft near airports. As air traffic increases, the interaction between manned and unmanned aircraft becomes a pivotal challenge, especially during landing phases. Our project will create mathematical models and algorithms that forecast the behaviors of these aircraft under various operational scenarios, thus preventing potential conflicts and optimizing flight patterns. We will focus on the theoretical aspects of motion planning, drawing from the established “rules of the road” in aviation to formulate predictive behaviors. These algorithms will be implemented in simulation environments to test their efficacy in real-time airspace management, comparing their performance against existing traffic control methods. This research has the potential to improve safety margins and to increase the throughput of aircraft landings, significantly enhancing operational efficiency at congested airports.

This project aligns with the NASA Mission Directorate in several areas: By developing predictive algorithms that integrate UAS and manned aircraft operations near airports, we optimize flight patterns and increase landing throughput, addressing ARMD’s goal of enhancing air traffic management systems. We create models to predict and manage interactions between aircraft during critical landing phases, aiming to prevent conflicts and enhance safety. Our research on motion planning for UAS, tested in simulations, aligns with ARMD’s efforts to ensure safe UAS operations alongside manned aircraft. Weekly meetings with NASA Langley foster innovation and ensure our research aligns with advanced aviation technology developments.