Julian Gutierrez PhD Proposal Review

Title: Towards Real-Time Safe Flight Paths for Urban Air Mobility

Committee Members:
Prof. David Kaeli, (Advisor)
Prof. Pau Closas
Dr. Evan Dill (NASA)
Dr. Natasha Neogi (NASA)

Abstract:
The emergence and development of advanced technologies and vehicle types have created a growing demand for introducing new forms of flight operations. These new and increasingly complex operational paradigms, such as Advanced and Urban Air Mobility (AAM/UAM), present regulatory authorities and the aviation community with the challenge of finding methods to integrate these emerging operations without significant additional risk to pedestrians and infrastructure. Predictive and autonomous risk mitigation capabilities become critical to meet this challenge. However, urban environments experience effects that are computationally expensive to model, limiting conventional aviation concepts, policy, and risk prediction tools from being effectively translated into this space. With the emergence of High-Performance Computing (HPC) ecosystems in the last two decades, we can use these software and hardware capabilities to help bridge the gap between real-time predictive responses and modeling accuracy.

In this dissertation we first present a simulation framework to estimate the quality of Global Navigation Satellite System (GNSS) performance for autonomous aircraft in urban environments. We propose a new algorithm designed for HPC to accelerate modeling the characteristic effects of dense urban canyons on GNSS, allowing the extension of established GNSS integrity techniques into urban navigation. Additionally, we provide a thorough validation of the simulator, which proves high-accuracy modeling when compared to sensors in the real world. Second, we use this simulation framework as the input into a new 4D path-planning algorithm based on an adaptation of the Bellman-Ford algorithm. HPC techniques are employed to accelerate the algorithm to produce flight paths that minimize exposure to GNSS risks. We evaluate the computational cost of satellite availability fluctuations by prioritizing events when satellite availability changes as triggers for these updates.