Urban Air mobility (UAM) promises reduced congestion on roads, reduced travel times and stronger overall efficiency in densely populated areas. However several challenges arise when wanting to implement UAM such as safety and social acceptance. The aim of this paper is to gain valuable insights how to implement safe and socially accepted UAM into society. Current regulations are discussed as well as X, Y and Z volumes in U-space, flight separations with ellipsoidal safety buffers, high speed corridors, landing separation at vertiports and airspace partition with voronoi diagrams are proposed and discussed. Social acceptance is addressed with some of the most prominent concerns e.g. safety, privacy and noise. Examples are set in Stockholm, Sweden, resulting in a maximum airspace occupation of 1 % which means 210 UAS (Unmanned Aircraft Systems) on each flight level. Sensitive areas and people with privacy concerns should have the option to opt-out of having their properties under the flight paths of UAM-vehicles. Concerns with UAM from the public has to be taken into great consideration for a successful implementation of UAM.

<p>The research effort addressed important gaps in the modeling to simulate Urban Air Mobility (UAM) operations and couple optimization analyses for vehicle design, fleet allocations, and operational choices for next generation urban travel. Urban Air Mobility is expected to be a \$1 trillion dollar industry by 2040, but operators and designers have limited models and tools to estimate fleet performance, cost metrics, emissions performance, and profit for a given concept under future concepts of operations. A review of the literature reveals 14 modeling gaps related to infrastructure, operations, airspace, vehicles, and customers. In addition, the UAM industry requires better understanding of how operational choices may impact vehicle design and fleet allocations in a market with significant economic barriers and infrastructure needs. To address those needs, this effort proposed alternatives to address modeling challenges and develop studies to evaluate UAM vehicle concepts and concepts of operations in ways once not possible using the enhanced modeling tools. The research findings revealed that modeling coupled design/fleet and operational choices can affect daily profitability potential by 2-4\times\, for piloted and autonomous operations and affect the fleet size from between 12-50 vehicles across small, medium, and large metropolitan areas. The modeling capability provided by the improvements in UAM operations simulations and accessing vehicle and fleet metrics enables future studies to address UAM in a holistic manner. The increased capability could benefit the UAM community and inform future operations and concepts of operations in preparation for ubiquitous operations.</p>

Small unmanned aircraft and electric vertical takeoff and landing (eVTOL) aircraft have recently emerged as vehicles able to perform new missions and stimulate future air transportation methods. This dissertation presents several system identification research advancements for these modern aircraft configurations enabling accurate mathematical model development for flight dynamics simulations based on wind-tunnel and flight-test data. The first part of the dissertation focuses on advances in flight-test system identification methods using small, fixed-wing, remotely-piloted, electric, propeller-driven aircraft. A generalized approach for flight dynamics model development for small fixed-wing aircraft from flight data is described and is followed by presentation of novel flight-test system identification applications, including: aero-propulsive model development for propeller aircraft and nonlinear dynamic model identification without mass properties. The second part of the dissertation builds on established fixed-wing and rotary-wing aircraft system identification methods to develop modeling strategies for transitioning, distributed propulsion, eVTOL aircraft. Novel wind-tunnel experiment designs and aero-propulsive modeling approaches are developed using a subscale, tandem tilt-wing, eVTOL aircraft, leveraging design of experiments and response surface methodology techniques. Additionally, a method applying orthogonal phase-optimized multisine input excitations to aircraft control effectors in wind-tunnel testing is developed to improve test efficiency and identified model utility. Finally, the culmination of this dissertation is synthesis of the techniques described throughout the document to form a flight-test system identification approach for eVTOL aircraft that is demonstrated using a high-fidelity flight dynamics simulation. The research findings highlighted throughout the dissertation constitute substantial progress in efficient empirical aircraft modeling strategies that are applicable to many current and future aeronautical vehicles enabling accurate flight simulation development, which can subsequently be used to foster advancement in many other pertinent technology areas. / Doctor of Philosophy / Small, electric-powered airplanes flown without an onboard pilot, as well as novel electric aircraft configurations with many propellers that operate at a wide range of speeds, referred to as electric vertical takeoff and landing (eVTOL) aircraft, have recently emerged as aeronautical vehicles able to perform new tasks for future airborne transportation methods. This dissertation presents several mathematical modeling research advancements for these modern aircraft that foster accurate description and prediction of their motion in flight. The mathematical models are developed from data collected in wind-tunnel tests that force air over a vehicle to simulate the aerodynamic forces in flight, as well as from data collected while flying the aircraft. The first part of the dissertation focuses on advances in mathematical modeling approaches using flight data collected from small traditional airplane configurations that are controlled by a pilot operating the vehicle from the ground. A generalized approach for mathematical model development for small airplanes from flight data is described and is followed by presentation of novel modeling applications, including: characterization of the coupled airframe and propulsion aerodynamics and model development when vehicle mass properties are not known. The second part of the dissertation builds on established airplane, helicopter, and multirotor mathematical modeling methods to develop strategies for characterization of the flight motion of eVTOL aircraft. Innovative data collection and modeling approaches using wind-tunnel testing are developed and applied to a subscale eVTOL aircraft with two tilting wings. Statistically rigorous experimentation strategies are employed to allow the effects of many individual controls and their interactions to be simultaneously distinguished while also allowing expeditious test execution and enhancement of the mathematical model prediction capability. Finally, techniques highlighted throughout the dissertation are combined to form a mathematical modeling approach for eVTOL aircraft using flight data, which is demonstrated using a realistic flight simulation. The research findings described throughout the dissertation constitute substantial progress in efficient aircraft modeling strategies that are applicable to many current and future vehicles enabling accurate flight simulator development, which can subsequently be used for many research applications.

<p>Unmanned aerial systems (UASs) are at the intersection of robotics and aerospace re-<br> search. Their rise in popularity spurred the growth of interest in urban air mobility (UAM)<br> across the world. UAM promises the next generation of transportation and logistics to be<br> handled by UASs that operate closer to where people live and work. Therefore safety and<br> security of UASs are paramount for UAM operations. Monitoring UAS traffic is especially<br> challenging in urban canyon environments where traditional radar systems used for air traffic<br> control (ATC) are limited by their line of sight (LOS).<br> This thesis explores the design and preliminary results of a target tracking system for<br> urban canyon environments based on a network of camera nodes. A network of stationary<br> camera nodes can be deployed on a large scale to overcome the LOS issue in radar systems<br> as well as cover considerable urban airspace. A camera node consists of a camera sensor, a<br> beacon, a real-time kinematic (RTK) global navigation satellite system (GNSS) receiver, and<br> an edge computing device. By leveraging high-precision RTK GNSS receivers and beacons,<br> an automatic calibration process of the proposed system is devised to simplify the time-<br> consuming and tedious calibration of a traditional camera network present in motion capture<br> (MoCap) systems. Through edge computing devices, the tracking system combines machine<br> learning techniques and motion detection as hybrid measurement modes for potential targets.<br> Then particle filters are used to estimate target tracks in real-time within the airspace from<br> measurements obtained by the camera nodes. Simulation in a 40m×40m×15m tracking<br> volume shows an estimation error within 0.5m when tracking multiple targets. Moreover,<br> a scaled down physical test with off-the-shelf camera hardware is able to achieve tracking<br> error within 0.3m on a micro-UAS in real time.</p>

This thesis presents a dedicated GPS-denied landing system designed for electric vertical takeoff and landing (eVTOL) aircraft. The system employs active fiducial light pattern localization (AFLPL), which provides highly accurate and reliable navigation during critical landing phases. AFLPL utilizes images of a constellation comprised of modulating infrared lights strategically positioned on the landing site, to determine the aircraft pose through the use of a perspective-n-point (PnP) solver. The AFLPL system underwent thorough development, enhancement, and implementation to address and demonstrate its potential in navigation and its inherent limitations. A proposed method addresses the limitations of AFLPL by using an extended Kalman filter (EKF) to fuse PnP camera pose estimates with sensor measurements from an inertial measurement unit (IMU), attitude heading reference system (AHRS), and optional global positioning system (GPS). The EKF estimation is reported to significantly enhance the accuracy, reliability, and update frequency of the aircraft state estimation. To refine and validate the AFLPL and EKF algorithms, a simulation was developed, consisting of an eVTOL executing a glideslope landing trajectory. Furthermore, a hardware system consisting of a multirotor and infrared light ground units was implemented to test these methods under real-world conditions. This research culminated in the successful demonstration of the AFLPL-based estimation system's efficacy through an autonomous, GPS-denied landing flight test, affirming its potential to improve the navigation and control of eVTOL aircraft lacking access to GPS information.

The first part of a study of passenger flight experience of Urban Air Mobility was completed. This first part included the design of different Urban Air Mobility vehicle models, in which the passenger flight experience would be quantitatively measured. A first version of a simulator setup, in which the measurements were performed, was also developed. Three concept vehicle models, a single main rotor, a side-by-side rotor and a quadrotor, were designed in the conceptual design software NDARC. The vehicles were electrically propelled with battery technology based on future technology predictions and were designed for autonomous flight with one passenger. The emissions of the vehicles were analyzed and compared with an existing turboshaft helicopter. The interface between NDARC and the flight dynamics analysis and control system software FlightCODE, which was used to create control systems to the NDARC models,  was developed to fit the vehicle configurations considered. The simulator setup was created with a VR headset, the flight simulation software X-Plane, an external autopilot software and stress sensors. Trial runs with the simulator setup were performed and gave important data for the continued development. Planned upgrades of the simulation station were presented and the continuation of the study was discussed.

Rising concerns related to the effects of traffic congestion have led to the search for alternative transportation solutions. Advances in battery technology have resulted in an increase of electric vehicles (EVs), which serve to reduce the impact of many of the negative consequences of congestion, including pollution and the cost of wasted fuel. Furthermore, the energy-efficiency and quiet operation of electric motors have made feasible concepts such as Urban Air Mobility (UAM), in which electric aircraft transport passengers in dense urban areas prone to severe traffic slowdowns. Electrified transportation may be the solution needed to combat urban gridlock, but many logistical questions related to the design and operation of the resultant transportation networks remain to be answered. This research begins by examining the near-term effects of EV charging networks. Stationary plug-in methods have been the traditional approach to recharge electric ground vehicles; however, dynamic charging technologies that can charge vehicles while they are in motion have recently been introduced that have the potential to eliminate the inconvenience of long charging wait times and the high cost of large batteries. Using an agent-based model verified with traffic data, different network designs incorporating these dynamic chargers are evaluated based on the predicted benefit to EV drivers. A genetic optimization is designed to optimally locate the chargers. Heavily-used highways are found to be much more effective than arterial roads as locations for these chargers, even when installation cost is taken into consideration. This work also explores the potential long-term effects of electrified transportation on urban congestion by examining the implementation of a UAM system. Interdependencies between potential electric air vehicle ranges and speeds are explored in conjunction with desired network structure and size in three different regions of the United States. A method is developed to take all these considerations into account, thus allowing for the creation of a network optimized for UAM operations when vehicle or topological constraints are present. Because the optimization problem is NP-hard, five heuristic algorithms are developed to find potential solutions with acceptable computation times, and are found to be within 10% of the optimal value for the test cases explored. The results from this exploration are used in a second agent-based transportation model that analyzes operational parameters associated with UAM networks, such as service strategy and dispatch frequency, in addition to the considerations associated with network design. General trends between the effectiveness of UAM networks and the various factors explored are identified and presented.

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The convergence of various technological and operational advancements has reinstated the interest in On-Demand Air Service (ODAS) as a viable mode of transportation. ODAS enables an end-user to be transported in an aircraft between their desired origin and destination at their preferred time without advance notice. Industry, academia, and the government organizations are collaborating to create technology solutions suited for large-scale implementation of this mode of transportation. Market studies suggest reducing vehicle operating cost per passenger as one of the biggest enablers of this market. To enable ODAS, an ODAS operator controls a fleet of aircraft that are deployed across a set of nodes (e.g., airports, vertiports) to satisfy end-user transportation requests. There is a gap in the literature for a tractable and online methodology that can enable ride-sharing in the on-demand operations while maintaining a publicly acceptable level of service (such as with low waiting time). The need for an approach that not only supports a dynamic-stochastic formulation but can also handle uncertainty with unknowable properties, drives me towards the field of Reinforcement Learning (RL). In this work, a novel two-layer hierarchical RL framework is proposed that can distribute a fleet of aircraft across a nodal network as well as perform real-time scheduling for an ODAS operator. The top layer of the framework - the Fleet Distributor - is modeled as a Partially Observable Markov Decision Process whereas the lower layer - the Trip Request Manager - is modeled as a Semi-Markov Decision Process. This framework is successfully demonstrated and assessed through various studies for a hypothetical ODAS operator in the Chicago region. This approach provides a new way of solving fleet distribution and scheduling problems in aviation. It also bridges the gap between the state-of-the-art RL advancements and node-based transportation network problems. Moreover, this work provides a non-proprietary approach to reasonably model ODAS operations that can be leveraged by researchers and policy makers.