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Enhancing Safety, Efficiency, and Resilience in Advanced Air Mobility Through Geofencing, Contingency Landing Management, and Optimized Network Strategies.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Enhancing Safety, Efficiency, and Resilience in Advanced Air Mobility Through Geofencing, Contingency Landing Management, and Optimized Network Strategies./
作者:	Kim, Joseph.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2024,
面頁冊數:	180 p.
附註:	Source: Dissertations Abstracts International, Volume: 85-12, Section: A.
Contained By:	Dissertations Abstracts International85-12A.
標題:	Aerospace engineering. -
電子資源:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=31349065
ISBN:	9798382740720

Enhancing Safety, Efficiency, and Resilience in Advanced Air Mobility Through Geofencing, Contingency Landing Management, and Optimized Network Strategies.
Kim, Joseph.

Enhancing Safety, Efficiency, and Resilience in Advanced Air Mobility Through Geofencing, Contingency Landing Management, and Optimized Network Strategies. - Ann Arbor : ProQuest Dissertations & Theses, 2024 - 180 p.

Source: Dissertations Abstracts International, Volume: 85-12, Section: A.

Thesis (Ph.D.)--University of Michigan, 2024.

Advanced Air Mobility (AAM) is a rapidly emerging sector of the aerospace industry with the potential to significantly improve the transportation system. Its benefits include reduced congestion and travel time, environmentally friendly low-emission operations, and the stimulation of economic growth through new infrastructure. This dissertation offers novel solutions for individual vehicle management, contingency planning, and optimal AAM traffic network management. The first contribution is a method to generate flight plans with geofencing for low-altitude urban airspace. We create conflict-free geofence volumes that wrap flight routes using computational geometry and polygon decimation. Visibility graph with constant altitude versus terrain following paths are compared by a weighted cost summing distance and energy. By generating conflict-free flight paths for a single vehicle and strategically deconflicting multiple vehicles, the proposed algorithms offer route flexibility relative to fixed corridor solutions. The second contribution is statistically-guided airspace geofence volume sizing. The optimal geofence sizing is critical for the scalability of AAM operations. If too large, airspace will be wasted; if too small, we risk AAM violating geofences due to uncertainties. We propose a method to determine geofence buffer sizes based on vehicle dynamics, statistical sensor errors, and urban wind models specified by computational fluid dynamics. This method generates statistically-guided geofence sizes with a three-sigma safety level with a fixed-wing Uncrewed Aircraft System (UAS) scaled up to an AAM vehicle. The findings provide insights into optimizing geofence volumes for different environmental conditions and vehicle types. The third contribution is an extension of two-dimensional polygon geofences to polyhedron geometries for climb, descent, and UAS swarms. We first define and analyze a parallelepiped geofence with top and bottom surface slopes matching the flight path angle. Next, we define a convex hull swarm containment geofencing algorithm and showed how much airspace volume is saved compared to single vehicle geofencing solutions. The fourth contribution is the development of assured contingency landing management (ACLM) for AAM. The ACLM architecture enables distressed AAM flights to quickly determine safe landing options, thereby enhancing overall safety in AAM operations. ACLM employs mathematically-provable controllability and reachability logic, and integrates pre-analyzed prepared and unprepared landing sites and plan databases to maximize response efficiency. A multicopter case study uses ACLM to safely land when motor and/or battery failures occur. Multicopter landing options are analyzed as a function of vehicle weight, and parallel threading enables ACLM to execute in milliseconds. The fifth contribution is AAM traffic management optimization. By strategically sectorizing urban airspace and modeling AAM routes, we formulate centralized management with vehicle, infrastructure, and operation constraints. Then, distributed network management is formulated as bi-level optimization using cooperative game theory and mixed integer programming. The research offers valuable insights into scalable AAM network management strategies by comparing centralized and distributed AAM network managements. In summary, this thesis contributes to the safety, efficiency, and resilience of AAM. It promotes safety with solutions for low-altitude flight planning through geofencing and assured contingency landing management for distressed vehicles. It improves efficiency by determining optimal geofence geometries and sizes for AAM flights. It enhances AAM operational resilience with strategically optimal traffic management solutions that adapt to time-dependent infrastructural and operational constraints, as well as shifting demands in regional airspace congestion. Research insights pave the way for scalable and efficient AAM and urban airspace management.

ISBN: 9798382740720Subjects--Topical Terms:

1002622
Aerospace engineering.
Subjects--Index Terms:

Advanced Air Mobility

Enhancing Safety, Efficiency, and Resilience in Advanced Air Mobility Through Geofencing, Contingency Landing Management, and Optimized Network Strategies.
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590 $a School code: 0127.
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650 4 $a Transportation. $3 555912
650 4 $a Information science. $3 554358
650 4 $a Robotics. $3 519753
653 $a Advanced Air Mobility
653 $a Geofencing
653 $a Conflict-free flight paths
653 $a Urban Air Mobility
653 $a Environmental conditions
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690 $a 0709
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710 2 $a University of Michigan. $b Robotics. $3 3546383
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793 $a English
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