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Virginia Tech

Quantitative Approach and Departure Risk Assessment for Unmanned Aerial Systems

Abstract

dc:description.abstract

As the use of Unmanned Aerial Systems (UAS) becomes more common in both civilian/commercial and military applications, so too has the risk of injury to individuals and third parties on the ground. The purpose of this research is to further enhance methods currently in use for performing flight path risk assessment for UAS, as well as improve upon an existing software tool: Quantitative Approach and Departure Risk Assessment (QUADRA). The primary focus is upon the incorporation of building information to determine the protection offered to sheltered populations, reevaluate the probability of fatality models used in aircraft failures to more accurately determine the risk for smaller UAS systems, and to provide a metric for determining the number of individuals that are adversely affected by the noise of the autonomous system as it performs its mission.

Degree

thesis:*
Name thesis:degree_name
Master of Science
Level thesis:degree_level
masters
Discipline thesis:degree_discipline
Aerospace Engineering
Department dc:contributor.department
Aerospace and Ocean Engineering
Grantor dc:publisher
Virginia Tech
Year dc:date.issued
2023

Author and committee

dc:creator, dc:contributor.*
Author dc:creator
  • Adie, Dylan S.
Chair dc:contributor.committeechair
  • Canfield, Robert Arthur
Committee members dc:contributor.committeemember
  • Briggs, Robert Clayton
  • Woolsey, Craig A.

Subjects

dc:subject × 1

Rights

dc:rights
Statement dc:rights
  • In Copyright
Language dc:language.iso
en

Identifiers

dc:identifier.*
Dc Identifier Other
vt_gsexam:36319
OAI identifier oai:identifier
oai:vtechworks.lib.vt.edu:10919/113764

Chain of custody

source
Harvested from
Virginia Tech
Base URL
vtechworks.lib.vt.edu/oai/request
Last updated
2026-07-22
Source record
OAI-PMH GetRecord
citation

Adie, Dylan S.. Quantitative Approach and Departure Risk Assessment for Unmanned Aerial Systems. masters thesis, Virginia Tech, 2023. http://hdl.handle.net/10919/113764