Date of Award

3-2025

Document Type

Thesis

Degree Name

Master of Science in Operations Research

Department

Department of Operational Sciences

First Advisor

Lance E. Champagne, PhD

Abstract

Unmanned Aerial Vehicles (UAVs) have seen increased usage over the past two decades during the Global War on Terrorism (GWOT), operating in low-risk environments against dispersed enemies with minimal counter-drone capabilities. However, as the U.S. military shifts focus to Multi-Domain Operations (MDO) and Large Scale Combat Operations (LSCO), UAVs face significantly higher risks, including frequent and successful attacks, as well as the exploitation of their technology. Battle damage assessment (BDA) is not new; however, autonomous self-assessment by UAVs represents a novel advancement. Currently, UAV BDA relies on manual inspection, requiring approximately eight hours per drone. By adopting self-sensing technology, UAVs can autonomously determine damage status and decide whether to continue their mission or return to base. This capability aligns with the 2022 National Defense Strategy, enabling the U.S. Air Force and Department of Defense to better plan, resource, and synchronize operations in MDO and LSCO environments. This research evaluates the performance of UAVs with and without autonomous damage assessment capabilities using the Advanced Framework for Simulation, Integration, and Modeling (AFSIM). UAVs were tested against larger, equal, and smaller enemy forces. The results demonstrate that self-assessing UAVs improve survivability and lethality between 10 to 30%.

AFIT Designator

AFIT-ENS-MS-25-M-196

Comments

An embargo was observed for posting this work.

Distribution Statement A: Distribution Unlimited. Approved for public release. PA case number: 88ABW-2025-0252

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