Abstract

The United States Air Force (USAF) has increasing needs for unmanned aerial vehicle (UAV) operators. Automation may enable a single operator to manage multiple UAVs at the same time. Multi-UAV operation may require a unique set of skills and the need for new operators calls for targeting new populations for recruitment. The objective of this research is to develop a simulation environment for studying the role of individual differences in UAV operation under different task configurations and investigate predictors of performance and stress. Primarily, the study examined the impact of levels of automation (LOAs), as well as task demands, on task performance, stress and operator reliance on automation. Two intermediate LOAs were employed for two surveillance tasks included in the simulation of UAV operation. Task demand was manipulated via the high and low frequency of events associated with additional tasks included in the simulation. The task demand and LOA manipulations influenced task performance generally as expected. The task demand manipulations elicited higher subjective distress and workload. LOAs did not affect operator workload but affected reliance behavior. Also, this study examined the role of individual differences in simulated UAV operation. A variety of individual difference factors were associated with task performance and with subjective stress response. Video gaming experience was linked to lower distress and better performance, suggesting possible transfer of skills. Some gender differences were revealed in stress response, task performance, but all the gender effects became insignificant with gaming experience controlled. Generally, the effects of personality were consistent with previous studies, except some novel findings with the performance metrics. Additionally, task demand was found to moderate the influence of personality factors on stress response and performance metrics. Specifically, conscientiousness was associated with higher subjective engagement and performance when demands were higher. This study supports future research which aims to improve the dynamic interfaces in UAV operation, optimize operator reliance on automation, and identify individuals with the highest aptitude for multi-UAV control.

Graduation Date

2017

Semester

Spring

Advisor

Matthews, Gerald

Degree

Doctor of Philosophy (Ph.D.)

College

College of Sciences

Degree Program

Modeling & Simulation

Format

application/pdf

Identifier

CFE0006951

URL

http://purl.fcla.edu/fcla/etd/CFE0006951

Language

English

Release Date

November 2017

Length of Campus-only Access

None

Access Status

Doctoral Dissertation (Open Access)

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