Date of Award

Summer 2012

Access Type

Thesis - Open Access

Degree Name

Master of Science in Human Factors & Systems


Human Factors and Systems

Committee Chair

Kelly Neville, Ph.D.

First Committee Member

Dahai Liu, Ph.D.

Second Committee Member

Richard Stansbury, Ph.D.


An important characteristic of UASs is lag because it can become a considerable challenge to successful human-in-the-loop control. As such, UASs are designed and configured to minimize system lag, though this can increase acquisition and operation costs considerably. In an effort to cut costs, an organization may choose to accept greater risk and deploy a UAS with high system lag. Before this risk can be responsibly accepted, it must be quantified.

While many studies have examined system lag, very few have been able to quantify the risk that various levels of lag pose to an internally piloted, manually landed UAS. This study attempted to do so by evaluating pilot landing performance in a simulator with 0 ms, 240 ms, and 1000 ms of additional lag. Various measures were used, including a novel coding technique.

Results indicated that 1000 ms of lag was unsafe by all measures. They also indicate that 240 ms of lag degrades performance, but participants were able to successfully land the simulated aircraft. This study showed the utility of using several measures to evaluate the effect of lag on landing performance and it helped demonstrate that while 1000 ms poses a high risk, 240 ms of lag may be a much more manageable risk.

Future research suggested by this research includes: investigating lag between 240 ms and 1000 ms, introducing different weather phenomena, developing system lag training techniques for operators, and investigating the effect of aides such as predictive displays and autopilot-assisted recovery.