Presentation
Workload Modeling for 1:N UAS Delivery Operations
SessionPoster Session 2
DescriptionWhile many organizations believe that the human-to-robot (m:N) ratio is a key factor driving workload during operation of uncrewed aircraft systems, there is some question about whether this assumption holds for the operation of the highly autonomous systems often deployed across industry today. Unfortunately, there is a lack of robust conceptual workload models that capture human performance while interacting with these highly autonomous systems. The current study attempts to address this gap by modeling a supervisory task in which a pilot-in-command supervised a large fleet of autonomous delivery drones in a large urban metro area. Modeling was implemented using IMPRINT Pro, a modeling software used broadly in the United States Army. Overall, and across various scenarios, while the number of supervised aircraft had a trivial-to-small positive impact on workload, it appears that other cognitive and visual demands inherent with the task scenarios themselves instead appear to drive a majority of the observed change in workload. These modeling efforts provide an initial baseline dataset, derived within a real-world context, to help guide subsequent investigations into what factors influence perceived workload during the operation of automated uncrewed aircraft.
Event Type
Poster
TimeWednesday, October 15th5:30pm - 6:30pm CDT
LocationRiverside East