Year: 2025
Type: article
Abstract: While many organizations believe that the human-to-robot (m:N) ratio is a key factor driving workload during operation of uncrewed aircraft systems, does this assumption hold 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 addresses this gap by modeling a supervisory task where a pilot-in-command supervised a large fleet of autonomous delivery drones in a large urban metro area. Across various scenarios, while the number of supervised aircraft had a small impact on workload, it appears that other task-based cognitive and visual demands underlied observed changes 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.
Authors: Vivek Mallampati, Joshua Bhagat Smith, Emily Burgess, Christopher A. Sanchez, Julie A. Adams
Institution: Oregon State University
Language: English
Subfield: Social Psychology
Field: Psychology
Domain: Social Sciences
Open Access status: bronze
OpenAlex ID: W4413909111