Facilitating the Work of Unmanned Aerial Vehicle Operators Using Artificial Intelligence: An Intelligent Filter for Command-and-Control Maps to Reduce Cognitive Workload.

Objective: Evaluating the ability of a Gibsonian-inspired artificial intelligence (AI) algorithm to reduce the cognitive workloads of military Unmanned Aerial Vehicle (UAV) operators.

Background: Military UAV operators use the command-and-control (C2) map for developing mission-relevant situation awareness (SA). Yet C2 maps are overloaded with information, mostly irrelevant to the mission, causing operators to neglect the map altogether.

Combining cognitive work analysis and empirical evaluations to understand map use by operators of small carry-on unmanned aerial systems.

Operating a small carry-on unmanned aerial system (UAS) alone is challenging. Research on facilitating single-operator work has focused mainly on payload operation and health monitoring. Little focus has been given to mission-related aspects and how the command and control (C2) map display contributes to mission accomplishment.