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. To reduce irrelevant information, an intelligent filtering algorithm was developed. Here we evaluate its effectiveness in reducing operators' cognitive workloads. METHOD: Two-stage operational scenarios were conducted with professional ex-military UAV operators, using two filter protocols and a no-filter control. High-end real-time techniques were used to continuously assess workload from muscle behavior and machine learning models. RESULTS: Lower cognitive workload was found when applying the algorithm's protocols, especially when fatigue started to accumulate (Stage II). However, concerns about the quality of SA arose. CONCLUSION: The algorithm was positively evaluated for its ability to reduce operators' cognitive workloads. More evaluations of operators' SA are required. APPLICATION: The algorithm demonstrates the possibility of integrating AI to improve human performance in complex systems, and can be applied to other domains where spatial-temporal information needs to be contextually filtered in real time.

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. This study uses cognitive work analysis (CWA) to describe the operational work of the mission operator of a Skylark miniature UAS system. Three CWA phases were conducted - work domain analysis, control task analysis and strategy analysis - providing a rich framework of operational mission phases, task components, processes and the physical interface-objects in use. These representations highlight the operators' extensive use of the C2 map during all mission phases, for all object-related processes. To further enhance the outcomes of the CWA, and prior to outlining specific design requirements, an empirical investigation was conducted in which the eye movements of five experienced operators were obtained during a simulated mission. The empirical results confirm and further specify the work patterns that operators adopt. Quantitative analysis shows operators' extensive focus on the map, especially during mission-critical phases. These analyses led to the conclusion that a significant change in the way operators interact with the C2 map, or alternative designs to enhance map-based information utilization, should be applied. Insights drawn from this analysis can be applied to other aerial surveillance work domains, and adding empirical evaluations is helpful to further refine and reinforce the CWA outcomes.