Operator Performance and Intelligent Aiding in Unmanned Aerial Vehicle Scheduling

Unmanned vehicles (UVs) are quickly becoming ubiquitous in almost every aspect of hostile-environment operations. A key challenge in designing futuristic one-controlling-many systems will be minimizing periods of excessive operator workload that can arise when critical tasks for several UVs occur simultaneously. To a certain degree, you can predict and mitigate such periods in advance. However, actions that mitigate a particular period of high workload in the short term might create long-term episodes of high workload that were previously nonexistent. So, we need decision support that helps an operator evaluate alternative actions for managing a mission schedule in real time. To this end, we present an iterative design cycle that tries to leverage intelligent, predictive aiding together with human judgment and pattern recognition to maximize both system and human performance in the supervision of four UAVs. Automated decision support tools that provide more local, as opposed to global, visual recommendations can produce better performance in multiple UAV scheduling