Operator performance and intelligent aiding in unmanned aerial vehicle scheduling

An iterative design cycle to support intelligent, predictive aiding together with human judgment and pattern recognition to maximize both system and human performance in the supervision of unmanned aerial vehicles (UAV) is presented. To provide this intelligent aiding, a multi-aerial unmanned vehicle experiment (MAUVE) interface is developed that lets an operator supervise the independent UAVs simultaneously and intervene as the situation requires. The MAUVE UAVs can perform six high level actions including traveling to targets, moving slowly at specific locations, arming payloads, firing payloads, assessing battle damage, and returning to base. The MAUVE interface's right side consists of a UAV status window, chat box, UAV health, status updates, and decision support window, which provides operator decision support. Decision support aims to simplify a priori mission planning information and provides a schedule of events and resource allocation for the prespecified mission.