Adaptive Aiding of Human-Robot Teaming

Abstract

In many emerging civilian and military operations, human operators are increasingly being tasked to supervise multiple robotic uninhabited vehicles (UVs) with the support of automation. As 100% automation reliability cannot be assured, it is important to understand the effects of automation imperfection on performance. In addition, adaptive aiding may help counter any adverse effects of static (fixed) automation. Using a high-fidelity multi-UV simulation involving both air and ground vehicles, two experiments examined the effects of automation reliability and adaptive automation on human-system performance with different levels of task load. In Experiment 1, participants performed a reconnaissance mission while assisted with an automatic target recognition (ATR) system whose reliability was low, medium, or high. Overall human-robot team performance was higher than with either human or ATR performance alone. In Experiment 2, participants performed a similar reconnaissance mission with no ATR, static automation, or with adaptive automation keyed to task load. Participant trust and self-confidence were higher and workload was lower for adaptive automation compared with the other conditions. The results show that human-robot teams can benefit from imperfect static automation even in high task load conditions and that adaptive automation can provide additional benefits in trust and workload.