Adaptive Automation Effects on Operator Performance during a Reconnaissance Mission with an Unmanned Ground Vehicle

Abstract

We simulated a generic military crew station and examined the workload and performance of robotics operators when interacting with a ground robot in the two modes of robotic autonomy, teleoperation or semi-autonomous. We examined the effect of autonomy and invocation strategies on performance. The operator had either full teleoperation (manual) or semiautonomy (static) regardless of task load. In a third condition, the robots autonomy changed based on task load (adaptive). The operator had to identify hostile targets during the mission and maintain situation awareness (SA) of his local environment and the overall mission via a SA map. Results showed that when task load increased from low to high, participants' SA performance was better in the adaptive and static automation conditions than the manual condition; their threat detection performance degradation was less in manual and adaptive than in the static condition. On the other hand, when task load shifted from high to low, threat detection performance was better in the adaptive than the other two conditions.