Adjustable Autonomy for Human-UAVs Collaborative Searching Using Fuzzy Cognitive Maps

Collaborating with a team of UAVs remains a challenge for a human operator in the target searching task where the operator has to simultaneously monitor several video streams to search and verify the targets. In this paper, we propose an adjustable autonomy method that can dynamically change the levels of autonomy (LOA) for the UAVs according to the task complexity as well as the operator's cognitive workload. Specifically, we use a fuzzy cognitive map (FCM) to construct the framework of autonomy levels for unmanned systems (ALFUS). We have shown the effectiveness of the proposed method in an experiment that one human collaborates with four UAVs in a target searching task.