Choice modeling of relook tasks for UAV search missions

Abstract

This paper addresses human decision-making in supervisory control of a team of unmanned vehicles performing search missions. Previous work has proposed the use of a two-alternative choice framework, in which operators declare the presence or absence of a target in an image. It has been suggested that relooking at a target at some later time can help operators improve the accuracy of their decisions but it is not well understood how - or how well - operators handle this relook task with multiple UAVs. This paper makes two novel contributions in developing a choice model for a search task with relooks. First, we extend a previously proposed queueing model of the human operator by developing a retrial queue model that formally includes relooks. Since real models may deviate from some of the theoretical assumptions made in the requeueing literature, we develop a Discrete Event Simulation (DES) that embeds operator models derived from previous experimental data and present new results in the predicted performance of multi-UAV visual search tasks with relook. Our simulation results suggest that while relooks can in fact improve detection accuracy and decrease mean search times per target, the overall fraction found correctly is extremely sensitive to increased relooks.