Auditory Decision Aiding in Supervisory Control of Multiple Unmanned Aerial Vehicles

Abstract

This paper investigates the effectiveness of sonification, continuous auditory alert mapped to the state of a monitored task, in supporting unmanned aerial vehicle (UAV) supervisory control. Background: UAV supervisory control requires monitoring each UAV across multiple tasks (e.g., course maintenance) via a predominantly visual display, which currently is supported with discrete auditory alerts. Sonification has been shown to enhance monitoring performance in domains such as anesthesiology by allowing an operator to immediately determine an entity's (e.g., patient) current and projected states, and is a promising alternative to discrete alerts in UAV control. However, minimal research compares sonification to discrete alerts, and no research assesses the effectiveness of sonification for monitoring multiple entities (e.g., multiple UAVs). Method: An experiment was conducted with 39 military personnel, using a simulated setup. Participants controlled single and multiple UAVs, and received sonifications or discrete alerts based on UAV course deviations and late target arrivals. Results: Regardless of the number of UAVs supervised, the course deviation sonification resulted in 1.9 s faster reactions to course deviations, a 19% enhancement from discrete alerts. However, course deviation sonification interfered with the effectiveness of discrete late arrival alerts in general, and with operator response to late arrivals when supervising multiple vehicles. Conclusions: Sonifications can outperform discrete alerts when designed to aid operators to predict future states of monitored tasks. However, sonifications may mask other auditory alerts, and interfere with other monitoring tasks that require divided attention.