Effects of System Automation Management Strategies and Multi-mission Operator-to-vehicle Ratio on Operator Performance in UAV Systems

It has been documented that the military intends to increase the number of Unmanned Aerial Vehicles (UAVs) in service while at the same time reducing the number of operators (Dixon et al. 2004). To meet this demand, many of the current UAV operator function will need to be automated. Levels of automation exist along a continuum from fully manual to fully automatic. Different automation strategies have been applied widely in UAV systems. Management by Consent (MBC), where the operator selects the task to be executed, and Management by Exception (MBE), where the computer selects the task to be executed are two proposed levels of automation for future UAV systems. Meanwhile, the optimum operator-to-vehicle ratio for future UAV systems is not yet known. It is expected that the optimum operator-to-vehicle ratio will vary with the level of automation applied to the system. Future UAV systems may require the use of adaptive automation to ensure maximum human–machine performance across varying operator-to-vehicle ratios. This study aims to help determine what levels of automation are most appropriate for different operator-to-vehicle ratios and how adaptive automation should be applied in future UAV systems. We investigated the effect of various operator-to-vehicle ratios and the two automation strategies on UAV mission tasks, results were analyzed using Analysis of Variance (ANOVA) and discussed in the last section of the paper.