The Effect of Task Based Automation for the Control of Unmanned Systems on Operator Performance

Robotic technology will be a vital component of future combat. However, the combination of robotic operational tasks with other traditional military tasks will create high workload peaks during military operations. The objective of this research is to develop and evaluate flexible automation strategies to aid the operator in this complex military environment. In this experiment we evaluated the effect of an automation that was invoked based on task load. Participants conducted a military reconnaissance mission using a simulation that required them to: use an unmanned air vehicle sensor for target detection, monitor an unmanned ground vehicle, and respond to multi-level communications. Participants completed sixteen missions in the environment, during which task load and automation were manipulated. The results of this experiment showed that operator performance did improve when the automation, an aided target recognition for the unmanned air vehicle, was invoked relative to when it was not invoked. Further, when automation was appropriately applied (high task load conditions) workload decreased significantly. This data along with the results of other experiments discussed in this paper indicate that adaptive automation may be a useful mitigation strategy to help offset the potential deleterious effects of high cognitive load on Army robotic operators in a multitasking environment.