Assessing Operator Workload and Performance in Expeditionary Multiple Unmanned Vehicle Control

Assessing operator strategies for real-time replanning of multiple unmanned vehicles

“…7(c). These findings support previous experimental results [6] showing that even starting operators at too rapid of a rate of replan prompting, at the 30 second interval, can lead to lower system performance. There were no significant correlations between the time-weighted average prompting interval and any of the performance metrics.…”

“…In a previous experiment, the impact of increasing automation replan prompting rates on operator performance and workload was examined [6]. The operator was prompted to replan at various intervals, but could choose to replan whenever he or she desired.…”

“…The algorithm is able to make these local repairs faster through inter-agent communication than it could if it had to wait for the next update from the human operator. While modern automated planners are capable of generating new schedules on the order of seconds [4,35], human approval of these frequent schedule updates can cause high operator workload with potentially negative performance consequences [6].…”

Operator Object Function Guidance for a Real-Time Unmanned Vehicle Scheduling Algorithm

“…In this concept of operations, a single operator will supervise multiple vehicles, providing high-level direction to achieve mission goals, and will need to comprehend a large amount of information while under time pressure to make effective decisions in a dynamic environment. Although multiple studies have demonstrated the capacity of a single operator to control multiple UVs [12,13], the large amount of data generated by such a system could cause operator cognitive saturation, which has been shown to correlate with poor operator performance [14,15]. To mitigate possible high mental workload in these future systems, operators will be assisted by automated planners, which can be faster and more accurate than humans at path planning [16] and task allocation [17] in a multivariate, dynamic, time-pressured environment.…”

“…In these previous studies, the assumption was that the planning algorithms were static and unchanging throughout the period in which the human interacted with the automation. Operator SA was typically low, and operators complained about the lack of transparency in how the automation generated plans [12,18,33,45]. Thus, developing a method for human operators to modify the objective function of the automated planner in real time could provide the transparency necessary to maintain operator SA, while enabling operators to communicate their desires to the automation.…”