By van Maanen A.

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The low replanning condition contained 7 replanning events, while the high replanning condition contained 13. Each simulation was run several times faster than real time so an entire strike could take place over 30 minutes (instead of several hours). Predicting Operator Capacity for Supervisory Control of Multiple UAVs 21 Results and Discussion. In order to determine whether or not the revised fan-out prediction in (3) provided a more realistic estimate than the original fan-out (1), the number of vehicles controlled in the experiment was held constant (four) across all levels of automation.

Interestingly, as in the case with 5 targets, the minimum cost occurs at 2 UAVs for 10 targets. However, in the case of 5 targets, only 2 UAVs were needed to meet the operational requirements and the operator could meet this demand. , the wait times incurred by controlling more vehicles became unacceptably high. Thus, at the inflection point in these curves, the left region is primarily constrained by operational demands, but the right region is dominated by human performance limitations, specifically wait times, as seen in Figure 13.

14. B. L. Verplank, “Human and Computer Control of Undersea Teleoperators,” MIT, Cambridge, Man-Machine Systems Laboratory Report 1978. 15. G. Osga, K. Van Orden, N. Campbell, D. Kellmeyer, and D. Lulue, “Design and Evaluation of WarfighterTask Support Methods in a Multi-Modal Watchstation,” SPAWAR, San Diego 1874, 2002. 16. L. J. Mitchell, “Automated Scheduling Decision Support for Supervisory Control of Multiple UAVs,” Journal of Aerospace Computing, Information, and Communication, in press. 17.

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