Defense Intelligence Reference Document Cognitive Limits On Simultaneous Control Of Multiple Unmanned Spacecraft

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Lee has previously shown that multiple automated ground vehicles can be autopiloted
successfully in a two-stage process when nominal information is available beforehand
about the environment to be searched. The two-stage process includes an offline,
permission routing table generation where the primary path of each vehicle in planned
and downloaded, and an online traffic control stage where small changes to the plan
are executed to avoid collisions and deal with contingent activity. 47
Attempting to increase the limits of UAV to pilot ratio, Ruff examined simulations using
three augmented control techniques: manual, management by consent, and
management by exception. The authors concluded that the middle level of automation
performed best when considering that augmentation algorithms may have associated
errors. They also concluded that the absolute maximum number of UAVs a person could
control is four.
Cummings addresses the UAV interface issue in a study on retargeting multiple in-flight
cruise missiles. Cruise missiles were chosen because they require minimal active
piloting. A dual screen interface was constructed very similar to the ATC setup on one
map and one list of objects being tracked (in the case of the ATC system, the list is
physical rather than a second computer display - see Figure 3b). Cummings side-steps
the issue of a cognitive workload metric based on complexity and number of tracked
objects by assuming that an operator can execute changes to only one vehicle at a time,
and then counting the ratio of time busy making changes to total time in a scenario.
This measure is called utilization and previous work in systems engineering 48 and
queuing theory has shown that utilization rates around 70% max out the typical human
operator's ability to hold a big picture. We note that this scenario involves minimal
interaction between the missiles, such as flight path conflicts. The missile study is
compared to free flight ATC task, where en route and conflict resolution is the
responsibility of pilots rather than ATC operators.
Cummings develops several performance measures that are worthy, but for the current
treatise an analogue is more succinct: the utilization measure in multiple-object
tracking and retargeting is similar to a grandmaster playing many games of chess
simultaneously. They walk from board to board, think for a bit, make a move, and then
move to the next board. Many high-level chess players can look at board position and
evaluate what the next move is without knowledge of previous moves in the game. The
question at hand is, how many simultaneous games can the grandmaster play before
he is forced to revert to cold position analysis with every new presentation of a game?
The conclusion is 16, and it agrees with previous work on free flight ATC. 49 Cummings
additionally takes issue with the Ruff limit of four UAVs, noting that this previous
experiment included a far more demanding piloting task.
More recent work by Cummings added aircraft heterogeneity to the experiment. 50 She
concluded again that 70% utilization is optimal, but notes that the queuing theory
concept of wait times will significantly affect the maximum number of vehicles that can
be attended. In a multiple-vehicle control situation, a vehicle that has exhibited some
decrement in performance has an interaction time with the operator to bring it back to
acceptable performance. It will then follow its automated routine for a period, called the
neglect time, until it falls again below performance threshold and requires the pilot's
attention: the time between the need for attention and the beginning of the next
interaction time is the wait time. Including wait times generated by more complex
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