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

Defense Intelligence Agency · 31 pages · text from the file's own layer

This Defense Intelligence Reference Document, dated 15 December 2010, is a Defense Intelligence Agency report from its Advanced Aerospace Weapons System Applications (AAWSA) Program. It looks at how many unmanned spacecraft one human pilot could control during future deep-space missions, drawing on research into air traffic control and piloting of multiple unmanned vehicles. It concludes that the limits are about 16 craft for simple tasks, 7 for moderately complex tasks and 4 for complex mixed fleets. It adds that physiological measures can signal when an operator is overloaded.

  • p. 5 …While additional future research may help to increase the automation component of aircraft and mission control…
  • p. 6 …to possible spacecraft missions. When a pilot or ATC operator is in control of several craft…
  • p. 19 …Correlation analyses were performed with the number of aircraft the operator was currently controlling. No adjustment…
  • p. 23 …Examples of several UAVs and their primary missions are shown in Figure 6. Control of vehicles…
  • p. 27 …of four craft can be controlled and tasked to complete missions. The number of four is…
  • p. 28 …Future research may increase the automation component of aircraft and mission control, but there is no…
  • p. 31 …Mission control of multiple unmanned aerial vehicles: a workload analysis. Hum Factors 47, 479-487 (2005…
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crash any planes in any of the scenarios - this required approximately 6 hours of
practice per participant before the experiment was conducted.
Only one of the eight controllers in the Brookings study rated the overload condition as
a loss of situational awareness. The results compared the TLX, primary task
performance, and physiological measures to low, medium, and high workload conditions,
as well as the max or overload condition. Primary task performance is represented in
Figure 4 (this chart was recreated visually from the source chart to accurately represent
all trends), showing a trending effect for complexity but not volume. Additional results
showed that changes in task difficulty (volume or complexity) produced changes in TLX,
eye blink rate, respiration rate, and the EEG power spectra. The EEG power spectra
were different for changes in volume versus changes in complexity. There were no
observed significant correlations with heart rate or heart rate variability. The authors
conclude that psychophysiological data can be used to accurately measure workload in
real-time, an observation they note confirms earlier work on F4 crew members
performing flight tasks of modulated complexity. 32
The Brookings data was reanalyzed by Wilson in 2003 using an artificial neural network
approach as well as a stepwise discriminate analysis to classify a physiological state as
either operational or overloaded. Wilson successfully classified the overload condition
consistently in more than 98% of the cases. The authors admit an issue with
psychophysiological variation (day-to-day) that would need to be normalized and
further research is required. 33
100 ,-----==---------------;=====.--
80
60
40
20
0 1-~-
Low Medium High
Workload
□ Volume
■ Complexity
□ Overload
Max
Figure 4. Representation of Performance Results from Brookings Study. 30 Shown are the three scenarios
with modulation of traffic volume, complexity, and the overload condition. Note that the Low workload entry for
the volume modulation performance (6 planes) was already 80% and this was similar to the 12-plane medium
complexity performance. Only the low complexity, 12-plane scenario showed near 100% primary task
performance.
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Report, from the dia collection. The PDF is mirrored here; the original link is above. 31 pages are in the text index: search them above, or from the library's search.