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The human-machine partnership in UCAV operations

Published online by Cambridge University Press:  04 July 2016

A. D. White
A. D. White*
Affiliation:
QinetiQ , Cody Technology Park Farnborough, UK
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Abstract

At a time when a great deal of research on UCAVs is aimed at maximising their autonomy it should not be forgotten that human operators will, ultimately, remain ‘in control’ to some degree. The decision-sharing relationship between the operator and the UCAV depends on political constraints as well as the intelligence of the UCAV system. This in turn dictates the amount and type of information to be exchanged and the way in which it is communicated with the operator.

Operational flexibility is a key military driver and in order to achieve it, a variable autonomy command interface, combined with information fusion and intelligent decision-support systems, will be required. To be effective the operator will need to work in concert with the UCAV system rather than act simply as a command source and an information sink. The implications of this ‘partnership’ for command and monitoring requirements and in particular for weapons release authorisation, are discussed in this paper.

Type
Research Article
Information
The Aeronautical Journal , Volume 107 , Issue 1068: The UAV Special Issue , February 2003 , pp. 111 - 116
Copyright
Copyright © Royal Aeronautical Society 2001 

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References

1 Newell, A. Unified Theories of Cognition, Harvard University Press, London, 1990.Google Scholar
2 Rasmussen, J. Outline of a hybrid model of the process plant operator, in SHERIDAN and JOHANNSEN, Monitoring Behaviour and Supervisory control, Plenum, New York, 1976.Google Scholar
3 Howitt, S.L., Mayo, E. and Platts, J. Simulating the attack of a high value mobile target using combat UAVs, presented at the 16th Bristol International Conference on UAV systems, April 2001.Google Scholar
4 DrBanbury, S. The scope for decision support systems to improve FOAS mission effectiveness, Unpublished DERA report, 1997.Google Scholar
5 Taylor, R.M., Abdi, S., Dru-Drury, R., and Bonner, M.C. Cognitive cockpit systems: information requirements analysis for pilot control of cockpit automation, Ch. 10, p81-88, Engineering Psychology and Cog nitive Ergonomics Vol J, Aerospace and Transportation Systems, Harris, D. (Ed) Ashgate, Aldershot, 2001.Google Scholar
6 Taylor, R.M. Cognitive cockpit systems engineering: pilot authorisation and control of tasks, September 2001, 8th conference on cognitive science approaches to process control, Neubiberg, Germany.Google Scholar
7 Mayo, E. et al Combat UAV real-time SEAD mission simulation, presented at the 15th Bristol International Conference on UAV systems, April 2000.Google Scholar
8 Doll, T.J. and Home, R., Guidelines for developing and validating models of visual search and acquisition, September 2001, Opt Eng 40 (9) pp 1776–1783.Google Scholar
9 Baxter, J.W. Final report for control and co-ordination of entity level computer generated forces, March 2001, DERA/S&E/SPI/CR001524.Google Scholar
10 Allen, J.F. et al Toward conversational human-computer interaction, Al magazine, Winter 2001.Google Scholar