Hostname: page-component-848d4c4894-jbqgn Total loading time: 0 Render date: 2024-06-30T05:48:23.786Z Has data issue: false hasContentIssue false

Autonomy in unmanned air vehicles

Published online by Cambridge University Press:  03 February 2016

J. T. Platts*
Affiliation:
QinetiQ Ltd, Bedford Technology Park, Bedford, UK

Abstract

The paper describes a key risk area threatening the widespread deployment of unmanned air vehicles (UAVs), that of attaining high levels of autonomy. Autonomy is loosely defined in the context of UAVs and the meaning of ‘level of autonomy’ discussed. The paper argues that the achievement of high levels of autonomy is not merely a function of increasing machine intelligence but also of maintaining the human operator’s engagement with the decision making process and retaining human authority. An assumption is that a human being in the loop will be a requirement for safety, flight clearance and legal reasons on early systems. Therefore, developers of highly autonomous systems are presented with a paradox. It will be argued that the human must be placed at the centre of the design process and consequently human factors, the human machine interface and the system architecture become critical to achieving high levels of autonomy. This quality impacts on the entire knowledge acquisition and design cycle and broadens what is meant by that term placing it as a discipline firmly in the systems design community. The paper concludes by outlining the key barriers to the successful development of highly autonomous UAVs.

Type
Research Article
Copyright
Copyright © Royal Aeronautical Society 2006 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

1. Clough, B., Metrics, schmetrics! how do you track a UAV’s autonomy?, 1st AIAA UAV Systems, Technologies, and Operations Conference and Workshop, May 2002, Portsmouth VA, USA.Google Scholar
2. White, A., (a), The Human-machine Partnership in UCAV Operations, 17th Unmanned Air Vehicle Systems Conference, Bristol, UK.Google Scholar
3. White, A., (b), The role of the operator in UCAV operations, Unmanned Systems Conference 2002, Disney’s Coronado Springs Resort, FL, USA.Google Scholar
4. Lin, C-F., Advanced Control Systems Design, Prentice Hall Series in Advanced Navigation, Guidance, and Control, and their Applications, Prentice-Hall Inc, 1994, USA.Google Scholar
5. Rasmussen, J., Skills, rules, and knowledge; signals, signs, and symbols, and other distinctions in human performance models, IEEE transactions on systems, man, and cybernetics, SMC-13, 3, May/June 1983.Google Scholar
6. Rasmussen, J., Information Processing and Human-Machine Interaction, An Approach to Cognitive Engineering, 1986, 12, Elsevier Science Publishing Company.Google Scholar
7. Strens, M.J. e-mail from Strens, M. (mjstrens@qinetiq.com) to Platts, J.T. (jtplatts@qinetiq.com), dated 30 May 2002, Subject: Levels Comment.Google Scholar
8. Platts, J.T. and Forsythe, W., (a) The application of self-organising fuzzy control to station-keeping in unmanned air vehicles, Aeronaut J, July 2001, 105, (1049), pp 359 Google Scholar
9. Platts, J.T., (b) A Self-organising Fuzzy Logic Supervisor, PhD Thesis, 2001, Loughborough University.Google Scholar
10. Norman, D. (1991), in Harris, D., The human factors of fully automatic flight, measurement and control, J Institute of Measurement and Control, July 2003, 36, (6).Google Scholar
11. Smith, P.R.S. Mayo, E., O’hara, J. and Griffith, D., Combat UAV real-time SEAD mission simulation, AIAA Flight Mechanics Conference 1999, AIAA-99-4185, Baltimore, USA.Google Scholar
12. Howitt, S.L., Mayo, E. and Platts, J.T., Simulating the attack of high value mobile targets using combat UAVs, 2001, Bristol RPV Conference, Bristol, UK.Google Scholar
13. Howitt, S.L. and Platts, J.T., Real-time deep strike mission simulation using air-launched UAVs, 2002, AIAA Conference on UAVs, Portsmouth VA, USA.Google Scholar
14. Barber, K.S. and Martin, C.E., Agent autonomy: specification, measurement, and dynamic adjustment. In proceedings of the autonomy control software at autonomous agents 1999 (Agents ’99), 8-15 May 1999, Seattle, WA.Google Scholar
15. Agard, , 1995, Knowledge Based Guidance and Control Functions, AGARD Report No 325.Google Scholar
16. Taylor, R.M., Abdi, S., Dru-Drury, R. and Bonner, M.C. 2001, Cognitive cockpit systems: information requirements analysis for pilot control of cockpit automation, 2001, Chapter 10, pp. 8188 in ‘Engineering Psychology and Cognitive Ergonomics. 5, Aerospace and Transportation Systems, Harris, D. (Ed), Ashgate, Aldershot, 2001.Google Scholar
17. Platts, J.T., Application of a variable autonomy framework to the control of multiple air launched UAVs; Proceedings of 16th association of unmanned vehicle systems international conference, July 2002, Orlando, FL.Google Scholar
18. Bonner, M., Diethe, T. and Mathews, S., Scoping study for the insertion of cognitive cockpit adaptive automation taxonomy and control system into RTAVS for control of UCAV autonomy, QinetiQ internal study report, August 2001.Google Scholar
19. OED, 2004, Oxford English Dictionary, Oxford University Press.Google Scholar