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Flocking of autonomous unmanned air vehicles

Published online by Cambridge University Press:  04 July 2016

B. Crowther*
Affiliation:
School of Engineering University of Manchester, UK

Abstract

The use of large numbers of unmanned air vehicles in a given air space presents a challenge for conventional air traffic control methods. Flocking (or schooling, swarming or herding) in nature arises when mobile organisms find benefit in living at high densities. The present paper applies rules of flocking (cohesion, alignment, separation, and migration) to the problem of managing the flight of a number of autonomous unmanned air vehicles. A six-degree of freedom aerodynamic model of an existing UAV is used to simulate the flocking flight vehicles. It is found that application of the cohesion and alignment rules is sufficient to generate true flocking behaviour in that the flight vehicle density is increased and the flock members converge on a common heading. Increasing rule strength reduces the time taken to achieve flocking behaviour. However increasing rule strength too far leads to oscillatory or unstable flight paths. It is found that flock behaviour can be adequately described using the time histories of two statistical parameters: the mean radius between flock members and the standard deviation of the flock members’ heading angles.

Type
Research Article
Copyright
Copyright © Royal Aeronautical Society 2001 

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References

1. Burdun, I. and Parfentyev, O. AI knowledge model for self-organizing conflict prevention/resolution in close free-flight air space, IEEE Aerospace Applications conference proceedings, 1999, 2, pp 409428.Google Scholar
2. Potts, W. The chorus line hypothesis of manoeuvre coordination in avian flocks, letter in Nature, 24 May 1984, 309, pp 344345.Google Scholar
3. Shaw, E. Fish in schools, Natural History, 1975, 84, (8), p 4046.Google Scholar
4. Partridge, B. The structure and function of fish schools, Scientific American, June 1982, pp 114123.Google Scholar
5. Anderson, M. and Robbins, A. Formation flight as a cooperative game, 1998, AIAA Guidance, Navigation and Control conference and exhibition, Boston, MA, August 1998, Technical Papers Part 1 (A98-37001 10-63), pp 244251.Google Scholar
6. Czirok, A., Vicsek, M. and Vicsek, T. Collective motion of organisms in three dimensions, Physica A, Feb 1999, 264, (1-2), pp 299304.Google Scholar
7. Toner, J. and Tu, Y. Flocks, herds, and schools: a quantitative theory of flocking, Physical Review E, October 1998, 58, (4), pp 48284858.Google Scholar
8. Reynolds, C. Flocks, herds, and schools: a distributed behavioural model, Computer Graphics, July 1987, 21, (4), pp 2534.Google Scholar
9. Reynolds, C. Not bumping into things, 1988, Notes on ‘obstacle avoid ance’ for course on physically-based modeling at SIGGRAPH 88, August 1988, Atlanta, GA, USA.Google Scholar