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Particle Swarm Guidance System for Autonomous Unmanned Aerial Vehicles in an Air Defence Role

Published online by Cambridge University Press:  10 December 2007

Alec Banks ,
Jonathan Vincent  and
Keith Phalp
Alec Banks*
Affiliation:
(Tornado In-service Software Maintenance Team)
Jonathan Vincent
Affiliation:
(Bournemouth University)
Keith Phalp
Affiliation:
(Bournemouth University)
*
(Email: [email protected])
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Abstract

This work investigates the utilisation of Particle Swarm Optimisation (PSO) for the non-deterministic navigation of Unmanned Aerial Vehicles (UAVs), allowing them to work cooperatively toward the goal of protecting a wide area against airborne attack. To negate the PSO's inherent weakness in dynamic environments, a neighbourhood scheme is proposed that not only enables the efficient interception of targets several times faster than the UAVs but also facilitates the maintenance of effective airspace coverage. Empirical results suggest that these techniques may indeed be of use in autonomous navigation systems for UAVs in air defence roles.

Keywords

UAVGuidance systemParticle Swarm OptimisationDynamic Environment
Type
Research Article
Information
The Journal of Navigation , Volume 61 , Issue 1 , January 2008 , pp. 9 - 29
Copyright
Copyright © The Royal Institute of Navigation 2007

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References

REFERENCES

AdaCore, (2005). Ada Academic Initiative. Available from: http://www.adacore.com/academic_overview.php [accessed 26 January 2005].Google Scholar
Alighanbari, M., Kuwata, Y. and How, J. P. (2003). Coordination and Control of Multiple UAVs with Timing Constraints and Loitering. IEEE American Control Conference 2003.CrossRefGoogle Scholar
Anderson, C. and Franks, N. R. (2001). Teams in animal societies. Behavioral Ecology Vol. 12, No. 5, pp. 534–540.Google Scholar
Chandler, P, Pachter, M., Swaroop, D., Fowler, J, et al. (2002). Complexity in UAV cooperative control. IEEE American Control Conference 2002.CrossRefGoogle Scholar
Clerc, M. and Kennedy, J. (2002). The particle Swarm: explosion, stability and convergence in a multi-dimensional complex space. IEEE Transactions on Evolutionary Computation No.6, 2002Google Scholar
Doctor, S. and Venayagamoorthy, G. K. (2004). Unmanned Vehicle Navigation Using Swarm Intelligence. International Conference on Intelligent Sensing and Information Processing, Chennai, India, pp. 249–253, 2004.Google Scholar
Eberhart, R. C. and Shi, Y. (2000). Comparing inertia weights and constriction factors in particle swarm optimization. Proc. 2000 Congr. Evolutionary Computing, pp 84–88, 2000.Google Scholar
English, J. (2000). About JEWL. Available from: http://www.it.bton.ac.uk/staff/je/jewl/ [Accessed 22 June 2005].Google Scholar
Frew, E. W. and Lawrence, D. A. (2005). Cooperative Stand-off Tracking of Moving Targets by a Team of Autonomous Aircraft. AIAA Guidance, Navigation, and Control Conference, San Francisco, CA, August 2005.CrossRefGoogle Scholar
Heppner, F. and Grenander, U. (1990). A stochastic nonlinear model for coordinated bird flocks. In Krasner, S., Ed., The Ubiquity of Chaos. Washington DC, USA: AAAS Publications, 1990.Google Scholar
Jin, Y., Minai, A. A. and Polycarpou, M. M. (2003). Cooperative real-time search and task allocation in UAV Teams. In Proceedings of the IEEE Conference on Decision and Control, pp. 7–12.Google Scholar
Kennedy, J. and Eberhart, R. C. (1995). Particle swarm optimization. Proceedings of IEEE International Conference on Neural Networks, Piscataway, NJ. pp. 1942–1948, 1995.CrossRefGoogle Scholar
Kennedy, J. (1997). The particle swarm: Social adaptation of knowledge. Proceedings of the 1997 International Conference on Evolutionary Computation, pp. 303–308. IEEE Service Center, Piscataway, NJ, 1997.CrossRefGoogle Scholar
Kennedy, J. and Eberhart, R. C. (1997). A discrete binary version of the particle swarm algorithm. Proceedings of the World Multiconference on Systemics, Cybernetics and Informatics 1997, Piscataway, NJ. pp. 4104–4109, 1997.CrossRefGoogle Scholar
Kennedy, J. (1999). Small Worlds and Mega-Minds: effects of neighborhood topology on particle swarm performance. Proc. Congress on Evolutionary Computation 1999, pp. 1931–1938. Piscataway, NJ: IEEE Service Center, 1999.Google Scholar
Kennedy, J. and Mendes, R. (2002). Population structure and particle swarm performance. Proceedings of the Congress on Evolutionary Computation, 2002. CEC 02., Volume 2, pp. 1671–1676, 2002.CrossRefGoogle Scholar
Krishna, K. M., Hexmoor, H., Pasupuleti, S. and Llinas, J. (2005). Parametric control of multiple unmanned air vehicles over an unknown hostile territory. In 2005 Proceedings of Knowledge Intensive Multiagent Systems (KIMAS-05), pages 117–121. Boston, MACrossRefGoogle Scholar
McCarley, J. S. and Wickens, C. D. (2004). Human factors concerns in UAV flight. University of Illinois at Urbana-Champaign Institute of Aviation, Aviation Human Factors Division, 2004.Google Scholar
Miasnikov, E. (2005). Threat of Terrorism Using Unmanned Aerial Vehicles: Technical Aspects. Center for Arms Control, Energy and Environmental Studies Moscow Institute of Physics and Technology 2005.Google Scholar
Millonas, M. M. (1994). Swarms, Phase Transitions, and Collective Intelligence. Artificial Life III. Ed., Langton, C.G., SFI Studies in the Sciences of Complexity, Proc. Vol. XVII, Addison-Wesley, 1994.Google Scholar
Reeves, W. T. (1983). Particle systems – a technique for modeling a class of fuzzy objects. ACM Transactions on Graphics, Vol. 2, No. 2, pp 91–108, April 1983.Google Scholar
Reynolds, C. W. (1987). Flocks, Herds, and Schools: A Distributed Behavioral Model, in Computer Graphics, 21(4) (SIGGRAPH ‘87 Conference Proceedings) pp 25–34. 1987.Google Scholar
Robinson, T. (2006). Out of the black. Aerospace International, April 2006. Royal Aeronautical Society.Google Scholar
Shi, Y. and Eberhart, R. C. (1998). A modified particle swarm optimizer. Proceedings of the IEEE International Conference on Evolutionary Computation, 69–73. Piscataway, NJ: IEEE Press, 1998.CrossRefGoogle Scholar