Abstract
With recent and ongoing improvements to unmanned aerial vehicle (UAV) endurance and availability, they are in a unique position to provide long term surveillance in risky environments. This paper presents a swarm intelligence algorithm for executing an exhaustive and persistent search of a non-trivial area of interest using a decentralized UAV swarm without long range communication. The algorithm allows for an environment containing arbitrary arrangements of no-fly zones, non-uniform levels of priority and dynamic priority changes in response to target acquisition or external commands. Performance is quantitatively analysed via comparative simulation with another leading algorithm of its class.
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References
Parunak, H., Purcell, M., O’Connell, R.: Digital pheromones for autonomous coordination of swarming UAVs. In: Proceedings of First AIAA Unmanned Aerospace Vehicles, Systems, Technologies, and Operations Conference, AIAA (2002)
Varga, R.: The UAV Reloaded: Placing Video Intelligence On-board. COTS Journal, 72–76 (June 2003)
Parunak, H.V.D.: “Go to the Ant”: Engineering Principles from Natural Multi-Agent Systems (1999)
Martin, C.E., Macfadzean, R.H., Barber, K.S.: Supporting Dynamic Adaptive Autonomy for Agent-based Systems. In: Proceedings of 1996 Artificial Intelligence and Manufacturing Research Planning Workshop, AIAA, pp. 112–120 (1996)
Brueckner, S.: Return from the Ant: Synthetic Ecosystems for Manufacturing Control, Thesis at Humboldt University Berlin, Department of Computer Science (2000)
Altshuler, Y., Yanovski, V., Wagner, I.A., Bruckstein, A.M.: The Cooperative Hunters - Efficient Cooperative Search For Smart Targets Using UAV Swarms. In: ICINCO-MARS-2005, Barcelona, Spain (2005)
Erignac, C.A.: An Exhaustive Swarming Search Strategy Based on Distributed Pheromone Maps. In: Advanced Design Systems M&CT, The Boeing Company (2007)
Sauter, J.A., Matthews, R., Parunak, H.V.D., Arbor, A., Brueckner, S.A.: Performance of digital pheromones for swarming vehicle control. In: Proceedings of the fourth international joint conference on Autonomous agents and multiagent systems (2005)
Walter, B., Sannier, A., Reiners, D., Oliver, J.: UAV Swarm Control: Calculating Digital Pheromone Fields with the GPU. The Journal of Defense Modeling and Simulation: Applications, Methodology, Technology 3(3), 167–176 (2006)
Howden, D., Hendtlass, T.: Collective intelligence and bush fire spotting. In: GECCO 2008: Proceedings of the 10th annual conference on Genetic and evolutionary computation (2008)
Aerosonde Pty Ltd, http://www.aerosonde.com/pdfs/aerosonde02-20-09.pdf
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© 2009 Springer-Verlag Berlin Heidelberg
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Howden, D. (2009). Continuous Swarm Surveillance via Distributed Priority Maps. In: Korb, K., Randall, M., Hendtlass, T. (eds) Artificial Life: Borrowing from Biology. ACAL 2009. Lecture Notes in Computer Science(), vol 5865. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-10427-5_22
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DOI: https://doi.org/10.1007/978-3-642-10427-5_22
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-10426-8
Online ISBN: 978-3-642-10427-5
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