Abstract
In this paper we present the parameterisation and optimisation of the CACOC (Chaotic Ant Colony Optimisation for Coverage) mobility model applied to Unmanned Aerial Vehicles (UAV) in order to perform surveillance tasks. The use of unpredictable routes based on the chaotic solutions of a dynamic system as well as pheromone trails improves the area coverage performed by a swarm of UAVs. We propose this new application of CACOC to detect intruders entering an area under surveillance. Having identified several parameters to be optimised with the aim of increasing intruder detection rate, we address the optimisation of this model using a Cooperative Coevolutionary Genetic Algorithm (CCGA). Twelve case studies (120 scenarios in total) have been optimised by performing 30 independent runs (360 in total) of our algorithm. Finally, we tested our proposal in 100 unseen scenarios of each case study (1200 in total) to find out how robust is our proposal against unexpected intruders.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Acevedo, J.J., Arrue, B.C., Maza, I., Ollero, A.: Cooperative large area surveillance with a team of aerial mobile robots for long endurance missions. J. Intell. Robot. Syst. 70(1–4), 329–345 (2013)
Atten, C., Channouf, L., Danoy, G., Bouvry, P.: UAV fleet mobility model with multiple pheromones for tracking moving observation targets. In: Squillero, G., Burelli, P. (eds.) EvoApplications 2016. LNCS, vol. 9597, pp. 332–347. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-31204-0_22
Chelouah, R., Siarry, P.: Continuous genetic algorithm designed for the global optimization of multimodal functions. J. Heuristics 6(2), 191–213 (2000)
Galceran, E., Carreras, M.: A survey on coverage path planning for robotics. Robot. Auton. Syst. 61(12), 1258–1276 (2013)
Goldberg, D.E.: Genetic Algorithms in Search, Optimization and Machine Learning, 1st edn. Addison-Wesley Longman Publishing Co., Inc., Boston (1989)
Goldberg, D.E., Deb, K.: A comparative analysis of selection schemes used in genetic algorithms. Found. Genet. Algorithms 1, 69–93 (1991)
Hecker, J.P., Letendre, K., Stolleis, K., Washington, D., Moses, M.E.: Formica ex Machina: ant swarm foraging from physical to virtual and back again. In: Dorigo, M., et al. (eds.) ANTS 2012. LNCS, vol. 7461, pp. 252–259. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-32650-9_25
Holland, J.H.: Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control and Artificial Intelligence. MIT press, Cambridge (1992)
McNeal, G.S.: Drones and the future of aerial surveillance. George Wash. Law Rev. Arguendo 84(2), 354 (2016)
Metropolis, N., Ulam, S.: The Monte Carlo method. J. Am. Stat. Assoc. 44(247), 335–341 (1949)
Potter, M.A., De Jong, K.A.: A cooperative coevolutionary approach to function optimization. In: Davidor, Y., Schwefel, H.-P., Männer, R. (eds.) PPSN 1994. LNCS, vol. 866, pp. 249–257. Springer, Heidelberg (1994). https://doi.org/10.1007/3-540-58484-6_269
Riehl, J.R., Collins, G.E., Hespanha, J.P.: Cooperative search by UAV teams: a model predictive approach using dynamic graphs. IEEE Trans. Aerospace Electron. Syst. 47(4), 2637–2656 (2011)
Rosalie, M., Danoy, G., Chaumette, S., Bouvry, P.: Chaos-enhanced mobility models for multilevel swarms of UAVs. Swarm Evol. Comput. 41(November 2017), 36–48 (2018)
Rosalie, M., Letellier, C.: Systematic template extraction from chaotic attractors: II. genus-one attractors with multiple unimodal folding mechanisms. J. Phys. A: Math. Theor. 48(23), 235101 (2015)
Varrette, S., Bouvry, P., Cartiaux, H., Georgatos, F.: Management of an academic HPC cluster: the UL experience. In: Proceedings of the 2014 International Conference on High Performance Computing & Simulation (HPCS 2014), pp. 959–967. IEEE, Bologna, July 2014
Zhu, W., Duan, H.: Chaotic predator-prey biogeography-based optimization approach for UCAV path planning. Aerospace Sci. Technol. 32(1), 153–161 (2014)
Zurad, M., et al.: Target tracking optimization of UAV swarms based on dual-pheromone clustering. In: 2017 3rd IEEE International Conference on Cybernetics (CYBCONF), pp. 1–8, no. October. IEEE, June 2017
Acknowledgments
This work relates to Department of Navy award N62909-18-1-2176 issued by the Office of Naval Research. The United States Government has a royalty-free license throughout the world in all copyrightable material contained herein. This work is partially funded by the joint research programme UL/SnT-ILNAS on Digital Trust for Smart-ICT. The experiments presented in this paper were carried out using the HPC facilities of the University of Luxembourg [15] – see https://hpc.uni.lu.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Stolfi, D.H., Brust, M.R., Danoy, G., Bouvry, P. (2020). Optimizing the Performance of an Unpredictable UAV Swarm for Intruder Detection. In: Dorronsoro, B., Ruiz, P., de la Torre, J., Urda, D., Talbi, EG. (eds) Optimization and Learning. OLA 2020. Communications in Computer and Information Science, vol 1173. Springer, Cham. https://doi.org/10.1007/978-3-030-41913-4_4
Download citation
DOI: https://doi.org/10.1007/978-3-030-41913-4_4
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-41912-7
Online ISBN: 978-3-030-41913-4
eBook Packages: Computer ScienceComputer Science (R0)