Optimizing the Performance of an Unpredictable UAV Swarm for Intruder Detection

  • Daniel H. StolfiEmail author
  • Matthias R. Brust
  • Grégoire Danoy
  • Pascal Bouvry
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 1173)


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.


Swarm robotics Mobility model Unmanned Aerial Vehicle Evolutionary Algorithm Surveillance 



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


  1. 1.
    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)CrossRefGoogle Scholar
  2. 2.
    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). Scholar
  3. 3.
    Chelouah, R., Siarry, P.: Continuous genetic algorithm designed for the global optimization of multimodal functions. J. Heuristics 6(2), 191–213 (2000)CrossRefGoogle Scholar
  4. 4.
    Galceran, E., Carreras, M.: A survey on coverage path planning for robotics. Robot. Auton. Syst. 61(12), 1258–1276 (2013)CrossRefGoogle Scholar
  5. 5.
    Goldberg, D.E.: Genetic Algorithms in Search, Optimization and Machine Learning, 1st edn. Addison-Wesley Longman Publishing Co., Inc., Boston (1989)zbMATHGoogle Scholar
  6. 6.
    Goldberg, D.E., Deb, K.: A comparative analysis of selection schemes used in genetic algorithms. Found. Genet. Algorithms 1, 69–93 (1991)MathSciNetGoogle Scholar
  7. 7.
    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). Scholar
  8. 8.
    Holland, J.H.: Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control and Artificial Intelligence. MIT press, Cambridge (1992)CrossRefGoogle Scholar
  9. 9.
    McNeal, G.S.: Drones and the future of aerial surveillance. George Wash. Law Rev. Arguendo 84(2), 354 (2016)Google Scholar
  10. 10.
    Metropolis, N., Ulam, S.: The Monte Carlo method. J. Am. Stat. Assoc. 44(247), 335–341 (1949)CrossRefGoogle Scholar
  11. 11.
    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). Scholar
  12. 12.
    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)CrossRefGoogle Scholar
  13. 13.
    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)CrossRefGoogle Scholar
  14. 14.
    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)MathSciNetCrossRefGoogle Scholar
  15. 15.
    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 2014Google Scholar
  16. 16.
    Zhu, W., Duan, H.: Chaotic predator-prey biogeography-based optimization approach for UCAV path planning. Aerospace Sci. Technol. 32(1), 153–161 (2014)CrossRefGoogle Scholar
  17. 17.
    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 2017Google Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Interdisciplinary Centre for Security, Reliability and Trust (SnT)University of LuxembourgEsch-sur-AlzetteLuxembourg
  2. 2.FSTM/DCSUniversity of LuxembourgEsch-sur-AlzetteLuxembourg

Personalised recommendations