Surveillance of Unmanned Aerial Vehicles Using Probability Collectives

  • Přemysl Volf
  • David Šišlák
  • Dušan Pavlíček
  • Michal Pěchouček
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6867)


A rising deployment of unmanned aerial vehicles in complex environment operations requires advanced coordination and planning methods. We address the problem of multi-UAV-based area surveillance and collision avoidance. The surveillance problem contains non-linear components and non-linear constraints which makes the optimization problem a hard one. We propose discretization of the problem based on the definition of the points of interest and time steps to reduce its complexity. The objective function integrates both the area surveillance and collision avoidance sub-problems. The optimization task is solved using a probability collection solver that allows to distribute computation of the optimization. We have implemented the probability collective solver as a multi-agent simulation. The results show the approach can be used for this problem.


Surveillance Collision Avoidance Probability Collectives Multi-Agent Systems 


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  1. 1.
    Bar-Yam, Y.: Dynamics of Complex Systems. Perseus Books, Cambridge (1997)zbMATHGoogle Scholar
  2. 2.
    Bieniawski, S.R.: Distributed optimization and flight control using collectives. Dissertation Stanford University (2005)Google Scholar
  3. 3.
    Bloch, A.M.: Nonholonomic Mechanics and Control. Springer, NY (2003)CrossRefGoogle Scholar
  4. 4.
    Caffarelli, L., Crespi, V., Cybenko, G., Gamba, I., Rus, D.: Stochastic Distributed Algorithms for Target Surveillance. Intelligent Systems Design and Applications, 137 (2003)Google Scholar
  5. 5.
    Dubins, L.E.: On curves of minimal length with a constraint on average curvature, and with prescribed initial and terminal positions and tangents. American Journal of Mathematics (79), 497–516 (1957)MathSciNetCrossRefzbMATHGoogle Scholar
  6. 6.
    Fudenberg, D., Levine, D.K.: The Theory of Learning in Games. MIT Press, Cambridge (1998)zbMATHGoogle Scholar
  7. 7.
    Goldberg, D.E.: Genetic Algorithms in Search, Optimization and Machine Learning. Addison-Wesley Longman Publishing Co., Inc., Boston (1989)zbMATHGoogle Scholar
  8. 8.
    Lee, C.F., Wolpert, D.H.: Product distribution theory for control of multi-agent systems. In: AAMAS 2004: Proceedings of the Third International Joint Conference on Autonomous Agents and Multiagent Systems, pp. 522–529. IEEE Computer Society, Washington, DC, USA (2004)Google Scholar
  9. 9.
    MacKay, D.: Information theory, inference, and learning algorithms. Cambridge University Press, Cambridge (2003)zbMATHGoogle Scholar
  10. 10.
    Metropolis, N., Ulam, S.: The monte carlo method. Journal of the American Statistical Association 44(247), 335–341 (1949)MathSciNetCrossRefzbMATHGoogle Scholar
  11. 11.
    Nigam, N., Kroo, I.: Persistent Surveillance Using Multiple Unmanned Air Vehicles. In: 2008 IEEE Aerospace Conference, pp. 1–14 (2008)Google Scholar
  12. 12.
    Savla, K.: Multi UAV Systems with Motion and Communication Constraints. PhD thesis, University of California (2007)Google Scholar
  13. 13.
    Schutte, J., Greonwold, A.: Optimal sizing design of truss structures using the particle swarm optimization algorithm. In: 9th AIAA/ISSMO Symposium on Multidisciplinary Analysis and Optimization, pp. 2002–5639. AIAA (September 2002)Google Scholar
  14. 14.
    Vollmer, H.: Computational complexity of constraint satisfaction. In: Cooper, S.B., Löwe, B., Sorbi, A. (eds.) CiE 2007. LNCS, vol. 4497, pp. 748–757. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  15. 15.
    Wolpert, D.H.: Information theory – the bridge connecting bounded rational game theory and statistical physics. In: Braha, D., Minai, A.A., Bar-Yam, Y. (eds.) Complex Engineered Systems, pp. 262–290. Springer, Berlin (2006)CrossRefGoogle Scholar
  16. 16.
    Wolpert, D.H., Bieniawski, S.: Distributed control by lagrangian steepest descent. In: 43th IEEE Conference on Decision and Control (December 2004)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Přemysl Volf
    • 1
  • David Šišlák
    • 1
  • Dušan Pavlíček
    • 1
  • Michal Pěchouček
    • 1
  1. 1.Agent Technology Center, Department of Cybernetics Faculty of Electrical EngineeringCzech Technical UniversityPragueCzech Republic

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