Modeling the Behavior of Unskilled Users in a Multi-UAV Simulation Environment

  • Víctor Rodríguez-FernándezEmail author
  • Antonio Gonzalez-Pardo
  • David Camacho
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9375)


The use of Unmanned Aerial Vehicles (UAVs) has been growing over the last few years. The accelerated evolution of these systems is generating a high demand of qualified operators, which requires to redesign the training process and focus on a wider range of candidates, including inexperienced users in the field, in order to detect skilled-potential operators. This paper uses data from the interactions of multiple unskilled users in a simple multi-UAV simulator to create a behavioral model through the use of Hidden Markov Models (HMMs). An optimal HMM is validated and analyzed to extract common behavioral patterns among these users, so that it is proven that the model represents correctly the novelty of the users and may be used to detect and predict behaviors in multi-UAV systems.


Unmanned Aerial Vehicles (UAVs) Human-Robot Interaction (HRI) Hidden Markov Model (HMM) Behavioral model 



This work is supported by the Spanish Ministry of Science and Education under Project Code TIN2014-56494-C4-4-P, Comunidad Autonoma de Madrid under project CIBERDINE S2013/ICE-3095, and Savier an Airbus Defense & Space project (FUAM-076914 and FUAM-076915). The authors would like to acknowledge the support obtained from Airbus Defence & Space, specially from Savier Open Innovation project members: José Insenser, Gemma Blasco and Juan Antonio Henríquez.


  1. 1.
    Baum, L.E., Petrie, T.: Statistical inference for probabilistic functions of finite state Markov chains. Ann. Math. Stat. 37, 1554–1563 (1966)MathSciNetCrossRefzbMATHGoogle Scholar
  2. 2.
    Boussemart, Y., Cummings, M.L., Fargeas, J.L., Roy, N.: Supervised vs. unsupervised learning for operator state modeling in unmanned vehicle settings. J. Aerosp. Comput. Inf. Commun. 8(3), 71–85 (2011)CrossRefGoogle Scholar
  3. 3.
    Burnham, K.P., Anderson, D.R.: Multimodel inference understanding AIC and BIC in model selection. Sociol. Methods Res. 33(2), 261–304 (2004)MathSciNetCrossRefGoogle Scholar
  4. 4.
    Forney Jr, G.D.: The viterbi algorithm. Proc. IEEE 61(3), 268–278 (1973)MathSciNetCrossRefGoogle Scholar
  5. 5.
    Hayashi, M.: Hidden Markov models to identify pilot instrument scanning and attention patterns. In: 2003 IEEE International Conference on Systems, Man and Cybernetics, vol. 3, pp. 2889–2896. IEEE (2003)Google Scholar
  6. 6.
    Leiden, K., Laughery, K.R., Keller, J., French, J., Warwick, W., Wood, S.D.: A review of human performance models for the prediction of human error. Ann Arbor 1001, 48105 (2001)Google Scholar
  7. 7.
    McCarley, J.S., Wickens, C.D.: Human factors concerns in UAV flight. University of Illinois at Urbana-Champaign Institute of Aviation, Aviation Human Factors Division (2004)Google Scholar
  8. 8.
    McKinley, R.A., McIntire, L.K., Funke, M.A.: Operator selection for unmanned aerial systems: comparing video game players and pilots. Aviat. Space Env. Med. 82(6), 635–642 (2011)CrossRefGoogle Scholar
  9. 9.
    Pereira, E., Bencatel, R., Correia, J., Félix, L., Gonçalves, G., Morgado, J., Sousa, J.: Unmanned air vehicles for coastal and environmental research. J. Coast. Res. 2009(56), 1557–1561 (2009)Google Scholar
  10. 10.
    Rabiner, L.: A tutorial on hidden Markov models and selected applications in speech recognition. Proc. IEEE 77(2), 257–286 (1989)CrossRefGoogle Scholar
  11. 11.
    Schmittmann, V.D., Visser, I., Raijmakers, M.E.: Multiple learning modes in the development of performance on a rule-based category-learning task. Neuropsychologia 44(11), 2079–2091 (2006)CrossRefGoogle Scholar
  12. 12.
    Rodríguez-Fernández, V., Héctor, D., Menéndez, D.C.: Design and development of a lightweight multi-uav simulator. In: 2015 IEEE International Conference on Cybernetics (CYBCONF). IEEE (Paper accepted, 2015)Google Scholar
  13. 13.
    Visser, I., Speekenbrink, M.: depmixs4: an R-package for hidden Markov models. J. Stat. Softw. 36(7), 1–21 (2010)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Víctor Rodríguez-Fernández
    • 1
    Email author
  • Antonio Gonzalez-Pardo
    • 2
    • 3
  • David Camacho
    • 1
  1. 1.Universidad Autónoma de Madrid (UAM)MadridSpain
  2. 2.Basque Center for Applied Mathematics (BCAM)BilbaoSpain
  3. 3.TECNALIA, OPTIMA UnitDerioSpain

Personalised recommendations