Assisting Dependent People at Home Through Autonomous Unmanned Aerial Vehicles

  • Lidia M. Belmonte
  • Rafael Morales
  • Arturo S. García
  • Eva Segura
  • Paulo Novais
  • Antonio Fernández-CaballeroEmail author
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1006)


This work describes a proposal of autonomous unmanned aerial vehicles (AUAVs) for home assistance of dependent people. AUAVs will monitor and recognize human activities during flight to improve their quality of life. However, before bringing such AUAV assistance to real homes, several challenges must be faced to make them viable and practical. Some challenges are technical and some others are related to human factors. In particular, several technical aspects are described for AUAV assistance: (1) flight control, based on our active disturbance rejection control algorithm, (2) flight planning (navigation in obstacle environments), and, (3) processing signals, acquired both from flight-control and monitoring sensors. From the assisted person’s viewpoint, our research focuses on three cues: (1) the user’s perception about AUAV assistance, (2) the influence on human acceptance of AUAV appearance and behavior at home, and (3) the human-robot interaction between assistant AUAV and assisted person. Finally, virtual reality environments are proposed to carry out preliminary tests and user acceptance evaluations.


Autonomous unmanned aerial vehicles Home assistance Dependent people 



This work has been partially supported by Spanish Ministerio de Ciencia, Innovación y Universidades, Agencia Estatal de Investigación (AEI)/European Regional Development Fund (FEDER, UE) under DPI2016-80894-R grant, and by CIBERSAM of the Instituto de Salud Carlos III. Lidia M. Belmonte holds FPU014/05283 scholarship from Spanish Ministerio de Educación y Formación Profesional.


  1. 1.
    Fernández-Caballero, A., Martínez-Rodrigo, A., Pastor, J.M., Castillo, J.C., Lozano-Monasor, E., López, M.T., Zangróniz, R., Latorre, J.M., Fernández-Sotos, A.: Smart environment architecture for emotion recognition and regulation. J. Biomed. Inform. 64, 55–73 (2016)CrossRefGoogle Scholar
  2. 2.
    Castillo, J.C., Castro-González, Á., Fernández-Caballero, A., Latorre, J.M., Pastor, J.M., Fernández-Sotos, A., Salichs, M.A.: Software architecture for smart emotion recognition and regulation of the ageing adult. Cogn. Comput. 8(2), 357–367 (2016)CrossRefGoogle Scholar
  3. 3.
    Belmonte, L.M., Morales, R., Fernández-Caballero, A., Somolinos, J.A.: A tandem active disturbance rejection control for a laboratory helicopter with variable speed rotors. IEEE Trans. Ind. Electron. 63(10), 6395–6406 (2016)CrossRefGoogle Scholar
  4. 4.
    Mahony, R., Kumar, V., Corke, P.: Multirotor aerial vehicles: modeling, estimation, and control of quadrotor. IEEE Robot. Autom. Mag. 19(3), 20–32 (2012)CrossRefGoogle Scholar
  5. 5.
    Leishman, R.C., MacDonald, J.C., Beard, R.W., McLain, T.W.: Quadrotors and accelerometers: state estimation with an improved dynamic model. IEEE Control Syst. Mag. 34(1), 28–41 (2014)MathSciNetCrossRefGoogle Scholar
  6. 6.
    Tanveer, M.H., Ahmed, S.F., Hazry, D., Warsy, F.A., Joyo, M.K.: Stabilized controller design for attitude and altitude controlling of quad-rotor under disturbance and noisy conditions. Am. J. Appl. Sci. 10(8), 819–831 (2013)CrossRefGoogle Scholar
  7. 7.
    Belmonte, L.M., Morales, R., Fernández-Caballero, A., Somolinos, J.A.: Robust decentralized nonlinear control for a twin rotor MIMO system. Sensors 16(8), 1160 (2016)CrossRefGoogle Scholar
  8. 8.
    Yu, Y., Lu, G., Sun, C., Liu, H.: Robust backstepping decentralized tracking control for a 3-DOF helicopter. Nonlinear Dynam. 82(1–2), 947–960 (2015)MathSciNetCrossRefGoogle Scholar
  9. 9.
    Fernández-Caballero, A., Belmonte, L.M., Morales, R., Somolinos, J.A.: Generalized proportional integral control for an unmanned quadrotor system. Int. J. Adv. Robot. Syst. 12, 85 (2015)CrossRefGoogle Scholar
  10. 10.
    Bouabdallah, S., Noth, A., Siegwart, R.: PID vs LQ control techniques applied to an indoor micro quadrotor. In: Proceedings of the 2004 IEEE/RSJ International Conference on Intelligent Robotic Systems, Senday, Japan, pp. 2451–2456 (2004)Google Scholar
  11. 11.
    Bertrand, S., Guenard, N., Hamel, T., Piet-Lahanier, H., Eck, L.: A hierarchical controller for miniature VTOL UAVs: design and stability analysis using singular perturbation theory. Cont. Eng. Pract. 19(10), 1099–1108 (2011)CrossRefGoogle Scholar
  12. 12.
    Dydek, Z.T., Annaswamy, A.M., Lavretsky, E.: Adaptive control of quadrotor UAVs: a design trade study with flight evaluations. IEEE Trans. Cont. Syst. Tech. 21(4), 1400–1406 (2013)CrossRefGoogle Scholar
  13. 13.
    Sun, L., Zuo, Z.: Nonlinear adaptive trajectory tracking control for a quad-rotor with parametric uncertainty. Proc. Inst. Mech. Eng. G 229(9), 1–13 (2014)Google Scholar
  14. 14.
    Castillo, J.C., Castro-González, Á., Alonso-Martín, F., Fernández-Caballero, A., Salichs, M.A.: Emotion detection and regulation from personal assistant robot in smart environment. In: Costa, A., Julián, V., Novais, P. (eds.) Personal Assistants: Emerging Computational Technologies, pp. 179–195. Springer, New York (2018)CrossRefGoogle Scholar
  15. 15.
    Morales, R., Fernández-Caballero, A., Somolinos, J.A., Sira-Ramírez, H.: Integration of sensors in control and automation systems. J. Sensors 2017, 6415876 (2017)Google Scholar
  16. 16.
    Castillo, J.C., Fernández-Caballero, A., Serrano-Cuerda, J., López, M.T., Martínez-Rodrigo, A.: Smart environment architecture for robust people detection by infrared and visible video fusion. J. Ambient. Intell. Humaniz. Comput. 8(2), 223–237 (2017)CrossRefGoogle Scholar
  17. 17.
    Lozano-Monasor, E., López, M.T., Vigo-Bustos, F., Fernández-Caballero, A.: Facial expression recognition in ageing adults: from lab to ambient assisted living. J. Ambient. Intell. Humaniz. Comput. 8(4), 567–578 (2017)CrossRefGoogle Scholar
  18. 18.
    Ekman, P., Friesen, W.V., Hager, J.C.: Facial Action Coding System. Manual and Investigator’s Guide. Research Nexus, Salt Lake City (2002)Google Scholar
  19. 19.
    Lamiraux, F., Sekhavat, S., Laumond, J.P.: Motion planning and control for Hilare pulling a trailer. IEEE Trans. Robot. Autom. 15(4), 640–652 (1999)CrossRefGoogle Scholar
  20. 20.
    Balch, T., Arkin, R.C.: Behavior-based information control for multirobot teams. IEEE Trans. Robot. Autom 14(6), 926–939 (1998)CrossRefGoogle Scholar
  21. 21.
    Shivaram, S.: Structural Health Monitoring of Wind Turbine Blades using Unmanned Air Vehicles. Master’s Dissertation, University of Dublin (2015)Google Scholar
  22. 22.
    Whitemore, H.: Koweit: how a drone is being used to monitor Health & Safety at the construction site, ENGIE Innovation (2015).
  23. 23.
    Florence, P.R., Carter, J., Ware, J., Tedrake, R.: NanoMap: fast, uncertainty-aware proximity queries with lazy search over local 3D data. In: International Conference on Robotics and Automation (ICRA), Brisbane, Australia (2018)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Lidia M. Belmonte
    • 1
    • 2
  • Rafael Morales
    • 1
    • 2
  • Arturo S. García
    • 1
    • 2
  • Eva Segura
    • 1
    • 2
  • Paulo Novais
    • 3
  • Antonio Fernández-Caballero
    • 1
    • 2
    • 4
    Email author
  1. 1.Instituto de Investigación en Informática de AlbaceteUniversidad de Castilla-La ManchaAlbaceteSpain
  2. 2.Escuela Técnica Superior de Ingenieros IndustrialesUniversidad de Castilla-La ManchaAlbaceteSpain
  3. 3.Escola de EngenhariaUniversidade do MinhoBragaPortugal
  4. 4.CIBERSAM (Biomedical Research Networking Centre in Mental Health)MadridSpain

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