Assisting Dependent People at Home Through Autonomous Unmanned Aerial Vehicles
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.
KeywordsAutonomous 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.
- 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
- 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.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.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
- 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
- 21.Shivaram, S.: Structural Health Monitoring of Wind Turbine Blades using Unmanned Air Vehicles. Master’s Dissertation, University of Dublin (2015)Google Scholar
- 22.Whitemore, H.: Koweit: how a drone is being used to monitor Health & Safety at the construction site, ENGIE Innovation (2015). https://innovation.engie.com/en/news/news/smart-buildings/koweit-how-a-drone-is-being-used-to-monitor-health-safety-at-the-construction-site-1/1112
- 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