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Automatic profile generation for UAV operators using a simulation-based training environment


Unmanned aerial vehicles (UAVs) are becoming a hot topic in the last few years for several research areas, such as aeronautics or computer science. Big companies such as Airbus or Amazon aim to incorporate this technology to their current systems to improve the quality of their services while reducing the human costs. However, current UAV technology requires a strong human supervisory control and this supposes an important potential risk. Therefore, it is critical to keep track of the pilot behavior to be able to determine whether he is ready or not to operate with this technology. To deal with this problem, we have developed a methodology based on different performance metrics to automatically evaluate planning and monitoring skills of new users trained in a multi-UAV simulation environment. This methodology, based on unsupervised learning and fuzzy logic, can automatically generate a profile of future operators and use it to assess their skills.

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This work has been supported by the next research projects: TIN2014-56494-C4-4-P, CIBERDINE S2013/ICE-3095, SeMaMatch EP/K032623/1 and Airbus Defence and Space (FUAM-076914 and FUAM-076915). The authors would like to acknowledge the support obtained from Airbus Defence and Space, specially from Savier Open Innovation project members: José Insenser, Gemma Blasco, Juan Antonio Henríquez and César Castro. Finally, the authors would like to acknowledge the students from Video Games Programming course at Universidad Autónoma de Madrid, for their valuable collaboration in these experiments.

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Correspondence to David Camacho.

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Rodríguez-Fernández, V., Menéndez, H.D. & Camacho, D. Automatic profile generation for UAV operators using a simulation-based training environment. Prog Artif Intell 5, 37–46 (2016).

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  • Unmanned aerial vehicles
  • Simulation-based training
  • User profile
  • Clustering
  • Fuzzy control system