Affiliations: [a]
Universidad Autónoma de Madrid (UAM) 28049, Madrid, Spain
| [b]
University College London (UCL), London, UK
Correspondence:
[*]
Corresponding author. Víctor Rodríguez-Fernández, Universidad Autónoma de Madrid (UAM) 28049, Madrid, Spain. Tel.: +34 91 497 5531; E-mail: victor.rodriguez@inv.uam.es.
Abstract: Unmanned Aerial Vehicles (UAVs) are starting to provide new possibilities to human societies and their demand is growing according to the new industrial application fields for these revolutionary tools. The current systems are still evolving, specially from an Artificial Intelligence perspective, which is increasing the different tasks that UAVs can perform. However, the current state still requires a strong human supervision. As a consequence, a good preparation for UAV operators is mandatory due to some of their applications might affect human safety. During the training process, it is important to measure the performance of these operators according to different factors that can help to decide what operators are more suitable for different kinds of missions creating operator profiles. Having this goal in mind, this work aims to present an extensive and robust methodology to automatically extract different performance profiles from the training process of operators in an UAV simulation environment. Our method combines the definition of a set of performance metrics with clustering techniques to define operators profiles, ensuring that the behavior discrimination is suitable and consistent.
