Resumen
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.
Antonio Gonzalez-Pardo
Profesor Titular de Universidad
Profesor del Departamento de Informática. Sus principales intereses de investigación están relacionados con la inteligencia computacional y la metaheurística aplicada al análisis de redes sociales, y la optimización de problemas basados en grafos.