Abstract
The increasing interest in the use of Unmanned Aerial Vehicles (UAV) in the last years has opened up a new complex area of research applications. Many works have been focused on the applicability of new Artificial Intelligence techniques to facilitate the successfully execution of UAV operations from the Ground Control Stations (GCSs). Some of the most demanded applications in this field are the reduction of the workload of operators and the automation of training processes. This paper presents new algorithms focused on this field: a Multi-Objective Genetic Algorithm for solving Mission Planning and Replanning problems and a Procedure Following Evaluation methodology based on Petri Nets. This paper is based on a framework that simulates a GCS with support for multiple UAVs. The functionality of this framework has been extended in two different directions: on the one hand, to deal with Mission Designing, Automated Mission Planning and Replanning, and Alert Generation; and, on the other hand, to perform different analysis tasks of the UAV operators. Using this framework, a test mission has been executed and debriefed, focusing on the main AI-based issues described in this work.
Antonio Gonzalez-Pardo
Associate Professor
Lecturer at the Computer Science Department. Main research interests are related to Computational Intelligence and Metaheuristics applied to Social Networks Analysis, and the optimization of graph-based problems.