Artificial Intelligence Methodologies Applicable to Support the Decision-Making Capability on Board Unmanned Aerial Vehicles

The need for unmanned air vehicles (UAVs) to operate autonomously and to manage their operation with minimal intervention from the ground control station, in order to reduce the datalink utilization and maximize their exploitation in beyond line of sight (BLOS) operations, has been long recognized within industry and research institutes. Many artificial intelligence (AI) techniques try to address the challenge of moving UAV towards full autonomy. However, no single technique has been able to provide the required autonomy for unmanned platforms. This paper presents a unmanned air systems (UAS) architecture within which the different AI methodologies applicable to each subsystem are presented.