Situational Understanding Tailorable Representation World Models

June 12, 2020

In the future, Air Force (AF) Unmanned Aerial Vehicles (UAVs) will need to perform missions more autonomously while in contested environments.   Having onboard, mission-relevant “knowledge” loaded before mission start will enable the UAVs to interpret incoming mission sensor data in context and provide better situational awareness which will in turn lead to better decisions.  This will be true both on an individual platform basis and for a collective of collaborating UAVs.  The goal of this project is to explore the concept of an onboard world model/data store that utilizes a synergistic graph-based and machine learning approach to provide a priori mission knowledge that is constantly updated with incoming knowledge from exploited mission sensor data.  The project will develop concepts and implement one or more representative instantiations of a world model to show and test capability.