A Game Theoretical Framework for the Evaluation of Unmanned Aircraft Systems Airspace Integration Concepts
This addresses the need for better simulation models to safely integrate UAS into airspace, though it appears incremental by building on existing game theory and reinforcement learning methods.
This thesis tackled the problem of predicting outcomes for integrating Unmanned Aerial Systems (UAS) into the National Aerospace by developing 2D and 3D simulation frameworks using game theory to evaluate integration concepts where manned and unmanned vehicles co-exist, with results demonstrated through example scenarios involving manned aircraft and autonomous UAS equipped with sense and avoid algorithms.
Predicting the outcomes of integrating Unmanned Aerial Systems (UAS) into the National Aerospace (NAS) is a complex problem which is required to be addressed by simulation studies before allowing the routine access of UAS into the NAS. This thesis focuses on providing 2D and 3D simulation frameworks using a game theoretical methodology to evaluate integration concepts in scenarios where manned and unmanned air vehicles co-exist. The fundamental gap in the literature is that the models of interaction between manned and unmanned vehicles are insufficient: a) they assume that pilot behavior is known a priori and b) they disregard decision making processes. The contribution of this work is to propose a modeling framework, in which, human pilot reactions are modeled using reinforcement learning and a game theoretical concept called level-k reasoning to fill this gap. The level-k reasoning concept is based on the assumption that humans have various levels of decision making. Reinforcement learning is a mathematical learning method that is rooted in human learning. In this work, a classical and an approximate reinforcement learning (Neural Fitted Q Iteration) methods are used to model time-extended decisions of pilots with 2D and 3D maneuvers. An analysis of UAS integration is conducted using example scenarios in the presence of manned aircraft and fully autonomous UAS equipped with sense and avoid algorithms.