Chris Eaton's PhD Thesis Abstract

Autonomous UAV Control and Testing Methods Utilizing Partially Observable Markov Decision Processes

Ph.D., Colorado State University, May 2018

Co-Major Professors: E.K.P. Chong and Anthony A. Maciejewski

The explosion of Unmanned Aerial Vehicles (UAV) and the rapid development of algorithms to support autonomous flight operations of UAVs has resulted in a diverse and complex set of requirements and capabilities. This dissertation provides and approach to effectively manage these autonomous UAVs, effectively and efficiently command these vehicles through their mission, and to verify and validate that the system meets requirements. A high level system architecture is proposed for implementation on any UAV. A Partially Observable Markov Decision Process algorithm for tracking moving targets is developed for fixed field of view targets while providing options for fuel efficient options. Finally, an approach for testing autonomous algorithms and systems is proposed to enable efficient and effective test and evaluation to support verification and validation of autonomous system requirements.