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Autonomous UAV control and testing methods utilizing partially observable Markov decision processes




Eaton, Christopher M., author
Chong, Edwin K. P., advisor
Maciejewski, Anthony A., advisor
Bradley, Thomas, committee member
Young, Peter, committee member

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The explosion of Unmanned Aerial Vehicles (UAVs) 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 an 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 sensors while providing an approach for more fuel efficient operations. 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.


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test and evaluation
verification and validation
services based testing of autonomy
unmanned aircraft


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