Abstract
This paper presents a hardware testbed that furthers the development of a dynamic data-driven application system (DDDAS). In particular, the focus of this testbed is on enabling a self-aware unmanned aerial vehicle (UAV). Self-awareness in this context refers to the ability of the vehicle to collect information about itself and use this information to alter the way it completes missions via on-board dynamic decision-making. Prior work has focused on developing computational methods that enable a digital twin of this vehicle, and demonstration of the resulting self-aware capability via simulation. This work presents a hardware testbed and associated experimental methodology for data collection, analysis, and demonstration of the self-aware UAV concept. The hardware testbed includes custom-built carbon fiber wings, the design of which have been validated via flight test. A sensor suite composed of wireless high frequency dynamic strain sensors has been developed and demonstrated using benchtop experiments. The proposed DDDAS architecture, which includes previously developed computational methods, has the potential to enable two-way coupling between estimation of the UAV structural state and dynamic mission replanning; capability that is critical for realizing the self-aware UAV concept.
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Acknowledgments
The authors thank Gray Riley and Alexander Vladimir Andersen of Aurora Flight Sciences for their contributions to the development of the testbed aircraft. This work was supported in part by AFOSR grant FA9550-16-1-0108 under the Dynamic Data-Driven Application Systems Program, the MIT-SUTD International Design Center, and a Cockrell School of Engineering graduate fellowship.
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Salinger, S.J., Kapteyn, M.G., Kays, C., Pretorius, J.V.R., Willcox, K.E. (2020). A Hardware Testbed for Dynamic Data-Driven Aerospace Digital Twins. In: Darema, F., Blasch, E., Ravela, S., Aved, A. (eds) Dynamic Data Driven Applications Systems. DDDAS 2020. Lecture Notes in Computer Science(), vol 12312. Springer, Cham. https://doi.org/10.1007/978-3-030-61725-7_7
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