This paper presents a wireless sensor system developed to use RC helicopter dynamics to measure wind turbulence. Wind turbulence is a safety concern for naval helicopter operations due to typical scarcity of landing/takeoff area on naval vessels. Wind turbulence affects the dynamics of helicopters by creating uneven thrust on the rotor blades. The proposed telemetry system, when retrofitted on an RC helicopter, extracts these external disturbances in the helicopter’s dynamics and maps the wind conditions. This study focuses on learning the helicopter’s dynamics in controlled wind conditions using machine learning algorithms. The presented telemetry system uses sensors such as an Inertial Measurement Unit (IMU), optical trackers, and GPS sensors to measure the dynamics of the flying RC helicopter. The system also measures the pilot’s radio inputs to account for pilot inputs in the helicopter’s dynamics. The telemetry system is trained and tested in a large indoor facility where turbulent wind conditions were created artificially using large wind circulation fans.
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ASME 2017 Dynamic Systems and Control Conference
October 11–13, 2017
Tysons, Virginia, USA
Conference Sponsors:
- Dynamic Systems and Control Division
ISBN:
978-0-7918-5829-5
PROCEEDINGS PAPER
An Inertial Sensor to Measure Wind Turbulence With RC Helicopters
Pinhas Ben-Tzvi
Pinhas Ben-Tzvi
Virginia Tech, Blacksburg, VA
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Anil Kumar
Virginia Tech, Blacksburg, VA
Pinhas Ben-Tzvi
Virginia Tech, Blacksburg, VA
Paper No:
DSCC2017-5354, V003T39A010; 6 pages
Published Online:
November 14, 2017
Citation
Kumar, A, & Ben-Tzvi, P. "An Inertial Sensor to Measure Wind Turbulence With RC Helicopters." Proceedings of the ASME 2017 Dynamic Systems and Control Conference. Volume 3: Vibration in Mechanical Systems; Modeling and Validation; Dynamic Systems and Control Education; Vibrations and Control of Systems; Modeling and Estimation for Vehicle Safety and Integrity; Modeling and Control of IC Engines and Aftertreatment Systems; Unmanned Aerial Vehicles (UAVs) and Their Applications; Dynamics and Control of Renewable Energy Systems; Energy Harvesting; Control of Smart Buildings and Microgrids; Energy Systems. Tysons, Virginia, USA. October 11–13, 2017. V003T39A010. ASME. https://doi.org/10.1115/DSCC2017-5354
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