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Reinforcement-learning control framework and sensing paradigm for flapping-wing micro aerial vehicles Motamed, Mehran
Abstract
Insects are fascinating for their maneuverability and complex aerobatics. Flapping-wing micro aerial vehicles are inspired from insect flight and aim to achieve high maneuverability at low speeds as well as hovering. Such a vehicle would have unique applications in social and economic sectors as well as in the military. This work, introduces a learning approach to flight control for flapping-wing micro aerial vehicles. A reinforcement-learning control framework has been proposed as a suitable biomimetic candidate for control of micro aerial vehicles. This work also discusses a matching sensing paradigm as a byproduct of the control approach. The control framework is then implemented using the Q-learning algorithm for the case study of lift generation for microflight. The results from a computer simulation using a quasi-steady aerodynamic model, and from an experimental investigation on a dynamically scaled model, confirm the applicability of the proposed framework. Moreover, the results of the learning scheme are shown to be comparable to a biological fruit fly, Drosophila melanogaster, in terms of the mean lift-force coefficient and the mean aerodynamic efficiency.
Item Metadata
Title |
Reinforcement-learning control framework and sensing paradigm for flapping-wing micro aerial vehicles
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Creator | |
Publisher |
University of British Columbia
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Date Issued |
2006
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Description |
Insects are fascinating for their maneuverability and complex aerobatics. Flapping-wing micro aerial vehicles are inspired from insect flight and aim to achieve high maneuverability at low speeds as well as hovering. Such a vehicle would have unique applications in social and economic sectors as well as in the military. This work, introduces a learning approach to flight control for flapping-wing micro aerial vehicles. A reinforcement-learning control framework has been proposed as a suitable biomimetic candidate for control of micro aerial vehicles. This work also discusses a matching sensing paradigm as a byproduct of the control approach. The control framework is then implemented using the Q-learning algorithm for the case study of lift generation for microflight. The results from a computer simulation using a quasi-steady aerodynamic model, and from an experimental investigation on a dynamically scaled model, confirm the applicability of the proposed framework. Moreover, the results of the learning scheme are shown to be comparable to a biological fruit fly, Drosophila melanogaster, in terms of the mean lift-force coefficient and the mean aerodynamic efficiency.
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Genre | |
Type | |
Language |
eng
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Date Available |
2010-01-08
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Provider |
Vancouver : University of British Columbia Library
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Rights |
For non-commercial purposes only, such as research, private study and education. Additional conditions apply, see Terms of Use https://open.library.ubc.ca/terms_of_use.
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DOI |
10.14288/1.0065534
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URI | |
Degree | |
Program | |
Affiliation | |
Degree Grantor |
University of British Columbia
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Graduation Date |
2006-05
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Campus | |
Scholarly Level |
Graduate
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Aggregated Source Repository |
DSpace
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Item Media
Item Citations and Data
Rights
For non-commercial purposes only, such as research, private study and education. Additional conditions apply, see Terms of Use https://open.library.ubc.ca/terms_of_use.