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Benjamin M. Finio
Researcher at Wyss Institute for Biologically Inspired Engineering
Publications - 17
Citations - 716
Benjamin M. Finio is an academic researcher from Wyss Institute for Biologically Inspired Engineering. The author has contributed to research in topics: Flapping & Torque sensor. The author has an hindex of 13, co-authored 17 publications receiving 663 citations. Previous affiliations of Benjamin M. Finio include Harvard University & Cornell University.
Papers
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Journal ArticleDOI
Progress on 'pico' air vehicles
Robert J. Wood,Benjamin M. Finio,Michael Karpelson,Kevin Y. Ma,Nestor O. Perez-Arancibia,Pratheev Sreetharan,Hiro Tanaka,John P. Whitney +7 more
TL;DR: Progress is presented in the essential technologies for insect-scale robots, or ‘pico’ air vehicles, as the characteristic size of a flying robot decreases.
Journal ArticleDOI
Artificial insect wings of diverse morphology for flapping-wing micro air vehicles
TL;DR: A novel fabrication process to create centimeter-scale wings of great complexity is introduced; via this process, a wing can be fabricated with a large range of desired mechanical and geometric characteristics, and will provide a platform for studies investigating the effects of wing morphology on flight dynamics.
Progress on "Pico" Air Vehicles.
Robert J. Wood,Benjamin M. Finio,Michael Karpelson,Kevin Y. Ma,Nestor O. Perez-Arancibia,Pratheev Sreetharan,Hiro Tanaka,John P. Whitney +7 more
TL;DR: Progress is presented in the essential technologies for insect-scale robots, or ‘pico’ air vehicles, as the characteristic size of a flying robot decreases.
Proceedings ArticleDOI
Body torque modulation for a microrobotic fly
TL;DR: A method to modulate body torques by altering the kinematics of each wing transmission independently, via the introduction of two additional control actuators is presented.
Proceedings ArticleDOI
System identification and linear time-invariant modeling of an insect-sized flapping-wing micro air vehicle
TL;DR: It is shown that a linearized model is sufficient to predict system behavior with reasonable accuracy over a large operating range, not just locally around the linearization state.