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Distributed Flow Sensing Using Bayesian Estimation for a Flexible Fish Robot

[+] Author Affiliations
Feitian Zhang, Francis D. Lagor, Derrick Yeo, Patrick Washington, Derek A. Paley

University of Maryland, College Park, MD

Paper No. DSCC2015-9732, pp. V002T23A004; 10 pages
doi:10.1115/DSCC2015-9732
From:
  • ASME 2015 Dynamic Systems and Control Conference
  • Volume 2: Diagnostics and Detection; Drilling; Dynamics and Control of Wind Energy Systems; Energy Harvesting; Estimation and Identification; Flexible and Smart Structure Control; Fuels Cells/Energy Storage; Human Robot Interaction; HVAC Building Energy Management; Industrial Applications; Intelligent Transportation Systems; Manufacturing; Mechatronics; Modelling and Validation; Motion and Vibration Control Applications
  • Columbus, Ohio, USA, October 28–30, 2015
  • Conference Sponsors: Dynamic Systems and Control Division
  • ISBN: 978-0-7918-5725-0
  • Copyright © 2015 by ASME

abstract

Flexibility plays an important role in fish behaviors by enabling high maneuverability for predator avoidance and swimming in turbulence. In this paper, we present a novel, flexible fish robot equipped with distributed pressure sensors for flow sensing. The body of the robot is made of a soft, hyperelastic material that provides flexibility. The fish robot features a Joukowski-foil shape conducive to modeling the fluid analytically. A quasisteady potential-flow model is adopted for real-time flow estimation, whereas a discrete-time vortex-shedding flow model is used for higher-fidelity simulation. The dynamics for the flexible fish robot are presented, and a reduced model for one-dimensional swimming is derived. A recursive Bayesian filter assimilates pressure measurements for estimating the flow speed, angle of attack, and foil camber. Simulation and experimental results are presented to show the effectiveness of the flow estimation algorithm.

Copyright © 2015 by ASME

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