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DMD-Based Distributed Flow Sensing for Bio-Inspired Autonomous Underwater Robots

[+] Author Affiliations
Fengying Dang, Feitian Zhang

George Mason University, Fairfax, VA

Paper No. DSCC2018-9113, pp. V001T04A013; 8 pages
  • ASME 2018 Dynamic Systems and Control Conference
  • Volume 1: Advances in Control Design Methods; Advances in Nonlinear Control; Advances in Robotics; Assistive and Rehabilitation Robotics; Automotive Dynamics and Emerging Powertrain Technologies; Automotive Systems; Bio Engineering Applications; Bio-Mechatronics and Physical Human Robot Interaction; Biomedical and Neural Systems; Biomedical and Neural Systems Modeling, Diagnostics, and Healthcare
  • Atlanta, Georgia, USA, September 30–October 3, 2018
  • Conference Sponsors: Dynamic Systems and Control Division
  • ISBN: 978-0-7918-5189-0
  • Copyright © 2018 by ASME


This paper presents a novel flow sensing method for autonomous underwater robots using distributed pressure measurements. The proposed flow sensing method harnesses a Bayesian filter and a dynamic mode decomposition (DMD)-based reduced-order flow model to estimate the dynamic flow environments. This data-driven estimation method does not rely on any analytical flow models and is applicable to many and various dynamic flow fields for arbitrarily shaped underwater robots. To demonstrate the effectiveness of the proposed distributed flow sensing approach, a simulation study with a Joukowski-foil-shaped underwater robot is presented.

Copyright © 2018 by ASME



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