• DocumentCode
    663912
  • Title

    Fish lateral line inspired hydrodynamic feedforward control for autonomous underwater vehicles

  • Author

    Yiming Xu ; Mohseni, Kamran

  • Author_Institution
    Dept. of Mech. & Aerosp. Eng., Univ. of Florida, Gainesville, FL, USA
  • fYear
    2013
  • fDate
    3-7 Nov. 2013
  • Firstpage
    3565
  • Lastpage
    3870
  • Abstract
    Studies have shown that the lateral line found in most fish and some other aquatic organisms is capable of providing hydrodynamic information of the surrounding fluid, which may facilitate many behavioral decisions. Previous work by the group introduced a lateral line inspired feedforward design for underwater vehicle control. The system utilizes pressure sensor arrays to estimate the hydrodynamic force acting on the vehicle such that the additional information will simplify the modeling process and improve the maneuvering accuracy for the control tasks in underwater exploration and environmental monitoring. In this paper, the feedforward control design is presented and tested in simulation for trajectory tracking and path following after expressing the force estimation algorithm in the three-dimensional domain. Pressure measurements at multiple locations on the vehicle surface form a least squares approximation of the pressure distribution. Hydrodynamic forces acting on the vehicle are then estimated and passed to the controller for improved performance. Preliminary experimental tests are conducted to vindicate the proposed algorithm.
  • Keywords
    approximation theory; autonomous underwater vehicles; feedforward; hydrodynamics; path planning; pressure measurement; trajectory control; aquatic organisms; autonomous underwater vehicles; behavioral decisions; feedforward control design; fish lateral line inspired hydrodynamic feedforward control; force estimation algorithm; hydrodynamic forces; hydrodynamic information; least squares approximation; path following; pressure distribution; pressure measurements; three-dimensional domain; trajectory tracking; Estimation; Feedforward neural networks; Force; Hydrodynamics; Trajectory; Vectors; Vehicles;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Intelligent Robots and Systems (IROS), 2013 IEEE/RSJ International Conference on
  • Conference_Location
    Tokyo
  • ISSN
    2153-0858
  • Type

    conf

  • DOI
    10.1109/IROS.2013.6696909
  • Filename
    6696909