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Dynamic Modeling of Robotic Fish With a Flexible Caudal Fin

[+] Author Affiliations
Jianxun Wang, Philip K. McKinley, Xiaobo Tan

Michigan State University, East Lansing, MI

Paper No. DSCC2012-MOVIC2012-8695, pp. 203-212; 10 pages
doi:10.1115/DSCC2012-MOVIC2012-8695
From:
  • ASME 2012 5th Annual Dynamic Systems and Control Conference joint with the JSME 2012 11th Motion and Vibration Conference
  • Volume 2: Legged Locomotion; Mechatronic Systems; Mechatronics; Mechatronics for Aquatic Environments; MEMS Control; Model Predictive Control; Modeling and Model-Based Control of Advanced IC Engines; Modeling and Simulation; Multi-Agent and Cooperative Systems; Musculoskeletal Dynamic Systems; Nano Systems; Nonlinear Systems; Nonlinear Systems and Control; Optimal Control; Pattern Recognition and Intelligent Systems; Power and Renewable Energy Systems; Powertrain Systems
  • Fort Lauderdale, Florida, USA, October 17–19, 2012
  • Conference Sponsors: Dynamic Systems and Control Division
  • ISBN: 978-0-7918-4530-1
  • Copyright © 2012 by ASME

abstract

Among the various designs of robotic fish, tail-actuation is particularly attractive since it is simple, enables both swimming and turning, and allows a rigid body for housing sensors and electronics. In this paper we present a new dynamic model for robotic fish propelled by a flexible tail that is actuated at the base, and explore the effect of stiffness of the tail beam on the locomotion performance of the robot. The tail is approximated by multiple rigid elements connected in series through rotational springs and dampers, to effectively and efficiently capture the large deformation of the beam. The hydrodynamic force on each segment is evaluated with Lighthill’s large-amplitude elongated-body theory. Model analysis shows that, given the same base movement, the stiffness of the tail has a prominent influence on the tail dynamics and the resulting motion (e.g., speed) of the robotic fish. Experiments are conducted on a robotic fish prototype when it is mounted with a rigid and flexible tail, respectively. The results show that the model is able to predict the motion of the robot for both cases, when the tail base is actuated with a variety of patterns. The model is expected to be instrumental in the optimization and control of robotic fish.

Copyright © 2012 by ASME

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