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Evolutionary optimization of robotic fish control and morphology

Published: 06 July 2013 Publication History

Abstract

The nonlinear dynamics of an aquatic environment make robotic fish behavior difficult to predict and subsequently difficult to optimize. In this paper, we present a method for optimizing robotic fish propulsion through the evolution of control patterns and caudal fin flexibility. Evolved solutions are evaluated in a physics-based simulation environment. Control signals are generated with both simple sinusoids and neural oscillators. This study demonstrates how evolutionary algorithms can be utilized to handle the complex interactions among material properties, physical form, and control patterns in an aquatic environment.

References

[1]
A. J. Clark and P. K. McKinley. Evolutionary optimization of robotic fish control and morphology. Technical Report MSU-CSE-13-2, Computer Science and Engineering, Michigan State University, East Lansing, Michigan, April 2013.
[2]
A. J. Clark, J. M. Moore, J. Wang, X. Tan, and P. K. McKinley. Evolutionary design and experimental validation of a flexible caudal fin for robotic fish. In Artificial Life, volume 13, pages 325--332, 2012.
[3]
M. J. Lighthill. Large-Amplitude Elongated-Body Theory of Fish Locomotion. Proceedings of the Royal Society B: Biological Sciences, 179(1055):125--138, Nov. 1971.
[4]
K. Matsuoka. Sustained oscillations generated by mutually inhibiting neurons with adaptation. Biological Cybernetics, 376:367--376, 1985
[5]
R. Smith. The Open Dynamics Engine, www.ode.org, 2012.
[6]
J. Wang, P. K. McKinley, and X. Tan. Dynamic modeling of robotic fish with a flexible caudal fin. In Proceedings of the ASME 2012 5th Annual Dynamic Systems and Control Conference, joint with the JSME 2012 11th Motion and Vibration Conference, Ft. Lauderdale, Florida, USA, October 2012. Paper DSCC2012-MOVIC2012-8695.

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  • (2024)Design optimizer for planar soft-growing robot manipulatorsEngineering Applications of Artificial Intelligence10.1016/j.engappai.2023.107693130(107693)Online publication date: Apr-2024

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  1. Evolutionary optimization of robotic fish control and morphology

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    cover image ACM Conferences
    GECCO '13 Companion: Proceedings of the 15th annual conference companion on Genetic and evolutionary computation
    July 2013
    1798 pages
    ISBN:9781450319645
    DOI:10.1145/2464576
    • Editor:
    • Christian Blum,
    • General Chair:
    • Enrique Alba
    Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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    Association for Computing Machinery

    New York, NY, United States

    Publication History

    Published: 06 July 2013

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    Author Tags

    1. evolutionary robotics
    2. morphology
    3. neural oscillator
    4. robotic fish

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    GECCO '13
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    GECCO '13: Genetic and Evolutionary Computation Conference
    July 6 - 10, 2013
    Amsterdam, The Netherlands

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    Overall Acceptance Rate 1,669 of 4,410 submissions, 38%

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    • (2024)Design optimizer for planar soft-growing robot manipulatorsEngineering Applications of Artificial Intelligence10.1016/j.engappai.2023.107693130(107693)Online publication date: Apr-2024

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