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A Review of Locomotion, Control, and Implementation of Robot Fish

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Abstract

A comprehensive review of bio-inspired robot fish is presented in this paper with an emphasis on locomotion, actuation, and control methods. Different swimming modes of biological fish, such as Body and/or Caudal Fin propulsion (BCF) mode and Media and/or Paired Fin propulsion (MPF) mode, with their classification, are discussed in detail. Furthermore, the mechanics’ principles behind the locomotion, both kinematic and dynamic, are also introduced. In addition to dynamic modeling, motion control is also one of the key problems in the research of robot fish. In this paper, in addition to the classic control methodologies used for rigid robot fish, we also summarize the control approaches for soft robot fish, and the intelligent control based on machine learning that emerged in recent years. With basic ideas bear in mind, we will introduce two typical examples illustrating how to utilize these principles in the robot fish design. In the end, the potential research gaps and future research directions are summarized.

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Acknowledgements

The support from the Natural Sciences and Engineering Research Council of Canada (NSERC) through a Discovery Grant (No. 213541) and the VP Startup Fund from Memorial University (No. 212948) is dutifully acknowledged.

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This work was supported in part by the Natural Sciences and Engineering Research Council of Canada (NSERC) through a Discovery Grant (No. 213541) and in part by VP Startup Fund from Memorial University under Grant 212948.

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    Xinyu Jian & Ting Zou

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Xinyu Jian performed the literature survey, drafted the manuscript and revised it critically for the key content. Ting Zou is the corresponding author, responsible for organizing the manuscript sequence alignment, proofreading and revising the manuscript, and giving the final approval of the version to be published. All authors read and approved the final manuscript.

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Jian, X., Zou, T. A Review of Locomotion, Control, and Implementation of Robot Fish. J Intell Robot Syst 106, 37 (2022). https://doi.org/10.1007/s10846-022-01726-w

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