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Cited by 288

The DOI matches its Crossref record; 77 of the 77 references it cites were found in a registry or an index.

Fish-inspired robots: design, sensing, actuation, and autonomy—a review of research

Raj, Aditi, Thakur, Atul·2016·Bioinspiration & Biomimetics

Abstract

Underwater robot designs inspired by the behavior, physiology, and anatomy of fishes can provide enhanced maneuverability, stealth, and energy efficiency. Over the last two decades, robotics researchers have developed and reported a large variety of fish-inspired robot designs. The purpose of this review is to report different types of fish-inspired robot designs based upon their intended locomotion patterns. We present a detailed comparison of various design features like sensing, actuation, autonomy, waterproofing, and morphological structure of fish-inspired robots reported in the past decade. We believe that by studying the existing robots, future designers will be able to create new designs by adopting features from the successful robots. The review also summarizes the open research issues that need to be taken up for the further advancement of the field and also for the deployment of fish-inspired robots in practice.

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Crossref · 10.1088/1748-3190/11/3/031001

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Metadata

Authors
Raj, A., & Thakur, A.
Venue
Bioinspiration & Biomimetics
Published
2016-04-13
Volume
11
Issue
3
Pages
031001
ISSN
  • 1748-3182 (ISSN-L)
  • 1748-3190
OpenAlex
W2340642587
Cited by
288
References
77
FWCI
18.63
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Fish-inspired robots: design, sensing, actuation, and autonomy—a review of research. https://app.cochat.ai/papers/10.1088/1748-3190/11/3/031001
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Raj, A., & Thakur, A. (2016). Fish-inspired robots: design, sensing, actuation, and autonomy—a review of research. Bioinspiration & Biomimetics. https://doi.org/10.1088/1748-3190/11/3/031001

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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.