Untethered Bimodal Robotic Fish with Tunable Bistability

Xu Chao, Imran Hameed, David Navarro-Alarcon, X.J. Jing*

*Corresponding author for this work

Research output: Chapters, Conference Papers, Creative and Literary WorksRGC 32 - Refereed conference paper (with host publication)peer-review

1 Citation (Scopus)

Abstract

In nature, fish are excellent swimmers due to their flexible and precise control of tail, which allows them to freely transform between the smooth flapping and the motion of rapid response so that they can move with dexterity. Here, inspired by the versatile motion abilities of fish, a novel robotic fish has been developed, featuring the capability of adaptable bistability. Through tuning the bistability, the robot can acquire two locomotion modes, namely monostable and bistable modes, and it can also swim at different energy barrier that needs to be overcome to realize the bistable motion. The theoretical models are derived to facilitate the control of the robot and the understanding of its nonlinear behavior. The impact of the tunable bistability on the swimming and turning performance is investigated through extensive experiments. The study effectively demonstrates the robotic fish’s capability to swiftly and efficiently navigate through mode switches, enabled by its tunable bistability. This feature is essential for underwater robots to perform tasks in intricate environments. © 2024 IEEE.
Original languageEnglish
Title of host publication2024 IEEE International Conference on Robotics and Automation (ICRA)
PublisherIEEE
Pages1491-1497
ISBN (Electronic)979-8-3503-8457-4
ISBN (Print)979-8-3503-8458-1
DOIs
Publication statusPublished - 2024
Event2024 IEEE International Conference on Robotics and Automation (ICRA 2024): CONNECT+ - Yokohama, Japan
Duration: 13 May 202417 May 2024
https://2024.ieee-icra.org/

Conference

Conference2024 IEEE International Conference on Robotics and Automation (ICRA 2024)
Abbreviated titleICRA2024
Country/TerritoryJapan
CityYokohama
Period13/05/2417/05/24
Internet address

Funding

This work was supported by the startup fund of City University of Hong Kong (9380140) and a Shenzhen-HK-Macau Scheme-C fund (9240115).

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