Using inverse learning for controlling bionic robotic fish with SMA actuators

Kewei Ning*, Pitoyo Hartono, Hideyuki Sawada

*この研究の対応する著者

研究成果: Article査読

抄録

In this study, we develop an untethered bionic soft robotic fish for swimming motion. The body of the fish is molded using soft silicone rubber, and we utilize shape memory alloy wires for its actuators. Its lightness and flexibility allow the robotic fish to generate biomimetic swimming motions. Due to the complexity of mathematically modeling the robot’s swimming dynamics, building a realistic simulator is prohibitively difficult. Hence, in this study, we introduce inverse learning for a feedforward neural network to generate control parameters for realizing desired swimming motions and subsequently utilize the neural network for real-time control. In this paper, we report on the electro-mechanical structure of our robotic fish and the experiment of the neuro-controller. Graphical abstract: [Figure not available: see fulltext.].

本文言語English
ページ(範囲)649-655
ページ数7
ジャーナルMRS Advances
7
30
DOI
出版ステータスPublished - 2022 11月

ASJC Scopus subject areas

  • 材料科学(全般)
  • 凝縮系物理学
  • 材料力学
  • 機械工学

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