Using inverse learning for controlling bionic robotic fish with SMA actuators

Kewei Ning*, Pitoyo Hartono, Hideyuki Sawada

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

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.].

Original languageEnglish
Pages (from-to)649-655
Number of pages7
JournalMRS Advances
Volume7
Issue number30
DOIs
Publication statusPublished - 2022 Nov

ASJC Scopus subject areas

  • Materials Science(all)
  • Condensed Matter Physics
  • Mechanics of Materials
  • Mechanical Engineering

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