Locomotion Optimization of a Tendon-Driven Robotic Fish With Variable Passive Tail Fin
Changlin Qiu, Zhengxing Wu, Jian Wang, Min Tan, Junzhi Yu
- Year
- 2022
- Citations
- 49
Abstract
This article explores a set of locomotion optimization methods for a novel tendon-driven robotic fish. With the typical features of the dual tendon driving active tail and variable stiffness passive caudal fin, a fully functioning tendon-drive robotic fish is developed. Next, a practical dynamic model for the tendon-driven robotic fish is established with full consideration of variable stiffness passive caudal fin, and the model parameters are accurately identified via data-driven methods. More importantly, to improve the motion performances, an asymmetric central pattern generator is particularly proposed, and an adjustment rule of passive stiffness is explored to fit different motion states of the robotic fish. Finally, extensive simulations and aquatic experiments verify the feasibility of the proposed prototype and locomotion optimization methods. The obtained results show that the improvements of steering radius and forward swimming velocity are 36.3% and 29%, respectively. At present, our robotic fish can achieve maximum forward swimming as 1.04 BL/s (BL for the body length), maximum turning rate as 153.3 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$^\circ$</tex-math></inline-formula> /s, and the minimum turning radius reaches to 0.31 BL, providing a valuable reference for bioinspired research of aquatic mechanical design and locomotion control.
Keywords
Related papers
Statistical Learning Theory
Yuhai Wu, Vladimir Vapnik
1999
Artificial intelligence: a modern approach
1995
Applied Nonlinear Control
Jean-Jacques Slotine, Weiping Li
1991
A new optimizer using particle swarm theory
R.C. Eberhart, James Kennedy
2002