Open AccessReinforcement learning-based optimization of locomotion controller using multiple coupled CPG oscillators for elongated undulating fin propulsion
Author(s) -
Van Hong Nguyen,
Dinh Quoc Vo,
Van Tu Duong,
Huy Hung Nguyen,
Tan Tien Nguyen
Publication year - 2021
Publication title -
mathematical biosciences and engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.451
H-Index - 45
eISSN - 1551-0018
pISSN - 1547-1063
DOI - 10.3934/mbe.2022033
Subject(s) - fin , reinforcement learning , controller (irrigation) , control theory (sociology) , envelope (radar) , central pattern generator , robot , amplitude , propulsion , convergence (economics) , computer science , simulation , engineering , artificial intelligence , acoustics , physics , aerospace engineering , rhythm , biology , agronomy , radar , control (management) , quantum mechanics , economic growth , economics
This article proposes a locomotion controller inspired by black Knifefish for undulating elongated fin robot. The proposed controller is built by a modified CPG network using sixteen coupled Hopf oscillators with the feedback of the angle of each fin-ray. The convergence rate of the modified CPG network is optimized by a reinforcement learning algorithm. By employing the proposed controller, the undulating elongated fin robot can realize swimming pattern transformations naturally. Additionally, the proposed controller enables the configuration of the swimming pattern parameters known as the amplitude envelope, the oscillatory frequency to perform various swimming patterns. The implementation processing of the reinforcement learning-based optimization is discussed. The simulation and experimental results show the capability and effectiveness of the proposed controller through the performance of several swimming patterns in the varying oscillatory frequency and the amplitude envelope of each fin-ray.