Central Pattern Generator Based Control of a Wire-driven Robot Fish

Xie, Fengran; Zhong, Yong; Kwok, Thomas M.; Du, Ruxu

doi:10.1109/icinfa.2018.8812605

Cited by 4 publications

(1 citation statement)

References 23 publications

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“…The present research proposes a different approach from the basic CPG model, and as a difference from other wire-driven robot fish motion approaches [43], this study introduces three fundamental undulation functions: forward, right-turning, and left-turning. These functions are derived from empirical measurements of the robot's caudal fin oscillation angles.…”

Section: Fuzzy-based Oscillation Pattern Generatormentioning

confidence: 99%

Electromyography-Based Biomechanical Cybernetic Control of a Robotic Fish Avatar

Montoya Martínez,

Torres-Córdoba,

Magid

et al. 2024

Machines

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This study introduces a cybernetic control and architectural framework for a robotic fish avatar operated by a human. The behavior of the robot fish is influenced by the electromyographic (EMG) signals of the human operator, triggered by stimuli from the surrounding objects and scenery. A deep artificial neural network (ANN) with perceptrons classifies the EMG signals, discerning the type of muscular stimuli generated. The research unveils a fuzzy-based oscillation pattern generator (OPG) designed to emulate functions akin to a neural central pattern generator, producing coordinated fish undulations. The OPG generates swimming behavior as an oscillation function, decoupled into coordinated step signals, right and left, for a dual electromagnetic oscillator in the fish propulsion system. Furthermore, the research presents an underactuated biorobotic mechanism of the subcarangiform type comprising a two-solenoid electromagnetic oscillator, an antagonistic musculoskeletal elastic system of tendons, and a multi-link caudal spine composed of helical springs. The biomechanics dynamic model and control for swimming, as well as the ballasting system for submersion and buoyancy, are deduced. This study highlights the utilization of EMG measurements encompassing sampling time and μ-volt signals for both hands and all fingers. The subsequent feature extraction resulted in three types of statistical patterns, namely, Ω,γ,λ, serving as inputs for a multilayer feedforward neural network of perceptrons. The experimental findings quantified controlled movements, specifically caudal fin undulations during forward, right, and left turns, with a particular emphasis on the dynamics of caudal fin undulations of a robot prototype.

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Section: Fuzzy-based Oscillation Pattern Generatormentioning

confidence: 99%