Force Optimization of Elongated Undulating Fin Robot Using Improved PSO-Based CPG.

Van Dong Nguyen,Quang Duy Tran,Thi Thom Hoang,Quoc Tuan Vu,Huy Hung Nguyen,Van Tu Duong,Tan Tien Nguyen,Ciro Castiello

doi:10.1155/2022/2763865

Abstract

Biorobotic fishes have a huge impact on the development of underwater devices due to both fast swimming speed and great maneuverability. In this paper, an enhanced CPG model is investigated for locomotion control of an elongated undulating fin robot inspired by black knife fish. The proposed CPG network includes sixteen coupled Hopf oscillators for gait generation to mimic fishlike swimming. Furthermore, an enhanced particle swarm optimization (PSO), called differential particle swarm optimization (D-PSO), is introduced to find a set of optimal parameters of the modified CPG network. The proposed D-PSO-based CPG network is not only able to increase the thrust force in order to make the faster swimming speed but also avoid the local maxima for the enhanced propulsive performance of the undulating fin robot. Additionally, a comparison of D-PSO with the traditional PSO and genetic algorithm (GA) has been performed in tuning the parametric values of the CPG model to prove the superiority of the introduced method. The D-PSO-based optimization technique has been tested on the actual undulating fin robot with sixteen fin-rays. The obtained results show that the average propulsive force of the untested material is risen 5.92%, as compared to the straight CPG model.

Highlights

Test Results and Discussion e proposed D-particle swarm optimization (PSO)-based central pattern generator (CPG) optimization method is performed both on the simulation model in MATLAB and during an experiment with the real elongated undulating fin
In the term of simulation, each joint of the undulating fin is driven by the CPG unit, whose amplitude values are chosen in the range of [0, 40] degree based on the actual mechanical structure
In the term of simulation, each joint of the undulating fin is driven by the CPG unit, whose amplitude values are chosen in the range of [2.5, 40] degree based on the actual mechanical structure

Summary

Introduction

Robotic technologies are attracting significant attention from researchers, especially in the fields of outer space and ocean exploration that are difficult for humans to access. In the area of ocean science, autonomous underwater vehicle (AUV) has been strongly developed by using different propulsion mechanisms such as jets, axial propellers, and body or fin [1–3]. High maneuverability, and rapid speed, bionic fish robots with fin propulsion systems have been widely applied for a large number of underwater devices. Many researchers have investigated to improve the motion performance of biorobotic fishes using fin-ray as a propulsion [4, 5], in which dynamic modeling, locomotion control, and optimization are mainly focused. The rhythmic movements of biorobotic fishes are produced by central pattern generator (CPG) networks [6–8]. Biological CPG serves neural networks that can generate patterned neural without any periodical inputs obtained by higher control centers or the feedback signal from sensors [9]. Using some differential equations with various imprecise parameters, selecting a set of CPG characteristic parameters for improved performance is necessary

Methods

Results

Conclusion