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

Computational Intelligence and Neuroscience - Tập 2022 - Trang 1-11 - 2022
Van Dong Nguyen1, Quang Duy Tran1, Quoc Tuan Vu1, Van Tu Duong1, Huy Hung Nguyen2, Thi Thom Hoang3, Tan Tien Nguyen1
1National Key Laboratory of Digital Control and System Engineering (DCSELab), Ho Chi Minh City University of Technology (HCMUT), 268 Ly Thuong Kiet Street, District 10, Ho Chi Minh City, Vietnam
2Faculty of Electronics and Telecommunication, Saigon University, Ho Chi Minh City, Vietnam
3Department of Electronic & Electrical Engineering, NhaTrang University, Nha Trang, Vietnam

Tóm tắt

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.

Từ khóa


Tài liệu tham khảo

10.1142/s0219843607001217

10.3934/mbe.2021115

C. I. Sprague, Improving the modularity of AUV control systems using behaviour trees, 10.1109/AUV.2018.8729810

X. Zheng, 2018, Artificial lateral line based local sensing between two adjacent robotic fish, Bioinspiration & Biomimetics, 13, 10.1088/1748-3190/aa8f2e

10.1109/tro.2019.2956343

10.1002/rob.21470

10.1109/tmech.2012.2210905

10.1007/s10514-007-9071-6

10.1016/j.neunet.2008.03.014

10.1109/iros.2006.281800

10.3182/20080706-5-kr-1001.00916

10.1109/IECON.2013.6700181

10.1016/s1672-6529(14)60042-1

10.1109/med.2016.7535989

10.1109/tie.2014.2308150

N. Xuelei, 2014, Locomotion learning for an anguilliform robotic fish using central pattern generator approach, IEEE Transactions on Industrial Electronics, 61, 4780, 10.1109/TIE.2013.2288193

H. B. Z. Jose, Two-phase GA parameter tunning method of CPGs for quadruped gaits

10.1016/j.neucom.2019.01.062

J. Yu, 2015, CPG network optimization for a biomimetic robotic fish via PSO, IEEE Trans. on Netw and Learning Systems, 27, 1962, 10.1109/TNNLS.2015.2459913

10.1109/tevc.2007.896686

J. Kennedy, 2001, Swarm Intelligence

10.1155/2017/4135465

V. H. Nguyen, 2018, Computational study on upward force generation of gymnotiform undulating fin, AETA 2018-Recent Advances in Electrical Engineering and Related Sciences: Theory and Application. AETA

10.1109/URAI.2018.8441812

Thông tin
Thông tin xuất bản

Computational Intelligence and Neuroscience

Tập 2022

1-11

Thông tin tác giả