• 文献检索
  • 文档翻译
  • 深度研究
  • 学术资讯
  • Suppr Zotero 插件Zotero 插件
  • 邀请有礼
  • 套餐&价格
  • 历史记录
应用&插件
Suppr Zotero 插件Zotero 插件浏览器插件Mac 客户端Windows 客户端微信小程序
定价
高级版会员购买积分包购买API积分包
服务
文献检索文档翻译深度研究API 文档MCP 服务
关于我们
关于 Suppr公司介绍联系我们用户协议隐私条款
关注我们

Suppr 超能文献

核心技术专利：CN118964589B侵权必究
粤ICP备2023148730 号-1Suppr @ 2026

文献检索

告别复杂PubMed语法，用中文像聊天一样搜索，搜遍4000万医学文献。AI智能推荐，让科研检索更轻松。

立即免费搜索

文件翻译

保留排版，准确专业，支持PDF/Word/PPT等文件格式，支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述，25分钟生成高质量综述，智能提取关键信息，辅助科研写作。

立即免费体验

基于改进的 PSO 的 CPG 的狭长波动鳍机器人的力优化。

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

机构信息

National 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.

Faculty of Electronics and Telecommunication, Saigon University, Ho Chi Minh City, Vietnam.

出版信息

Comput Intell Neurosci. 2022 Mar 9;2022:2763865. doi: 10.1155/2022/2763865. eCollection 2022.

DOI:10.1155/2022/2763865
PMID:35310595
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC8926474/
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.

摘要

仿生机器鱼由于其快速的游动速度和良好的机动性，对水下设备的发展有着巨大的影响。在本文中，研究了一种增强型 CP 模型，用于受黑鱼启发的仿鱼形波动鳍机器人的运动控制。所提出的 CP 网络包括十六个耦合的 Hopf 振荡器，用于产生步态以模拟鱼类游动。此外，引入了一种增强的粒子群优化（PSO），称为差分粒子群优化（D-PSO），以找到一组修改后的 CP 网络的最佳参数。所提出的基于 D-PSO 的 CP 网络不仅能够增加推力以实现更快的游泳速度，而且还能够避免局部最大值，从而提高波动鳍机器人的推进性能。此外，还对 D-PSO 与传统的 PSO 和遗传算法（GA）进行了比较，以调整 CP 模型的参数值，从而证明了所提出方法的优越性。基于 D-PSO 的优化技术已在具有十六个鳍条的实际波动鳍机器人上进行了测试。所得到的结果表明，与直线 CP 模型相比，未经测试材料的平均推力提高了 5.92%。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b9f2/8926474/fb92f1fcb3ab/CIN2022-2763865.010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b9f2/8926474/b85799f1fd4c/CIN2022-2763865.001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b9f2/8926474/7ea25019d072/CIN2022-2763865.002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b9f2/8926474/888c3162085a/CIN2022-2763865.003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b9f2/8926474/6ee96afca25b/CIN2022-2763865.004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b9f2/8926474/1840a4f98b02/CIN2022-2763865.005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b9f2/8926474/911f826519a8/CIN2022-2763865.006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b9f2/8926474/a7625fe87a54/CIN2022-2763865.007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b9f2/8926474/f6690a45ff8d/CIN2022-2763865.008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b9f2/8926474/04fe9120dd9a/CIN2022-2763865.009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b9f2/8926474/fb92f1fcb3ab/CIN2022-2763865.010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b9f2/8926474/b85799f1fd4c/CIN2022-2763865.001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b9f2/8926474/7ea25019d072/CIN2022-2763865.002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b9f2/8926474/888c3162085a/CIN2022-2763865.003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b9f2/8926474/6ee96afca25b/CIN2022-2763865.004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b9f2/8926474/1840a4f98b02/CIN2022-2763865.005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b9f2/8926474/911f826519a8/CIN2022-2763865.006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b9f2/8926474/a7625fe87a54/CIN2022-2763865.007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b9f2/8926474/f6690a45ff8d/CIN2022-2763865.008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b9f2/8926474/04fe9120dd9a/CIN2022-2763865.009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b9f2/8926474/fb92f1fcb3ab/CIN2022-2763865.010.jpg

相似文献

1
Force Optimization of Elongated Undulating Fin Robot Using Improved PSO-Based CPG.基于改进的 PSO 的 CPG 的狭长波动鳍机器人的力优化。
Comput Intell Neurosci. 2022 Mar 9;2022:2763865. doi: 10.1155/2022/2763865. eCollection 2022.
2
Reinforcement learning-based optimization of locomotion controller using multiple coupled CPG oscillators for elongated undulating fin propulsion.基于强化学习的运动控制器优化，使用多个耦合的 CPG 振荡器进行细长波动鳍推进。
Math Biosci Eng. 2022 Jan;19(1):738-758. doi: 10.3934/mbe.2022033. Epub 2021 Nov 19.
3
Force scaling and efficiency of elongated median fin propulsion.细长中鳍推进的力缩放和效率。
Bioinspir Biomim. 2022 May 13;17(4). doi: 10.1088/1748-3190/ac6375.
4
Shape memory alloy-driven undulatory locomotion of a soft biomimetic ray robot.形状记忆合金驱动的软仿生射线机器人的波动运动。
Bioinspir Biomim. 2021 Sep 9;16(6). doi: 10.1088/1748-3190/ac03bc.
5
Robotic device shows lack of momentum enhancement for gymnotiform swimmers.机器人装置显示出对电鳗游泳者缺乏动力增强。
Bioinspir Biomim. 2019 Jan 23;14(2):024001. doi: 10.1088/1748-3190/aaf983.
6
Design and experimental evaluation of the novel undulatory propulsors for biomimetic underwater robots.新型仿生水下机器人波动推进器的设计与实验评估。
Bioinspir Biomim. 2021 Jul 26;16(5). doi: 10.1088/1748-3190/ac10b0.
7
Undulating fins produce off-axis thrust and flow structures.波动的鳍片产生了非轴向推力和流动结构。
J Exp Biol. 2014 Jan 15;217(Pt 2):201-13. doi: 10.1242/jeb.091520. Epub 2013 Sep 26.
8
CPG Network Optimization for a Biomimetic Robotic Fish via PSO.通过粒子群算法优化仿生机器鱼的 CPG 网络。
IEEE Trans Neural Netw Learn Syst. 2016 Sep;27(9):1962-8. doi: 10.1109/TNNLS.2015.2459913. Epub 2015 Aug 7.
9
Swimming performance of a bio-inspired robotic vessel with undulating fin propulsion.仿生物机器人船舶的波动鳍推进游泳性能。
Bioinspir Biomim. 2018 Jul 20;13(5):056006. doi: 10.1088/1748-3190/aacd26.
10
Bio-inspired aquatic robotics by untethered piezohydroelastic actuation.无绳压电阻弹性致动的仿生水生机器人。
Bioinspir Biomim. 2013 Mar;8(1):016006. doi: 10.1088/1748-3182/8/1/016006. Epub 2013 Jan 24.

引用本文的文献

1
Kinematic Modeling and Experimental Study of a Rope-Driven Bionic Fish.绳索驱动仿生鱼的运动学建模与实验研究
Biomimetics (Basel). 2024 Jun 7;9(6):345. doi: 10.3390/biomimetics9060345.

本文引用的文献

1
A multi-scale UAV image matching method applied to large-scale landslide reconstruction.一种应用于大规模滑坡重建的多尺度无人机图像匹配方法
Math Biosci Eng. 2021 Mar 5;18(3):2274-2287. doi: 10.3934/mbe.2021115.
2
Artificial lateral line based local sensing between two adjacent robotic fish.基于人工侧线的两条相邻机器鱼之间的局部感应。
Bioinspir Biomim. 2017 Nov 27;13(1):016002. doi: 10.1088/1748-3190/aa8f2e.
3
Feature Selection and Parameters Optimization of SVM Using Particle Swarm Optimization for Fault Classification in Power Distribution Systems.
基于粒子群优化算法的支持向量机在配电网故障分类中的特征选择与参数优化
Comput Intell Neurosci. 2017;2017:4135465. doi: 10.1155/2017/4135465. Epub 2017 Jul 11.
4
CPG Network Optimization for a Biomimetic Robotic Fish via PSO.通过粒子群算法优化仿生机器鱼的 CPG 网络。
IEEE Trans Neural Netw Learn Syst. 2016 Sep;27(9):1962-8. doi: 10.1109/TNNLS.2015.2459913. Epub 2015 Aug 7.
5
Central pattern generators for locomotion control in animals and robots: a review.动物和机器人运动控制中的中枢模式发生器：综述
Neural Netw. 2008 May;21(4):642-53. doi: 10.1016/j.neunet.2008.03.014. Epub 2008 May 14.