Enhancing Legged Robot Locomotion Through Smooth Transitions Using Spiking Central Pattern Generators.

IF 3.4 3区 医学 Q1 ENGINEERING, MULTIDISCIPLINARY
Horacio Rostro-Gonzalez, Erick I Guerra-Hernandez, Patricia Batres-Mendoza, Andres A Garcia-Granada, Miroslava Cano-Lara, Andres Espinal
{"title":"Enhancing Legged Robot Locomotion Through Smooth Transitions Using Spiking Central Pattern Generators.","authors":"Horacio Rostro-Gonzalez, Erick I Guerra-Hernandez, Patricia Batres-Mendoza, Andres A Garcia-Granada, Miroslava Cano-Lara, Andres Espinal","doi":"10.3390/biomimetics10060381","DOIUrl":null,"url":null,"abstract":"<p><p>In this work, we propose the integration of a mechanism to enable smooth transitions between different locomotion patterns in a hexapod robot. Specifically, we utilize a spiking neural network (SNN) functioning as a Central Pattern Generator (CPG) to generate three distinct locomotion patterns, or gaits: walk, jog, and run. This network produces coordinated spike trains, mimicking those generated in the brain, which are translated into synchronized robot movements via PWM signals. Subsequently, these spike trains are compared using a similarity metric known as SPIKE-synchronization to identify the optimal point for transitioning from one gait to another. This approach aims to achieve three main objectives: first, to maintain the robot's balance during transitions; second, to ensure that gait transitions are almost imperceptible; and third, to improve energy efficiency by reducing abrupt changes in the robot's actuators (servomotors). To validate our proposal, we incorporated FSR sensors on the robot's legs to detect the rigidity of the terrain it navigates. Based on the terrain's rigidity, the robot dynamically transitions between gaits. The system was tested in real time on a physical hexapod robot across four different types of terrain. Although the method was validated exclusively on a hexapod robot, it can be extended to any legged robot.</p>","PeriodicalId":8907,"journal":{"name":"Biomimetics","volume":"10 6","pages":""},"PeriodicalIF":3.4000,"publicationDate":"2025-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12190837/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Biomimetics","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.3390/biomimetics10060381","RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, MULTIDISCIPLINARY","Score":null,"Total":0}
引用次数: 0

Abstract

In this work, we propose the integration of a mechanism to enable smooth transitions between different locomotion patterns in a hexapod robot. Specifically, we utilize a spiking neural network (SNN) functioning as a Central Pattern Generator (CPG) to generate three distinct locomotion patterns, or gaits: walk, jog, and run. This network produces coordinated spike trains, mimicking those generated in the brain, which are translated into synchronized robot movements via PWM signals. Subsequently, these spike trains are compared using a similarity metric known as SPIKE-synchronization to identify the optimal point for transitioning from one gait to another. This approach aims to achieve three main objectives: first, to maintain the robot's balance during transitions; second, to ensure that gait transitions are almost imperceptible; and third, to improve energy efficiency by reducing abrupt changes in the robot's actuators (servomotors). To validate our proposal, we incorporated FSR sensors on the robot's legs to detect the rigidity of the terrain it navigates. Based on the terrain's rigidity, the robot dynamically transitions between gaits. The system was tested in real time on a physical hexapod robot across four different types of terrain. Although the method was validated exclusively on a hexapod robot, it can be extended to any legged robot.

通过使用尖峰中心模式发生器平滑过渡增强腿式机器人运动。
在这项工作中,我们提出了一种机制的集成,以实现六足机器人中不同运动模式之间的平滑过渡。具体来说,我们利用脉冲神经网络(SNN)作为中枢模式生成器(CPG)来生成三种不同的运动模式或步态:步行、慢跑和跑步。这个网络产生协调的尖峰序列,模仿大脑中产生的尖峰序列,通过PWM信号转化为同步的机器人运动。随后,使用称为spike -synchronization的相似性度量来比较这些spike序列,以确定从一种步态过渡到另一种步态的最佳点。该方法旨在实现三个主要目标:第一,在过渡期间保持机器人的平衡;第二,确保步态转换几乎难以察觉;第三,通过减少机器人执行器(伺服电机)的突然变化来提高能源效率。为了验证我们的建议,我们在机器人的腿上安装了FSR传感器,以检测它所导航的地形的刚度。基于地形的刚度,机器人在步态之间动态转换。该系统在四种不同地形的物理六足机器人上进行了实时测试。虽然该方法仅在六足机器人上进行了验证,但它可以扩展到任何有腿的机器人。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
Biomimetics
Biomimetics Biochemistry, Genetics and Molecular Biology-Biotechnology
CiteScore
3.50
自引率
11.10%
发文量
189
审稿时长
11 weeks
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:604180095
Book学术官方微信