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通过基于Tegotae的反馈实现CPG中的自适应与节能最优控制。

Adaptive and Energy-Efficient Optimal Control in CPGs Through Tegotae-Based Feedback.

作者信息

Zamboni Riccardo, Owaki Dai, Hayashibe Mitsuhiro

机构信息

Politecnico di Milano, Milan, Italy.

Department of Robotics, Graduate School of Engineering, Tohoku University, Sendai, Japan.

出版信息

Front Robot AI. 2021 May 26;8:632804. doi: 10.3389/frobt.2021.632804. eCollection 2021.

DOI:10.3389/frobt.2021.632804
PMID:34124172
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC8187776/
Abstract

To obtain biologically inspired robotic control, the architecture of central pattern generators (CPGs) has been extensively adopted to generate periodic patterns for locomotor control. This is attributed to the interesting properties of nonlinear oscillators. Although sensory feedback in CPGs is not necessary for the generation of patterns, it plays a central role in guaranteeing adaptivity to environmental conditions. Nonetheless, its inclusion significantly modifies the dynamics of the CPG architecture, which often leads to bifurcations. For instance, the force feedback can be exploited to derive information regarding the state of the system. In particular, the approach can be adopted by coupling proprioceptive information with the state of the oscillation itself in the CPG model. This paper discusses this policy with respect to other types of feedback; it provides higher adaptivity and an optimal energy efficiency for reflex-like actuation. We believe this is the first attempt to analyse the optimal energy efficiency along with the adaptivity of the Tegotae approach.

摘要

为了获得受生物启发的机器人控制，中央模式发生器（CPG）的架构已被广泛用于生成用于运动控制的周期性模式。这归因于非线性振荡器的有趣特性。虽然在模式生成过程中CPG中的感觉反馈不是必需的，但它在保证对环境条件的适应性方面起着核心作用。尽管如此，它的加入会显著改变CPG架构的动力学，这通常会导致分岔。例如，力反馈可用于获取有关系统状态的信息。特别是，通过将本体感受信息与CPG模型中振荡本身的状态相结合，可以采用这种方法。本文讨论了这种策略相对于其他类型反馈的情况；它为类似反射的驱动提供了更高的适应性和最佳的能量效率。我们相信这是首次尝试分析Tegotae方法的最佳能量效率及其适应性。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/53c4/8187776/f1b8c306c2a7/frobt-08-632804-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/53c4/8187776/aac84f374e35/frobt-08-632804-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/53c4/8187776/219fe2f65e09/frobt-08-632804-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/53c4/8187776/5b949187ee42/frobt-08-632804-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/53c4/8187776/13acf27e30a3/frobt-08-632804-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/53c4/8187776/3358c93aef05/frobt-08-632804-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/53c4/8187776/41443b151a7c/frobt-08-632804-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/53c4/8187776/41b157310573/frobt-08-632804-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/53c4/8187776/2917f16f7262/frobt-08-632804-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/53c4/8187776/f1b8c306c2a7/frobt-08-632804-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/53c4/8187776/aac84f374e35/frobt-08-632804-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/53c4/8187776/219fe2f65e09/frobt-08-632804-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/53c4/8187776/5b949187ee42/frobt-08-632804-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/53c4/8187776/13acf27e30a3/frobt-08-632804-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/53c4/8187776/3358c93aef05/frobt-08-632804-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/53c4/8187776/41443b151a7c/frobt-08-632804-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/53c4/8187776/41b157310573/frobt-08-632804-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/53c4/8187776/2917f16f7262/frobt-08-632804-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/53c4/8187776/f1b8c306c2a7/frobt-08-632804-g009.jpg

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本文引用的文献

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Tegotae-Based Control Produces Adaptive Inter- and Intra-limb Coordination in Bipedal Walking.基于Tegotae的控制在双足行走中产生适应性的肢体间和肢体内部协调。
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2
Integrative Biomimetics of Autonomous Hexapedal Locomotion.自主六足运动的整合仿生学
Front Neurorobot. 2019 Oct 23;13:88. doi: 10.3389/fnbot.2019.00088. eCollection 2019.
3
Designing higher fourier harmonics of Tegotae function using genetic algorithm-a case study with an earthworm locomotion.
利用遗传算法设计 Tegotae 函数的更高阶傅里叶谐波——以蚯蚓运动为例的研究
Bioinspir Biomim. 2019 Aug 13;14(5):054001. doi: 10.1088/1748-3190/ab2fab.
4
Simple analytical model reveals the functional role of embodied sensorimotor interaction in hexapod gaits.简单的分析模型揭示了具身感觉运动交互在六足动物步态中的功能作用。
PLoS One. 2018 Feb 28;13(2):e0192469. doi: 10.1371/journal.pone.0192469. eCollection 2018.
5
A Minimal Model Describing Hexapedal Interlimb Coordination: The Tegotae-Based Approach.一种描述六足动物肢体间协调的最小模型：基于Tegotae的方法。
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6
Tegotae-based decentralised control scheme for autonomous gait transition of snake-like robots.基于 Tegotae 的蛇形机器人自主步态过渡分散式控制方案。
Bioinspir Biomim. 2017 Aug 4;12(4):046009. doi: 10.1088/1748-3190/aa7725.
7
Fast Dynamical Coupling Enhances Frequency Adaptation of Oscillators for Robotic Locomotion Control.快速动态耦合增强振荡器的频率适应性以用于机器人运动控制。
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A Quadruped Robot Exhibiting Spontaneous Gait Transitions from Walking to Trotting to Galloping.一种能够自主实现从行走、小跑至奔腾步态转换的四足机器人
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9
Synaptic plasticity in a recurrent neural network for versatile and adaptive behaviors of a walking robot.用于步行机器人通用和自适应行为的循环神经网络中的突触可塑性。
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