Bio-Inspired Central Pattern Generator for Adaptive Gait Generation and Stability in Humanoid Robots on Sloped Surfaces.

IF 3.9 3区 医学 Q1 ENGINEERING, MULTIDISCIPLINARY
Junwei Fang, Yinglian Jin, Binrui Wang, Kun Zhou, Mingrui Wang, Ziqi Liu
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Abstract

Existing research has preliminarily achieved stable walking in humanoid robots; however, natural human-like leg motion and adaptive capabilities in dynamic environments remain unattained. This paper proposes a bionic central pattern generator (CPG) gait generation method based on Kimura neurons. The method maps the CPG output to the spatial motion patterns of the robot's center of mass (CoM) and foot trajectory, modulated by 22 undetermined parameters. To address the vague physical interpretation of CPG parameters, the strong neuronal coupling, and the difficulty of decoupling, this research systematically optimized the CPG parameters by defining an objective function that integrates dynamic balance performance with step constraints, thereby enhancing the naturalness and coordination of gait generation. To further enhance the walking stability of the robot under varying road curvatures, a vestibular reflex mechanism was designed based on the Tegotae theory, enabling real-time posture adjustment during slope walking. To validate the proposed approach, a virtual simulation platform and a physical humanoid robot system were constructed to comparatively evaluate motion performance on flat terrain and slopes with different gradients. The results show that the energy consumption characteristics of robot-coordinated gait are highly consistent with the energy-saving mechanism of human natural motion. In addition, the established reflection mechanism significantly improves the motion stability of the robot in slope transition, and its excellent stability margin and environmental adaptability are verified by simulation and experiment.

仿生中心模式生成器用于斜坡上仿人机器人的自适应步态生成和稳定性。
现有研究已经初步实现了人形机器人的稳定行走;然而,在动态环境中,自然的类人腿运动和适应能力仍未实现。提出了一种基于木村神经元的仿生中枢模式生成器(CPG)步态生成方法。该方法将CPG输出映射到机器人质心(CoM)和足部轨迹的空间运动模式,由22个待定参数调制。针对CPG参数物理解释模糊、神经元耦合强、解耦困难等问题,本研究通过定义动态平衡性能与步长约束相结合的目标函数,对CPG参数进行了系统优化,增强了步态生成的自然性和协调性。为了进一步提高机器人在不同道路曲率下的行走稳定性,基于Tegotae理论设计了前庭反射机制,实现了机器人在斜坡行走时的实时姿态调整。为了验证该方法的有效性,搭建了虚拟仿真平台和实体仿人机器人系统,对比评估了仿人机器人在不同坡度的平坦地形和斜坡上的运动性能。结果表明,机器人协调步态的能量消耗特征与人类自然运动的节能机制高度一致。此外,所建立的反射机制显著提高了机器人在斜坡过渡中的运动稳定性,并通过仿真和实验验证了其良好的稳定裕度和环境适应性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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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
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