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生物启发的中央模式生成器,用于在斜坡表面的人形机器人中产生适应式步态和稳定性.

Junwei Fang^1,2, Yinglian Jin³, Binrui Wang^3,4

¹College of Metrology Measurement and Instrument, China Jiliang University, Hangzhou 310018, China.

Biomimetics (Basel, Switzerland)

|September 26, 2025

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这项研究引入了一种新的生物中心模式生成器 (CPG) 用于人形机器人步行,优化自然运动和稳定的参数. 前体反射增强了在斜坡上的适应能力,实现了类似人类的能源效率和强大的性能.

科学领域:

机器人技术机器人技术机器人技术
生物仿真工程生物仿真工程
计算神经科学是一种神经科学.

背景情况:

人形机器人运动研究已经取得了稳定性,但缺乏自然的,类似人类的腿部运动和动态适应能力.
现有的中央模式生成器 (CPG) 方法在参数解释,神经元合和脱方面面临挑战.

研究的目的:

为人形机器人开发一种先进的CPG步态生成方法.
为了增强机器人行走的自然性,协调性和动态稳定性.
提高适应能力在变化的地形,特别是斜坡.

主要方法:

提出了一种使用基穆拉神经元的生物CPG步态生成方法,将CPG输出映射到质量中心 (CoM) 和脚轨迹.
使用整合动态平衡和步骤约束的目标函数系统优化了22个CPG参数.
设计了一个基于Tegotae理论的前置反射机制,用于在斜坡上实时调整姿势.

主要成果:

优化的CPG参数增强了步行的自然性和协调性.
前体反射机制在斜坡过渡期间显著改善了机器人的稳定性.
机器人步行的能量消耗模式与人类的自然运动非常相匹配.

关键词:

适应性行走是一种适应性行走.中央模式发生器受约束的进化优化受约束的进化优化人形机器人的人形机器人反射调节反射调节

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结论:

拟议的生物CPG具有参数优化和前体反射,可以实现更自然和稳定的人形机器人运动.
该方法在各种地形上显示出出色的稳定性和环境适应性.
这种方法为开发高度适应性和类似人类的机器人运动提供了有希望的方向.