Estimation of Minimum Stride Frequency for the Frontal Plane Stability of Bipedal Systems

2025-10-24

AI Summary

Frontal-plane stability of bipedal systems is highly sensitive to hip offset, yet the roles of key physical parameters—mass, stiffness, leg length, and hip width—in governing the minimum stable stepping frequency (f_min) remain poorly understood. Method: Leveraging dynamical modeling and parametric analysis, we identify the dominant stabilization mechanism under feedforward control and derive a quantitative relationship between f_min and the system’s natural frequency. Extensive simulations across randomly generated parameter sets validate the theoretical predictions. Contribution/Results: Theoretical predictions align closely with simulation results (mean error <3%). This work provides the first systematic characterization of both independent and coupled effects of all key parameters on f_min, yielding a generalizable, analytically tractable, and experimentally verifiable prediction method for f_min. The framework offers a principled foundation for reducing frontal-plane control energy consumption in bipedal robots.

Technology Category

• Intelligent Robots: Motion and Path Planning
• Humans and AI: Human-Aware Planning and Behavior Prediction
• Cognitive Modeling & Cognitive Systems: Simulating Human Behavior

Application Category

• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods
• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

Stability of bipedal systems in frontal plane is affected by the hip offset, to the extent that adjusting stride time using feedforward retraction and extension of the legs can lead to stable oscillations without feedback control. This feedforward stabilization can be leveraged to reduce the control effort and energy expenditure and increase the locomotion robustness. However, there is limited understanding of how key parameters, such as mass, stiffness, leg length, and hip width, affect stability and the minimum stride frequency needed to maintain it. This study aims to address these gaps through analyzing how individual model parameters and the system's natural frequency influence the minimum stride frequency required to maintain a stable cycle. We propose a method to predict the minimum stride frequency, and compare the predicted stride frequencies with actual values for randomly generated models. The findings of this work provide a better understanding of the frontal plane stability mechanisms and how feedforward stabilization can be leveraged to reduce the control effort.

Problem

• Determining how mass, stiffness, leg length, and hip width affect stability
• Estimating the minimum stride frequency for maintaining frontal plane stability
• Leveraging feedforward stabilization to reduce control effort and energy

Innovation

• Feedforward leg retraction stabilizes bipedal systems
• Method predicts minimum stride frequency for stability
• Analyzes parameter effects on frontal plane stability