Model Analysis And Design Of Ellipse Based Segmented Varying Curved Foot For Biped Robot Walking

TL;DR

This paper introduces an ellipse-based segmented curved foot for bipeds, improving energy efficiency and gait control, validated through simulations and real-world experiments on a robot.

Contribution

It presents a novel analytical model and design methodology for an energy-efficient bipedal foot inspired by human anatomy, with experimental validation.

Findings

Up to 18.52% energy savings in lateral walking

Reduced energy consumption compared to line and flat feet

Validated through simulation and physical experiments

Abstract

This paper presents the modeling, design, and experimental validation of an Ellipse-based Segmented Varying Curvature (ESVC) foot for bipedal robots. Inspired by the segmented curvature rollover shape of human feet, the ESVC foot aims to enhance gait energy efficiency while maintaining analytical tractability for foot location based controller. First, we derive a complete analytical contact model for the ESVC foot by formulating spatial transformations of elliptical segments only using elementary functions. Then a nonlinear programming approach is engaged to determine optimal elliptical parameters of hind foot and fore foot based on a known mid-foot. An error compensation method is introduced to address approximation inaccuracies in rollover length calculation. The proposed ESVC foot is then integrated with a Hybrid Linear Inverted Pendulum model-based walking controller and validated through both simulation and physical experiments on the TT II biped robot. Experimental results across marking time, sagittal, and lateral walking tasks show that the ESVC foot consistently reduces energy consumption compared to line, and flat feet, with up to 18.52\% improvement in lateral walking. These findings demonstrate that the ESVC foot provides a practical and energy-efficient alternative for real-world bipedal locomotion. The proposed design methodology also lays a foundation for data-driven foot shape optimization in future research.