Frontal plane algorithms for dynamic bipedal walking

Authors:
Chee-Meng Chew;Gill A. Pratt
Affiliations:
Department of Mechanical Engineering, National University of Singapore, 10 Kent Ridge Crescent, Singapore 119260.;F.W. Olin College of Engineering, 1735 Great Plain Ave., Needham, MA 02492–1245 (USA)
Venue:
Robotica
Year:
2004

Citing 4
Cited 2

Legged robots that balance

Legged robots that balance
Introduction to Reinforcement Learning

Introduction to Reinforcement Learning
A simulation study of bipedal walking robots: modeling, walking algorithms, and neural network control

A simulation study of bipedal walking robots: modeling, walking algorithms, and neural network control
Dynamic bipedal walking assisted by learning

Robotica

An effective trajectory generation method for bipedal walking

Robotics and Autonomous Systems
Truncated fourier series formulation for bipedal walking balance control

Robotica

Quantified Score

Hi-index	0.00

Visualization

Abstract

This paper presents two frontal plane algorithms for 3D dynamic bipedal walking. One of which is based on the notion of symmetry and the other uses reinforcement learning algorithm to learn the lateral foot placement. The algorithms are combined with a sagittal plane algorithm and successfully applied to a simulated 3D bipedal robot to achieve level ground walking. The simulation results showed that the choice of the local control law for the stance-ankle roll joint could significantly affect the performance of the frontal plane algorithms.