Author:
Ho Ya-Fang,Li Tzuu-Hseng S.,Kuo Ping-Huan,Ye Yan-Ting
Abstract
AbstractThis paper presents a parameterized gait generator based on linear inverted
pendulum model (LIPM) theory, which allows users to generate a natural gait
pattern with desired step sizes. Five types of zero moment point (ZMP)
components are proposed for formulating a natural ZMP reference, where ZMP moves
continuously during single support phases instead of staying at a fixed point in
the sagittal and lateral plane. The corresponding center of mass (CoM)
trajectories for these components are derived by LIPM theory. To generate a
parameterized gait pattern with user-defined parameters, a gait planning
algorithm is proposed, which determines related coefficients and boundary
conditions of the CoM trajectory for each step. The proposed parameterized gait
generator also provides a concept for users to generate gait patterns with
self-defined ZMP references by using different components. Finally, the
feasibility of the proposed method is validated by the experimental results with
a teen-sized humanoid robot, David, which won first place in the sprint event at
the 20th Federation of International Robot-soccer Association (FIRA) RoboWorld
Cup.
Publisher
Cambridge University Press (CUP)
Subject
Artificial Intelligence,Software
Reference21 articles.
1. Zero-moment point trajectory modelling of a biped walking robot using an adaptive neuro-fuzzy system
2. Online bio-inspired trajectory generation of seven-link biped robot based on T–S fuzzy system
3. ZMP-Based Biped Running Control
4. Design and implementation of fuzzy policy
gradient gait learning method for walking pattern generation of humanoid
robots;Su;International Journal of Fuzzy
Systems,2011
5. Tedrake R. , Zhang T. W. & Seung H. S. 2004. Stochastic policy gradient reinforcement learning on a simple 3D biped. In Proceedings of 2004 IEEE/RSJ International Conference on Intelligent Robots and Systems, 3, 2849–2854.
Cited by
8 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献