A generic walking pattern generation method for humanoid robot walking on the slopes

Author(s):  
Fayong Guo ◽  
Tao Mei ◽  
Marco Ceccarelli ◽  
Ziyi Zhao ◽  
Tao Li ◽  
...  

Purpose Walking on inclined ground is an important ability for humanoid robots. In general, conventional strategies for walking on slopes lack technical analysis in, first, the waist posture with respect to actual robot and, second, the landing impact, which weakens the walking stability. The purpose of this paper is to propose a generic method for walking pattern generation considering these issues with the aim of enabling humanoid robot to walk dynamically on a slope. Design/methodology/approach First, a virtual ground method (VGM) is proposed to give a continuous and intuitive zero-moment point (ZMP) on slopes. Then, the dynamic motion equations are derived based on 2D and 3D models, respectively, by using VGM. Furthermore, the waist posture with respect to the actual robot is analyzed. Finally, a reformative linear inverted pendulum (LIP) named the asymmetric linear inverted pendulum (ALIP) is proposed to achieve stable and dynamical walking in any direction on a slope with lower landing impact. Findings Simulations and experiments are carried out using the DRC-XT humanoid robot platform with the aim of verifying the validity and feasibility of these new methods. ALIP with consideration of waist posture is practical in extending the ability of walking on slopes for humanoid robots. Originality/value A generic method called ALIP for humanoid robots walking on slopes is proposed. ALIP is based on LIP and several changes, including model analysis, motion equations and ZMP functions, are discussed.

2018 ◽  
Vol 10 (9) ◽  
pp. 168781401880088
Author(s):  
Liang Yang ◽  
Zhi Liu ◽  
Yong Chen

This article concentrates on the problem of walking pattern generation and online control for humanoid robot. However, it is challenging and thus still remains open so far in the field of bipedal locomotion control. In this article, we solve this problem by proposing a bivariate-stability-margin-based control scheme, in which a random vector function-link neural networks mechanism is additionally contained. By utilizing opposition-based learning algorithm to generate walking patterns and designing random vector function-link neural networks for compensating the combination of zero-moment point error and modeling error, the new walking controller exhibits good performance. Moreover, a bivariate-stability-margin-based fuzzy logic system is proposed to assign a weight to each training sample according to locomotion stability. With these results, a walking control system is successfully established and experiments validate the proposed control scheme.


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