Adaptive motion pattern generation on balancing of humanoid robot movement

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.

Download Full-text

Stretch Walking Pattern Generation for a Biped Humanoid Robot

TRANSACTIONS OF THE JAPAN SOCIETY OF MECHANICAL ENGINEERS Series C ◽

10.1299/kikaic.70.3509 ◽

2004 ◽

Vol 70 (700) ◽

pp. 3509-3515 ◽

Cited By ~ 4

Author(s):

Yu OGURA ◽

Hun-ok LIM ◽

Atsuo TAKANISHI

Keyword(s):

Humanoid Robot ◽

Pattern Generation ◽

Walking Pattern ◽

Walking Pattern Generation

Download Full-text

G151014 Walking Stability Control and Pattern Generation for Biped Humanoid Robot

The Proceedings of Mechanical Engineering Congress Japan ◽

10.1299/jsmemecj.2013._g151014-1 ◽

2013 ◽

Vol 2013 (0) ◽

pp. _G151014-1-_G151014-4

Author(s):

Yuki Kamogawa ◽

Kouhei Yamada ◽

Hiroyuki Masuta ◽

Hun-ok Lim

Keyword(s):

Humanoid Robot ◽

Stability Control ◽

Pattern Generation

Download Full-text

Optimal gait planning for humanoids with 3D structure walking on slippery surfaces

Robotica ◽

10.1017/s0263574715000715 ◽

2015 ◽

Vol 35 (3) ◽

pp. 569-587 ◽

Cited By ~ 15

Author(s):

Majid Khadiv ◽

S. Ali A. Moosavian ◽

Aghil Yousefi-Koma ◽

Majid Sadedel ◽

Saeed Mansouri

Keyword(s):

Coefficient Of Friction ◽

Humanoid Robot ◽

3D Structure ◽

Optimization Procedure ◽

Pattern Generation ◽

Friction Forces ◽

Gait Planning ◽

Walking Pattern ◽

Planning Procedure ◽

Optimal Gait

SUMMARYIn this study, a gait optimization routine is developed to generate walking patterns which demand the lowest friction forces for implementation. The aim of this research is to fully address the question “which walking pattern demands the lowest coefficient of friction amongst all feasible patterns?”. To this end, first, the kinematic structure of the considered 31 DOF (Degrees of Freedom) humanoid robot is investigated and a closed-form dynamics model for its lower-body is developed. Then, the medium through which the walking pattern generation is conducted is presented. In this medium, after designing trajectories for the feet and the pelvis, the joint space variables are obtained, using the inverse kinematics. Finally, by employing a genetic algorithm (GA), an optimization process is conducted to generate walking patterns with the minimum Required Coefficient Of Friction (RCOF). Six parameters are adopted to parameterize the pelvis trajectory and are exploited as the design variables in this optimization procedure. Also, a parametrical study is accomplished to address the effects of some other variables on RCOF. For comparison purposes, a tip-over Stability Margin (SM) is defined, and an optimization procedure is conducted to maximize this margin. Finally, the proposed gait planning procedure is implemented on SURENA III, a humanoid robot designed and fabricated in CAST, to validate the developed simulation procedure. The obtained results reveal merits of the proposed optimal gait planning procedure in terms of RCOF.

Download Full-text