Abstract
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.
Highlights
Research of walking control for humanoid robot has collected a great deal of attention over the past decades.[1,2,3,4,5,6,7,8] Roughly, these approaches can be classified into two groups, namely, model-based method and stability-criterion-based method
To evaluate the locomotion stability during robot walks, Zero-moment point (ZMP) stability margin and yaw moment stability margin are designed as follows, which were first proposed in Yang et al.[27]
The whole walking control scheme is composed of three parts: biped robot BRZ-4, the ground workstation, and onboard system, respectively
Summary
Research of walking control for humanoid robot has collected a great deal of attention over the past decades.[1,2,3,4,5,6,7,8] Roughly, these approaches can be classified into two groups, namely, model-based method and stability-criterion-based method. By combining polynomial regression technique with principal component analysis, an online walking pattern generation algorithm with a low number of parameters was further proposed in Gasparri et al.[17] Despite these contributions, the methods established in the literature[13,14,15] have a common assumption that yaw moment can be ignored during robot walks. To evaluate the locomotion stability during robot walks, ZMP stability margin and yaw moment stability margin are designed as follows, which were first proposed in Yang et al.[27]. In supervised learning tasks, the control performance usually suffers from data uncertainties and imbalanced data distribution To remove this obstacle, we assign a weight to each sample according to bivariate stability margin, which are ZMP stability margin and yaw moment margin. An adaptive controller in Yang et al.[20] is developed to track the desired trajectories
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