Humanoid state estimation using a moving horizon estimator

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In this research, a new state estimator based on moving horizon estimation theory is suggested for the humanoid robot state estimation. So far, there are almost no studies on the moving horizon estimator (MHE)-based humanoid state estimator. Instead, a large number of humanoid state estimators based on the Kalman filter (KF) have been proposed. However, such estimators cannot guarantee optimality when the system model is nonlinear or when there is a non-Gaussian modeling error. In addition, with KF, it is difficult to incorporate inequality constraints. Since a humanoid is a complex system, its mathematical model is normally nonlinear, and is limited in its ability to characterize the system accurately. Therefore, KF-based humanoid state estimation has unavoidable limitations. To overcome these limitations, we propose a new approach to humanoid state estimation by using a MHE. It can accommodate not only nonlinear systems and constraints, but also it can partially cope with non-Gaussian modeling error. The proposed estimator framework facilitates the use of a simple model, even in the presence of a large modeling error. In addition, it can estimate the humanoid state more accurately than a KF-based estimator. The performance of the proposed approach was verified experimentally.
Publisher
TAYLOR & FRANCIS LTD
Issue Date
2017-05
Language
English
Article Type
Article
Keywords

SYSTEMS

Citation

ADVANCED ROBOTICS, v.31, no.13, pp.695 - 705

ISSN
0169-1864
DOI
10.1080/01691864.2017.1326317
URI
http://hdl.handle.net/10203/224899
Appears in Collection
ME-Journal Papers(저널논문)
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