DC Field | Value | Language |
---|---|---|
dc.contributor.author | Bae, Hyoin | ko |
dc.contributor.author | Oh, Jun-Ho | ko |
dc.date.accessioned | 2017-12-19T00:57:43Z | - |
dc.date.available | 2017-12-19T00:57:43Z | - |
dc.date.created | 2017-11-28 | - |
dc.date.created | 2017-11-28 | - |
dc.date.issued | 2017-12 | - |
dc.identifier.citation | ROBOTICS AND AUTONOMOUS SYSTEMS, v.98, pp.258 - 275 | - |
dc.identifier.issn | 0921-8890 | - |
dc.identifier.uri | http://hdl.handle.net/10203/228432 | - |
dc.description.abstract | This study proposes a new Kalman filter-based framework for humanoid robot state estimation. The conventional Kalman filter generates optimal estimation solutions only when the nominal equations of the model and measurement include zero-mean, uncorrelated, white Gaussian noise. Because a humanoid robot is a complex system with multiple degrees of freedom, its mathematical model is limited in terms of expressing the system accurately, resulting in the generation of non-zero-mean, non-Gaussian, correlated modeling errors. Therefore, it is difficult to obtain accurate state estimates if the conventional Kalman filter-based approaches are used with such inexact humanoid models. The proposed modified Kalman filter framework consists of two loops: a loop to estimate the state, and a loop to estimate the disturbance generated by the modeling errors (a dual-loop Kalman filter). The disturbance values estimated by the disturbance estimation loop are provided as feedback to the state estimation loop, thereby improving the accuracy of the model-based prediction process. By considering the correlation between the state and disturbance in the estimation process, the disturbance can be accurately estimated. Therefore, the proposed estimator allows the use of a simple model, even if it implies the presence of a large modeling error. In addition, it can estimate the humanoid state more accurately than the conventional Kalman filter. Furthermore, the proposed filter has a simpler structure than the existing robust Kalman filters, which require the solution of complex Riccati equations; hence, it can facilitate recursive online implementation. The performance and characteristics of the proposed filter are verified by comparison with other existing linear/nonlinear estimators using simple examples and simulations. Furthermore, the feasibility of the proposed filter is verified by implementing it on a real humanoid robot platform. (C) 2017 Elsevier B.V. All rights reserved. | - |
dc.language | English | - |
dc.publisher | ELSEVIER SCIENCE BV | - |
dc.subject | PERTURBATION OBSERVER | - |
dc.subject | UNCERTAIN SYSTEMS | - |
dc.subject | MOTION CONTROL | - |
dc.subject | DESIGN | - |
dc.title | Novel state estimation framework for humanoid robot | - |
dc.type | Article | - |
dc.identifier.wosid | 000414881500019 | - |
dc.identifier.scopusid | 2-s2.0-85032298393 | - |
dc.type.rims | ART | - |
dc.citation.volume | 98 | - |
dc.citation.beginningpage | 258 | - |
dc.citation.endingpage | 275 | - |
dc.citation.publicationname | ROBOTICS AND AUTONOMOUS SYSTEMS | - |
dc.identifier.doi | 10.1016/j.robot.2017.09.021 | - |
dc.contributor.localauthor | Oh, Jun-Ho | - |
dc.description.isOpenAccess | N | - |
dc.type.journalArticle | Article | - |
dc.subject.keywordAuthor | Humanoid state estimation | - |
dc.subject.keywordAuthor | Robust Kalman filtering | - |
dc.subject.keywordAuthor | Modeling error compensation | - |
dc.subject.keywordAuthor | Humanoid robot | - |
dc.subject.keywordPlus | PERTURBATION OBSERVER | - |
dc.subject.keywordPlus | UNCERTAIN SYSTEMS | - |
dc.subject.keywordPlus | MOTION CONTROL | - |
dc.subject.keywordPlus | DESIGN | - |
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.