Drift compensation of a holonomic mobile robot using recurrent neural networks

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dc.authoridBaran, Eray/0000-0002-6300-061X|Canbek, Oguz/0000-0003-3917-6444
dc.authorwosidBaran, Eray/U-3499-2019
dc.contributor.authorCanbek, Kansu Oguz
dc.contributor.authorYalcin, Hulya
dc.contributor.authorBaran, Eray A.
dc.date.accessioned2024-07-18T20:42:18Z
dc.date.available2024-07-18T20:42:18Z
dc.date.issued2022
dc.departmentİstanbul Bilgi Üniversitesien_US
dc.description.abstractMecanum wheeled robots can exhibit serious slippage problems because of the discontinuous contact between the wheels and the ground which negatively influences the overall navigation quality. Addressing this problem, the aim of this paper is to demonstrate how a learning-based method can be used for the estimation of the drifting error from multiple sensors with distinct measurement types. Here, a recurrent neural network (RNN)-based drift compensation algorithm is proposed for the estimation of the positioning drift. In order to improve the positioning performance in dead reckoning the estimated drift is used within the real-time control loop for proper modification of the motion trajectory. During the training phase, the data acquired from the acceleration sensors attached to the robot chassis and the encoders of the wheels of the robot are used as the main features to train a gated recurrent unit-based RNN. The drift estimator is trained using the computer-generated reference position data, and the response position data which is measured using an optoelectronic motion tracking device. The performance of the proposed learning-based drift estimation and control algorithm is validated through a series of experiments. The responses obtained from the experiments are graphically illustrated and the improvements in the positioning performances are numerically evaluated. The results obtained from the experiments illustrate the effective performance of the proposed algorithm by considerably decreasing the positioning errors.en_US
dc.description.sponsorshipIstanbul Bilgi University [RDI.2020.1]en_US
dc.description.sponsorshipThis study is partially supported by the Internal Research Grant RDI.2020.1 of Istanbul Bilgi University.en_US
dc.identifier.doi10.1007/s11370-022-00430-w
dc.identifier.endpage409en_US
dc.identifier.issn1861-2776
dc.identifier.issn1861-2784
dc.identifier.issue3en_US
dc.identifier.scopus2-s2.0-85132799827en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage399en_US
dc.identifier.urihttps://doi.org/10.1007/s11370-022-00430-w
dc.identifier.urihttps://hdl.handle.net/11411/7236
dc.identifier.volume15en_US
dc.identifier.wosWOS:000817055500001en_US
dc.identifier.wosqualityQ4en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherSpringer Heidelbergen_US
dc.relation.ispartofIntelligent Service Roboticsen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectOdometryen_US
dc.subjectMobile Roboten_US
dc.subjectRecurrent Neural Networken_US
dc.subjectDrift Compensationen_US
dc.subjectGated Recurrent Uniten_US
dc.subjectDisturbance-Observeren_US
dc.subjectNavigation Systemen_US
dc.subjectDesignen_US
dc.subjectFilteren_US
dc.subjectImuen_US
dc.titleDrift compensation of a holonomic mobile robot using recurrent neural networks
dc.typeArticle

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