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dc.contributor.authorLi, C
dc.contributor.authorZhu, S
dc.contributor.authorSun, Z
dc.contributor.authorRogers, J
dc.date.accessioned2020-11-04T15:03:36Z
dc.date.available2020-11-04T15:03:36Z
dc.date.issued2020-10-29
dc.identifier.issn1549-7747
dc.identifier.issn1558-3791
dc.identifier.urihttp://hdl.handle.net/10026.1/16629
dc.descriptionNo embargo required.
dc.description.abstract

In this brief, an enhanced robotic learning interface has been investigated using Beetle Antennae Search (BAS) and Extreme Learning Machine (ELM). The initial values of learning weights and bias of the network have significant effect on the performance of the ELM, hence, BAS algorithm was employed to optimize the initial values of learning weights and bias. Kinect v2 camera sensor was applied to obtain the endpoint's position of the upper limb, MYO armband was used to measure the corresponding joint angle values. Those aforementioned data formed the dataset to be trained by ELM and after training the ELM model was able to generate angle values by only giving position as input without a need to carry out kinematic calculations. The proposed method has been validated by conducting series of experimental studies on a KUKA iiwa robot.

dc.format.extent1-1
dc.language.isoen
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectTraining
dc.subjectOptimization
dc.subjectGenetic algorithms
dc.subjectRobots
dc.subjectCameras
dc.subjectExtreme learning machine
dc.subjectbeetle antennae search
dc.subjectMYO armband
dc.subjectKinect v2
dc.subjectKUKA iiwa robot
dc.titleBAS Optimized ELM for KUKA iiwa Robot Learning
dc.typejournal-article
dc.typeArticle
plymouth.author-urlhttps://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000655844400050&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=11bb513d99f797142bcfeffcc58ea008
plymouth.issue6
plymouth.volume68
plymouth.publisher-urlhttp://dx.doi.org/10.1109/tcsii.2020.3034771
plymouth.publication-statusPublished
plymouth.journalIEEE Transactions on Circuits and Systems II: Express Briefs
dc.identifier.doi10.1109/tcsii.2020.3034771
plymouth.organisational-group/Plymouth
plymouth.organisational-group/Plymouth/Faculty of Science and Engineering
plymouth.organisational-group/Plymouth/Faculty of Science and Engineering/School of Engineering, Computing and Mathematics
plymouth.organisational-group/Plymouth/REF 2021 Researchers by UoA
plymouth.organisational-group/Plymouth/REF 2021 Researchers by UoA/UoA11 Computer Science and Informatics
plymouth.organisational-group/Plymouth/Users by role
plymouth.organisational-group/Plymouth/Users by role/Academics
dcterms.dateAccepted2020-10-27
dc.rights.embargodate2020-11-13
dc.identifier.eissn1558-3791
dc.rights.embargoperiodNot known
rioxxterms.versionofrecord10.1109/tcsii.2020.3034771
rioxxterms.licenseref.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/
rioxxterms.licenseref.startdate2020-10-29
rioxxterms.typeJournal Article/Review


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