Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.14365/3359
Full metadata record
DC FieldValueLanguage
dc.contributor.authorAlmusawi A.R.J.-
dc.contributor.authorDülger, Lale Canan-
dc.contributor.authorKapucu S.-
dc.date.accessioned2023-06-16T14:57:56Z-
dc.date.available2023-06-16T14:57:56Z-
dc.date.issued2019-
dc.identifier.issn2211-0984-
dc.identifier.urihttps://doi.org/10.1007/978-3-030-20131-9_182-
dc.identifier.urihttps://hdl.handle.net/20.500.14365/3359-
dc.description.abstractThis study presents a novel controller design for robot-assisted surgery based on Artificial Neural Network (ANN) architecture. The motion of surgical robot is constrained by the kinematics of remote center of motion (RCM). A new ANN design for inverse kinematics of RCM is proposed. ANN compared with classical ANN design. The input pattern of new ANN has included feedback of previous joint angles of robotic arm as well as the position and orientation of the tool tip. A six DOF robotic arm with a tool prototype used to demonstrate a surgical robot. The experimental results proved applicability and efficiency of NN in robotics assisted minimally invasive surgery (RAMIS). © 2019, Springer Nature Switzerland AG.en_US
dc.language.isoenen_US
dc.publisherSpringer Science and Business Media B.V.en_US
dc.relation.ispartofMechanisms and Machine Scienceen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectArtificial neural network (ANN)en_US
dc.subjectRemote center of motion (RCM)en_US
dc.subjectrobot assisted surgery (RAS)en_US
dc.subjectrobotic assisted minimally invasive surgery (RAMIS)en_US
dc.subjectInverse kinematicsen_US
dc.subjectMachine designen_US
dc.subjectNeural networksen_US
dc.subjectRobotic armsen_US
dc.subjectRoboticsen_US
dc.subjectSurgeryen_US
dc.subjectSurgical equipmenten_US
dc.subjectController designsen_US
dc.subjectInput patternsen_US
dc.subjectJoint angleen_US
dc.subjectMinimally invasive surgeryen_US
dc.subjectPosition and orientationsen_US
dc.subjectRemote center of motionsen_US
dc.subjectRobot-assisted surgeryen_US
dc.subjectSix-DOFen_US
dc.subjectRobotic surgeryen_US
dc.titleArtificial Neural Network Based Kinematics: Case Study on Robotic Surgeryen_US
dc.typeConference Objecten_US
dc.identifier.doi10.1007/978-3-030-20131-9_182-
dc.identifier.scopus2-s2.0-85067554906en_US
dc.authorscopusid57192003684-
dc.authorscopusid6603205256-
dc.identifier.volume73en_US
dc.identifier.startpage1839en_US
dc.identifier.endpage1848en_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.identifier.scopusqualityQ4-
dc.identifier.wosqualityN/A-
item.grantfulltextreserved-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
item.cerifentitytypePublications-
item.openairetypeConference Object-
item.fulltextWith Fulltext-
item.languageiso639-1en-
crisitem.author.dept05.10. Mechanical Engineering-
Appears in Collections:Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
Files in This Item:
File SizeFormat 
AT2-32-3359-Artifical.pdf
  Restricted Access		1.38 MBAdobe PDFView/Open    Request a copy
Show simple item record



CORE Recommender

SCOPUSTM   
Citations

5
checked on Sep 25, 2024

Page view(s)

54
checked on Sep 30, 2024

Download(s)

10
checked on Sep 30, 2024

Google ScholarTM

Check




Altmetric


Items in GCRIS Repository are protected by copyright, with all rights reserved, unless otherwise indicated.