Visual servoing based control methods for nonholonomic mobile robot

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dc.authoriddirik, mahmut/0000-0003-1718-5075
dc.authoridKocamaz, Adnan Fatih/0000-0002-7729-8322
dc.authoridDönmez, Emrah/0000-0003-3345-8344
dc.authorwosiddirik, mahmut/R-7971-2019
dc.authorwosidKocamaz, Adnan Fatih/C-2820-2014
dc.authorwosidDirik, Mahmut/AAD-6165-2021
dc.authorwosidDönmez, Emrah/W-2891-2017
dc.contributor.authorDirik, Mahmut
dc.contributor.authorKocamaz, Adnan Fatih
dc.contributor.authorDonmez, Emrah
dc.date.accessioned2024-08-04T20:48:56Z
dc.date.available2024-08-04T20:48:56Z
dc.date.issued2020
dc.departmentİnönü Üniversitesien_US
dc.description.abstractIn this paper, we utilized two different vision-based go-to-goal robot control approaches on indoor nonholonomic mobile robot systems. In the proposed methods, eye-out-device configured camera (overhead camera) image data are used as the input parameters to determine the speeds of robot wheels. The main purpose of this system is to minimize the complexity of conventional robot control kinematics and to provide an efficient control approach to manage the wheel speeds and the direction angle of the mobile robot. In addition to reducing the complexity of robot control kinematics, it is also intended to reduce systematic and nonsystematic errors. The proposed method is divided into three stages: the first stage consists of the overhead camera calibration and the configuration of the robot motion environment. At this stage. the labels placed on the robot and target position were identified and the position information of the robot was obtained. In the second stage, control inputs such as position and orientation based on robot motion tracking and visual feature information were obtained. In the third stage, Graph-based Gaussian and Angle-based Decision tree control approaches were performed. We have briefly described these control approaches as follows: Graph-based Decision Tree Control (GDTC), Graph-based Gaussian Control (GGC), Angle-based Decision Tree Control (ADTC), and Angle-based Gaussian Control (AGC). Using these control approaches, many real-time experimental studies with eye-catching device configuration have been performed. The efficacy and usability of the methods have been demonstrated by experimental results.en_US
dc.identifier.endpage113en_US
dc.identifier.issn2307-1877
dc.identifier.issn2307-1885
dc.identifier.issue2en_US
dc.identifier.scopus2-s2.0-85091085814en_US
dc.identifier.scopusqualityQ3en_US
dc.identifier.startpage95en_US
dc.identifier.urihttps://hdl.handle.net/11616/99547
dc.identifier.volume8en_US
dc.identifier.wosWOS:000536456800006en_US
dc.identifier.wosqualityQ4en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherAcademic Publication Councilen_US
dc.relation.ispartofJournal of Engineering Researchen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectdecision tree controlen_US
dc.subjectgaussian controlen_US
dc.subjectnon-holonomic mobile roboten_US
dc.subjectoverhead cameraen_US
dc.subjectvision-based controlen_US
dc.titleVisual servoing based control methods for nonholonomic mobile roboten_US
dc.typeArticleen_US

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