Design of Mobile Robot Control Infrastructure Based on Decision Trees and Adaptive Potential Area Methods

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dc.authoridDönmez, Emrah/0000-0003-3345-8344
dc.authoridKocamaz, Adnan Fatih/0000-0002-7729-8322
dc.authorwosidDönmez, Emrah/W-2891-2017
dc.authorwosidKocamaz, Adnan Fatih/C-2820-2014
dc.contributor.authorDonmez, Emrah
dc.contributor.authorKocamaz, Adnan Fatih
dc.date.accessioned2024-08-04T20:46:03Z
dc.date.available2024-08-04T20:46:03Z
dc.date.issued2020
dc.departmentİnönü Üniversitesien_US
dc.description.abstractThere have been a great number of studies in the scope of mobile robot systems. The most critical tasks in these systems are control and path planning. The main goal of the control task is to develop a stable control system. On the other hand, the basic motivation in the path planning task is to find a safe path with an acceptable cost. In most researches, a moving robot is considered as a point mass object and only the simulation experiments are applied. In this study, a decision tree-based mobile robot control has been developed for a static indoor environment hosting obstacle(s). The camera has been hung vertically (eye-out-device configuration) to obtain the configuration area map and track the wheeled mobile robot (WMR). A suitable path plan has been extracted with the adaptive artificial potential field (APF) method on the image obtained from the camera. Virtual distance sensors are used to calculate the potentials for APF. A decision tree-based controller has been developed to model the motion characteristics of the robot. A trigonometry-based approach is used to calculate the controller inputs. The controller has steered the WMR on the path in real time. Both simulation and real-world experiments have been conducted on a WMR in different configuration spaces. It has been determined that the designed system is convenient for controlling the WMR. The data obtained are compared to show the difference between the desired and actual path planning results. The efficiency of the controller method has been greatly improved by using dynamic parameters in the control modules.en_US
dc.description.sponsorshipTUBITAK [116E568]en_US
dc.description.sponsorshipThis study has been supported by TUBITAK with the Project Number of 116E568. We would like to thank Department of Computer Engineering at the Faculty of Engineering at Inonu University for providing us the robotic laboratory.en_US
dc.identifier.doi10.1007/s40998-019-00228-0
dc.identifier.endpage448en_US
dc.identifier.issn2228-6179
dc.identifier.issn2364-1827
dc.identifier.issue1en_US
dc.identifier.scopus2-s2.0-85068850863en_US
dc.identifier.scopusqualityQ2en_US
dc.identifier.startpage431en_US
dc.identifier.urihttps://doi.org/10.1007/s40998-019-00228-0
dc.identifier.urihttps://hdl.handle.net/11616/98866
dc.identifier.volume44en_US
dc.identifier.wosWOS:000514544600030en_US
dc.identifier.wosqualityQ4en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherSpringer Int Publ Agen_US
dc.relation.ispartofIranian Journal of Science and Technology-Transactions of Electrical Engineeringen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectDecision treeen_US
dc.subjectPotential fielden_US
dc.subjectPath planningen_US
dc.subjectVisual-based controlen_US
dc.titleDesign of Mobile Robot Control Infrastructure Based on Decision Trees and Adaptive Potential Area Methodsen_US
dc.typeArticleen_US

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