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dc.contributor.authorCáceres Hernández, Danilo
dc.contributor.authorDung Hoang, Van
dc.contributor.authorFilonenko, Alexander
dc.contributor.authorHyun Jo, Kang
dc.date.accessioned2018-06-29T20:59:25Z
dc.date.accessioned2018-06-29T20:59:25Z
dc.date.available2018-06-29T20:59:25Z
dc.date.available2018-06-29T20:59:25Z
dc.date.issued07/01/2014
dc.date.issued07/01/2014
dc.identifierhttps://ieeexplore.ieee.org/abstract/document/6864917/
dc.identifier.issn2163-5145
dc.identifier.urihttp://ridda2.utp.ac.pa/handle/123456789/5089
dc.identifier.urihttp://ridda2.utp.ac.pa/handle/123456789/5089
dc.descriptionAutonomous mobile robots navigation and control systems are still hugely important in real time robotic applications. When moving towards fully autonomous navigation, guidance plays a vital task for successful autonomous navigation. In this paper, the authors propose real time guidance fuzzy logic application based on edge and color information surrounding the road surface by using omnidirectional cameras. Autonomous navigation systems must be able to recognize feature descriptors from both edge and color information. Firstly, it was proposed to extract the longest segments of lines from the above mentioned methods. Secondly, RANdom SAmple Consensus (RANSAC) curve fitting method was implemented for detecting the best curve fitting given the data set of points for each line segment. Thirdly, the set of intersection points for each pair of curves were extracted. Fourthly, the Density-based spatial clustering of applications with noise (DBSCAN) method was used in estimating the vanishing point (VP). Finally, to control the mobile robot in an unknown environment, a fuzzy logic controller facilitated by the VP was implemented. Preliminary results were gathered and tested on a group of consecutive frames undertaken at the University of Ulsan (UoU) to prove their effectiveness.en_US
dc.description.abstractAutonomous mobile robots navigation and control systems are still hugely important in real time robotic applications. When moving towards fully autonomous navigation, guidance plays a vital task for successful autonomous navigation. In this paper, the authors propose real time guidance fuzzy logic application based on edge and color information surrounding the road surface by using omnidirectional cameras. Autonomous navigation systems must be able to recognize feature descriptors from both edge and color information. Firstly, it was proposed to extract the longest segments of lines from the above mentioned methods. Secondly, RANdom SAmple Consensus (RANSAC) curve fitting method was implemented for detecting the best curve fitting given the data set of points for each line segment. Thirdly, the set of intersection points for each pair of curves were extracted. Fourthly, the Density-based spatial clustering of applications with noise (DBSCAN) method was used in estimating the vanishing point (VP). Finally, to control the mobile robot in an unknown environment, a fuzzy logic controller facilitated by the VP was implemented. Preliminary results were gathered and tested on a group of consecutive frames undertaken at the University of Ulsan (UoU) to prove their effectiveness.en_US
dc.formatapplication/pdf
dc.formattext/html
dc.languageeng
dc.rightsinfo:eu-repo/semantics/embargoedAccess
dc.subjectNavigationen_US
dc.subjectImage segmentationen_US
dc.subjectRobot sensing systemsen_US
dc.subjectMobile robotsen_US
dc.subjectImage color analysisen_US
dc.subjectCamerasen_US
dc.subjectNavigation
dc.subjectImage segmentation
dc.subjectRobot sensing systems
dc.subjectMobile robots
dc.subjectImage color analysis
dc.subjectCameras
dc.titleVision-based heading angle estimation for an autonomous mobile robots navigationen_US
dc.typeinfo:eu-repo/semantics/article
dc.typeinfo:eu-repo/semantics/publishedVersion


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