Monocular vision with deep neural networks for autonomous mobile robots navigation

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dc.authoridAlaa Ali Hameed / 0000-0002-8514-9255en_US
dc.authorscopusidAlaa Ali Hameed / 56338374100en_US
dc.authorwosidAlaa Ali Hameed / ABI-8417-2020en_US
dc.contributor.authorSleaman, Walead Kaled
dc.contributor.authorHameed, Alaa Ali
dc.contributor.authorJamil, Akhtar
dc.date.accessioned2022-12-08T08:11:23Z
dc.date.available2022-12-08T08:11:23Z
dc.date.issued2022en_US
dc.departmentİstinye Üniversitesi, Mühendislik ve Doğa Bilimleri Fakültesi, Bilgisayar Mühendisliği Bölümüen_US
dc.description.abstractEnabling mobile robots to explore the formerly unidentified environment is a challenging task. The current paper describes the internal analysis algorithm for mobile robots that combines various convolutional neural network (CNN) layers with the decision-making process in a hierarchical way. The whole system is trained end-to-end on data captured by a low-cost depth camera (RGB-D). The output consists of the proposed expansion model of the robot's critical moving directions to achieve autonomous analysis ability. Training this model through the dataset is created using Hand-Controlled Mobile Robot (HCMR) built for this purpose. The experiments were conducted by moving this robot in natural and diverse environments. The robot was trained using this data and applied for environmental investigation decisions (the control labels) using CNN to enable the robot to automatically sense the navigation without a map in an unknown environment. Furthermore, extensive experiments were conducted indoors and attained an accuracy of 77%. Experiments showed that the proposed model was able to reach equivalent results that are generally obtained enormously from an expensive sensor. In addition, comprehensive comparisons were drawn between the human-controlled robot and a robot trained using a deep learning process to determine decisions to control the robot's movement. The reached results were identical and satisfactory.en_US
dc.identifier.citationSleaman, W. K., Hameed, A. A., & Jamil, A. (2023). Monocular vision with deep neural networks for autonomous mobile robots navigation. Optik, 272, 170162.en_US
dc.identifier.doi10.1016/j.ijleo.2022.170162en_US
dc.identifier.isbn00304026
dc.identifier.scopus2-s2.0-85142713055en_US
dc.identifier.scopusqualityN/Aen_US
dc.identifier.urihttp://dx.doi.org/10.1016/j.ijleo.2022.170162
dc.identifier.urihttps://hdl.handle.net/20.500.12713/3427
dc.identifier.volume272en_US
dc.identifier.wosWOS:000991395000002en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.institutionauthorAlaa Ali, Hameed
dc.language.isoenen_US
dc.publisherElsevieren_US
dc.relation.ispartofOptiken_US
dc.relation.publicationcategoryMakale - Ulusal Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectRobot Explorationen_US
dc.subjectDeep Learningen_US
dc.subjectCNN and Stereo System With Monocular Cameraen_US
dc.titleMonocular vision with deep neural networks for autonomous mobile robots navigationen_US
dc.typeArticleen_US

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