Object manipulation with a variable-stiffness robotic mechanism using deep neural networks for visual semantics and load estimation

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dc.contributor.authorBayraktar, Ertuğrul
dc.contributor.authorYiğit, Cihat Bora
dc.contributor.authorBoyraz, Pınar
dc.date.accessioned2020-04-30T13:32:54Z
dc.date.available2020-04-30T13:32:54Z
dc.date.issued2019
dc.departmentDÜ, Mühendislik Fakültesi, Mekatronik Mühendisliği Bölümüen_US
dc.description.abstractIn recent years, the computer vision applications in the robotics have been improved to approach human-like visual perception and scene/context understanding. Following this aspiration, in this study, we explored the possibility of better object manipulation performance by connecting the visual recognition of objects to their physical attributes, such as weight and center of gravity (CoG). To develop and test this idea, an object manipulation platform is built comprising a robotic arm, a depth camera fixed at the top center of the workspace, embedded encoders in the robotic arm mechanism, and microcontrollers for position and force control. Since both the visual recognition and force estimation algorithms use deep learning principles, the test set-up was named as Deep-Table. The objects in the manipulation tests are selected from everyday life and are common to be seen on modern office desktops. The visual object localization and recognition processes are performed from two distinct branches by deep convolutional neural network architectures. We present five of the possible cases, having different levels of information availability on the object weight and CoG in the experiments. The results confirm that using our algorithm, the robotic arm can move different types of objects successfully varying from several grams (empty bottle) to around 250 g (ceramic cup) without failure or tipping. The proposed method also shows that connecting the object recognition with load estimation and contact point further improves the performance characterized by a smoother motion. © 2019, Springer-Verlag London Ltd., part of Springer Nature.en_US
dc.identifier.doi10.1007/s00521-019-04412-5en_US
dc.identifier.issn0941-0643
dc.identifier.scopusqualityQ1en_US
dc.identifier.urihttps://dx.doi.org/10.1007/s00521-019-04412-5
dc.identifier.urihttps://hdl.handle.net/20.500.12684/480
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherSpringer Londonen_US
dc.relation.ispartofNeural Computing and Applicationsen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectContext awareness; Deep neural networks; Force estimation; Object recognition; Robotic manipulationen_US
dc.titleObject manipulation with a variable-stiffness robotic mechanism using deep neural networks for visual semantics and load estimationen_US
dc.typeArticleen_US

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