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Voice Recognition and Inverse Kinematics Control for a Redundant Manipulator Based on a Multilayer Artificial Intelligence Network

Anh, M.N. and Bien, D.X. (2021) Voice Recognition and Inverse Kinematics Control for a Redundant Manipulator Based on a Multilayer Artificial Intelligence Network. Journal of Robotics, 2021: 5805232. ISSN 16879600

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2021_Thang 7_ISI Q2_Voice Recognition and Inverse Kinematics Control for a Redundant Manipulator Based on a Multilayer Artificial Intelligence Network.pdf - Published Version

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Abstract

This study presents the construction of a Vietnamese voice recognition module and inverse kinematics control of a redundant manipulator by using artificial intelligence algorithms. The first deep learning model is built to recognize and convert voice information into input signals of the inverse kinematics problem of a 6-degrees-of-freedom robotic manipulator. The inverse kinematics problem is solved based on the construction and training. The second deep learning model is built using the data determined from the mathematical model of the system's geometrical structure, the limits of joint variables, and the workspace. The deep learning models are built in the PYTHON language. The efficient operation of the built deep learning networks demonstrates the reliability of the artificial intelligence algorithms and the applicability of the Vietnamese voice recognition module for various tasks. © 2021 Mai Ngoc Anh and Duong Xuan Bien.

Item Type: Article
Divisions: Faculties > Faculty of Control Engineering
Research centers > Advanced Technology Center
Identification Number: 10.1155/2021/5805232
Uncontrolled Keywords: Agricultural robots; Deep learning; Industrial manipulators; Inverse kinematics; Learning systems; Redundant manipulators; Speech recognition; Artificial intelligence algorithms; Geometrical structure; Inverse Kinematics Control; Learning models; Learning network; PYTHON language; Robotic manipulators; Voice informations; Inverse problems
Additional Information: Language of original document: English. All Open Access, Gold.
URI: http://eprints.lqdtu.edu.vn/id/eprint/8758

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