American Journal of Mechanical Engineering
ISSN (Print): 2328-4102 ISSN (Online): 2328-4110 Website: http://www.sciepub.com/journal/ajme Editor-in-chief: Kambiz Ebrahimi, Dr. SRINIVASA VENKATESHAPPA CHIKKOL
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American Journal of Mechanical Engineering. 2014, 2(7), 216-218
DOI: 10.12691/ajme-2-7-9
Open AccessArticle

Robotic Grasping System Using Convolutional Neural Networks

Pavol Bezák1, Yury Rafailovich Nikitin1 and Pavol Božek2,

1Institute of Applied Informatics, Automation and Mathematics, Faculty of Materials Science and Technology, Slovak University of Technology, Trnava, Slovakia

2Kalashnikov Izhevsk State Technical University, Mechatronic Systems Department, Izhevsk, Russia

Pub. Date: October 15, 2014

Cite this paper:
Pavol Bezák, Yury Rafailovich Nikitin and Pavol Božek. Robotic Grasping System Using Convolutional Neural Networks. American Journal of Mechanical Engineering. 2014; 2(7):216-218. doi: 10.12691/ajme-2-7-9

Abstract

Object grasping by robot hands is challenging due to the hand and object modeling uncertainties, unknown contact type and object stiffness properties. To overcome these challenges, the essential purpose is to achieve the mathematical model of the robot hand, model the object and the contact between the object and the hand. In this paper, an intelligent hand-object contact model is developed for a coupled system assuming that the object properties are known. The control is simulated in the Matlab Simulink/ SimMechanics, Neural Network Toolbox and Computer Vision System Toolbox..

Keywords:
robot hand modeling grasping convolutional neural networks deep learning object recognition pose estimation

Creative CommonsThis work is licensed under a Creative Commons Attribution 4.0 International License. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/

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