文章基本信息

标题：Robotic Grasping System Using Convolutional Neural Networks
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作者：Pavol Bezák ; Yury Rafailovich Nikitin ; Pavol Božek 等
期刊名称：American Journal of Mechanical Engineering
印刷版ISSN：2328-4102
电子版ISSN：2328-4110
出版年度：2014
卷号：2
期号：7
页码：216-218
DOI：10.12691/ajme-2-7-9
语种：English
出版社：Science and Education Publishing
摘要：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..
关键词：robot hand; modeling; grasping; convolutional neural networks; deep learning; object recognition; pose estimation