Open AccessDeep learning‐based grasp‐detection method for a five‐fingered industrial robot hand
Author(s) -
Chao Ya,
Chen Xingchen,
Xiao Nanfeng
Publication year - 2019
Publication title -
iet computer vision
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.38
H-Index - 37
eISSN - 1751-9640
pISSN - 1751-9632
DOI - 10.1049/iet-cvi.2018.5002
Subject(s) - grasp , artificial intelligence , computer science , industrial robot , computer vision , convolutional neural network , robot , deep learning , object (grammar) , robot hand , object detection , pattern recognition (psychology) , programming language
To improve the accuracy of robotic grasp in some uncertain environments, a deep learning‐based object‐detection method for a five‐fingered industrial robot hand model is proposed in this study. The authors first design a five‐fingered industrial robot hand model with 21‐degrees of freedom (DOF). Based on the sensor data of a 5DT data glove, the industrial robot hand can be controlled in real time. They use the object‐detection network's faster regions convolutional neural network and single shot multibox detector to locate the grasp objects. To optimise the robotic grasp detection, two grasp‐predictor methods, direct grasp predictor and multi‐modal grasp predictor, are applied to obtain the best graspable region. In the simulation designed in this study, cooperating with a 6‐DOF robot arm, the five‐fingered industrial robot hand can detect an object accurately and grasp it steadily.