http://iet.metastore.ingenta.com
1887

Deep learning-based grasp-detection method for a five-fingered industrial robot hand

Deep learning-based grasp-detection method for a five-fingered industrial robot hand

For access to this article, please select a purchase option:

Buy eFirst article PDF
£12.50
(plus tax if applicable)
Buy Knowledge Pack
10 articles for £75.00
(plus taxes if applicable)

IET members benefit from discounts to all IET publications and free access to E&T Magazine. If you are an IET member, log in to your account and the discounts will automatically be applied.

Learn more about IET membership 

Recommend Title Publication to library

You must fill out fields marked with: *

Librarian details
Name:*
Email:*
Your details
Name:*
Email:*
Department:*
Why are you recommending this title?
Select reason:
 
 
 
 
 
IET Computer Vision — Recommend this title to your library

Thank you

Your recommendation has been sent to your librarian.

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.

http://iet.metastore.ingenta.com/content/journals/10.1049/iet-cvi.2018.5002
Loading

Related content

content/journals/10.1049/iet-cvi.2018.5002
pub_keyword,iet_inspecKeyword,pub_concept
6
6
Loading
This is a required field
Please enter a valid email address