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Stable Robotic Grasping of Multiple Objects using Deep Neural Networks

Published online by Cambridge University Press:  20 July 2020

Dongeon Kim ,
Ailing Li  and
Jangmyung Lee Open the ORCID record for Jangmyung Lee [Opens in a new window]
Dongeon Kim
Affiliation:
Department of Electrical and Electronic Engineering, Pusan National University, Busan 46241, South Korea. E-mail: [email protected]
Ailing Li
Affiliation:
Department of Electrical and Electronic Engineering, Pusan National University, Busan 46241, South Korea. E-mail: [email protected]
Jangmyung Lee*
Affiliation:
Department of Electrical and Electronic Engineering, Pusan National University, Busan 46241, South Korea. E-mail: [email protected]
*
*Corresponding author. E-mail: [email protected]
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Summary

Optimal grasping points for a robotic gripper were derived, based on object and hand geometry, using deep neural networks (DNNs). The optimal grasping cost functions were derived using probability density functions for each local cost function of the normal distribution. Using the DNN, the optimum height and width were set for the robot hand to grasp objects, whose geometric and mass centre points were also considered in obtaining the optimum grasping positions for the robot fingers and the object. The proposed algorithm was tested on 10 differently shaped objects and showed improved grip performance compared to conventional methods.

Keywords

Stable robotic graspingDeep neural networksProbability density function
Type
Articles
Information
Robotica , Volume 39 , Issue 4 , April 2021 , pp. 735 - 748
Copyright
Copyright © The Author(s), 2020. Published by Cambridge University Press

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