A Neural Network Approach for optimal grasp planning

Author

Mesgari, H. ; Samavati, F.C. ; Jazeh, H. E. Shoori ; Moosavian, S.A.A.

Author_Institution

Dept. of Mech. Eng., K.N. Toosi Univ. of Tech., Tehran, Iran

fYear

2011

fDate

27-29 Dec. 2011

Firstpage

859

Lastpage

864

Abstract

In this paper, the Neural Network (NN) Approach is used to find the best point on the object, for executing object manipulation task by a manipulator. The MAG performance index is calculated for some sample points of objects heuristically by MSC.ADAMS and MATLAB co-simulation for the 6DOF Stäubli^© TX40 arm. These samples then would be used to train a feed-forward back propagation neural networks. The result is the dynamics model of the robot and the grasped object in which the MAG performance index value is the input and the position of the best grasping point of the objects which maximizes the MAG index is the output.

Keywords

backpropagation; feedforward neural nets; manipulators; MAG performance index; MATLAB co-simulation; MSC; feed-forward back propagation neural networks; manipulator; neural network approach; object manipulation; optimal grasp planning; Automation; Instruments; Grasp planning; MSC. ADAMS and MATLAB Co-simulation; Optimization; neural networks; object manipulation;

fLanguage

English

Publisher

ieee

Conference_Titel

Control, Instrumentation and Automation (ICCIA), 2011 2nd International Conference on

Conference_Location

Shiraz

Print_ISBN

978-1-4673-1689-7

Type

conf

DOI

10.1109/ICCIAutom.2011.6356774

Filename

6356774