DocumentCode :
577160
Title :
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
Link To Document :
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