scholarly journals Intelligent modeling of double link flexible robotic manipulator using artificial neural network

2018 ◽  
Vol 20 (2) ◽  
pp. 1021-1034 ◽  
Author(s):  
I. Z. Mat Darus ◽  
A. Jamali ◽  
P. P. Mohd Samin ◽  
M. O. Tokhi
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Robotic Manipulator ◽  
Intelligent Modeling ◽  
Artificial Neural ◽  
Flexible Robotic Manipulator

scholarly journals Experimental Validation for Inverse Kinematics of a Five Axis Robotic Manipulator using Deep Artificial Neural Network

2019 ◽  
Vol 9 (1) ◽  
pp. 3943-3946
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Inverse Kinematics ◽  
Human Life ◽  
Robotic Manipulator ◽  
Training Dataset ◽  
Ann Model ◽  
Line Spacing ◽  
Artificial Neural ◽  
Five Axis

The use of robotics is to improvise and simplify human life. Robotic manipulators have been around for a while now and are being used in many different sectors such as industries, households, warehouses, medicine etc. Solving of inverse kinematics is one of the most complex issues faced while designing the robotic manipulator. In this research a Deep Artificial neural network (D-ANN) model is proposed to solve inverse kinematics of a 5-axis robotic manipulator with rotary joints. The D-ANN model is trained in MATLAB. Training dataset was generated using forward kinematics equations obtained easily from transformation matrix of the robotic manipulator. To validate predictions made by this model an experimental robotic arm manipulator Is fabricated. A smart camera setup has been linked to MATLAB for real time image processing and calculating the deviation of the end needle in reaching the desired target coordinate. The trained model yielded satisfactory results with ±0.03 radians error and this was also validated experimentally. This research will help the robotic manipulator reach the desired target coordinates even when one does not have enough input data.Paper Setup must be in A4 size with Margin: Top 0.7”, Bottom 0.7”, Left 0.65”, 0.65”, Gutter 0”, and Gutter Position Top. Paper must be in two Columns after Authors Name with Width 8.27”, height 11.69” Spacing 0.2”. Whole paper must be with: Font Name Times New Roman, Font Size 10, Line Spacing 1.05 EXCEPT Abstract, Keywords (Index Term), Paper Tile, References, Author Profile (in the last page of the paper, maximum 400 words), All Headings, and Manuscript Details (First Page, Bottom, left side).

Visual Control of Robotic Manipulator Based on Artificial Neural Network

1991 ◽  
Vol 3 (5) ◽  
pp. 394-400 ◽  
Author(s):  
Hideki Hashimoto ◽  
◽  
Takashi Kubota ◽  
Motoo Sato ◽  
Fumio Harashima ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Visual Information ◽  
Degrees Of Freedom ◽  
Robotic Manipulator ◽  
Inverse Kinematic ◽  
End Effector ◽  
Control Scheme ◽  
Neural Network System ◽  
Artificial Neural

This paper describes a control scheme for a robotic manipulator system which uses visual information to position and orientate the end-effector. In the scheme the position and the orientation of the target workpiece with respect to the base frame of the robot are assumed to be unknown, but the desired relative position and orientation of the end-effector to the target workpiece are given in advance. The control system directly integrates visual data into the servoing process without subdividing the process into determination of the position, orientation of the workpiece and inverse kinematic calculation. An artificial neural network system is used for determining the change in joint angles required in order to achieve the desired position and orientaion. The proposed system can control the robot so that it approach the desired position and orientaion from arbitary initial ones. Simulation for the robotic manipulator with six degrees of freedom is done. The validity and the effectiveness of the proposed control scheme are varified by computer simulations.

scholarly journals Visual control of robotic manipulator based on artificial neural network.

1990 ◽  
Vol 8 (4) ◽  
pp. 390-396
Author(s):  
Hideki HASHIMOTO ◽  
Takashi KUBOTA ◽  
Motoo SATO ◽  
Fumio HARASHIMA
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Robotic Manipulator ◽  
Visual Control ◽  
Artificial Neural

Autofluorescence spectroscopy of oral leukoplakia using an artificial neural network

2000 ◽  
Vol 25 (4) ◽  
pp. 325-325
Author(s):  
J.L.N. Roodenburg ◽  
H.J. Van Staveren ◽  
N.L.P. Van Veen ◽  
O.C. Speelman ◽  
J.M. Nauta ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Oral Leukoplakia ◽  
Autofluorescence Spectroscopy ◽  
Artificial Neural

1902: Is Steinstrasse After ESWL of Renal Stones Predictable? Artificial Neural Network Analysis

2004 ◽  
Vol 171 (4S) ◽  
pp. 502-503
Author(s):  
Mohamed A. Gomha ◽  
Khaled Z. Sheir ◽  
Saeed Showky ◽  
Khaled Madbouly ◽  
Emad Elsobky ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Network Analysis ◽  
Renal Stones ◽  
Neural Network Analysis ◽  
Artificial Neural Network Analysis ◽  
Artificial Neural

Forecasting travel demand: a comparison of logit and artificial neural network methods

1998 ◽  
Vol 49 (7) ◽  
pp. 717-722 ◽  
Author(s):  
M C M de Carvalho ◽  
M S Dougherty ◽  
A S Fowkes ◽  
M R Wardman
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Travel Demand ◽  
Network Methods ◽  
Artificial Neural

An Artificial Neural Network to Evaluate Cartilage Collagen and Proteoglycan Fractions Based on Multiparametric Quantitative MRI Techniques

2019 ◽  
Author(s):  
Johannes Thüring ◽  
Kevin Linka ◽  
Christiane Kuhl ◽  
Sven Nebelung ◽  
Daniel Truhn
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Quantitative Mri ◽  
Artificial Neural
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