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.

An effective method for damage assessment based on limited measured locations in skeletal structures

2020 ◽  
Vol 24 (1) ◽  
pp. 183-195 ◽  
Author(s):  
Parsa Ghannadi ◽  
Seyed Sina Kourehli
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Damage Detection ◽  
Degrees Of Freedom ◽  
Truss Structures ◽  
Mode Shapes ◽  
Analysis Model ◽  
Test Analysis ◽  
Modal Test ◽  
Artificial Neural

This article proposes a new damage detection method using Modal Test Analysis Model and artificial neural networks. A challenge in damage detection problems is lack of measured degrees of freedom, as well as limitations of attached sensors. Modal Test Analysis Model has been used in order to estimate unmeasured degrees of freedom. An experimental cantilever beam was used to show Modal Test Analysis Model’s efficiency in estimation of unmeasured mode shapes. To solve the inverse problem of damage detection, mode shapes estimated by Modal Test Analysis Model were used as inputs, and characteristics of the damage served as outputs of the artificial neural network. The sensitivity analysis carried out for each example showing the performance of artificial neural network after mode shape expansion was efficiently improved. Three numerical examples for plane and space truss structures are considered, in order to verify effectiveness of the proposed method. Results demonstrate a high accuracy of Modal Test Analysis Model and artificial neural network for structural damage detection.

A simulation-based method using artificial neural networks for solving the inverse kinematic problem of articulated robots

2015 ◽  
Vol 231 (3) ◽  
pp. 470-479 ◽  
Author(s):  
Mustafa Soylak ◽  
Tuğrul Oktay ◽  
İlke Turkmen
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Artificial Neural Networks ◽  
Inverse Kinematic ◽  
Plasma Cutting ◽  
Three Dimensional Model ◽  
Inverse Kinematic Problem ◽  
Data Set ◽  
Artificial Neural

In our article, inverse kinematic problem of a plasma cutting robot with three degree of freedom is solved using artificial neural networks. Artificial neural network was trained using joint angle values according to cartesian coordinates ( x, y, z) of end point of a robotic arm. The Levenberg–Marquardt training algorithm was applied to educate artificial neural network. To validate the designed neural network, it was tested using a new test data set which is not applied in training. A simulation was performed on a three-dimensional model of MSC.ADAMS software using angle values obtained from artificial neural network test. It was revealed from this simulation that trajectory of plasma cutting torch obtained using artificial neural network agreed well with desired trajectory.

Early diagnosis of melanoma using an artificial neural network system

1997 ◽  
Vol 7 (Supplement 1) ◽  
pp. S58 ◽  
Author(s):  
M Burroni ◽  
G Dell??Eva ◽  
P Puddu ◽  
F Atzori ◽  
R Bono ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Early Diagnosis ◽  
Network System ◽  
Neural Network System ◽  
Artificial Neural

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).

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