scholarly journals A Robotic Calibration Method Using a Model-Based Identification Technique and an Invasive Weed Optimization Neural Network Compensator

2020 ◽  
Vol 10 (20) ◽  
pp. 7320
Author(s):  
Phu-Nguyen Le ◽  
Hee-Jun Kang
Keyword(s):  
Neural Network ◽  
Robot Manipulator ◽  
Kinematic Model ◽  
Calibration Method ◽  
Invasive Weed Optimization ◽  
Invasive Weed ◽  
Suggested Technique ◽  
Calibration Algorithm ◽  
Identification Technique ◽  
Position Precision

The study proposed a robotic calibration algorithm for improving the robot manipulator position precision. At first, the kinematic parameters as well as the compliance parameters of the robot can be identified together to improve its accuracy using the joint deflection model and the conventional kinematic model calibration technique. Then, an artificial neural network is constructed for further compensating the unmodeled errors. The invasive weed optimization is used to determine the parameters of the neural network. To show the advantages of the suggested technique, an HH800 robot is employed for the experimental study of the proposed algorithm. The improved position precision of the robot after the experiment firmly proves the practicability and positional precision of the proposed method over the other algorithms in comparison.

scholarly journals Improvement of Heavy Load Robot Positioning Accuracy by Combining a Model-Based Identification for Geometric Parameters and an Optimized Neural Network for the Compensation of Nongeometric Errors

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-13
Author(s):  
Yuxiang Wang ◽  
Zhangwei Chen ◽  
Hongfei Zu ◽  
Xiang Zhang ◽  
Chentao Mao ◽  
...  
Keyword(s):  
Neural Network ◽  
Manufacturing Systems ◽  
Robot Manipulator ◽  
Joint Stiffness ◽  
Calibration Method ◽  
Geometric Parameters ◽  
Heavy Load ◽  
Positioning Accuracy ◽  
Robot Positioning ◽  
Residual Errors

The positioning accuracy of a robot is of great significance in advanced robotic manufacturing systems. This paper proposes a novel calibration method for improving robot positioning accuracy. First of all, geometric parameters are identified on the basis of the product of exponentials (POE) formula. The errors of the reduction ratio and the coupling ratio are identified at the same time. Then, joint stiffness identification is carried out by adding a load to the end-effector. Finally, residual errors caused by nongeometric parameters are compensated by a multilayer perceptron neural network (MLPNN) based on beetle swarm optimization algorithm. The calibration is implemented on a SIASUN SR210D robot manipulator. Results show that the proposed method possesses better performance in terms of faster convergence and higher precision.

Hybrid Elman Neural Network and an Invasive Weed Optimization Method for Bridge Defect Recognition

2020 ◽  
pp. 036119812096794
Author(s):  
Eslam Mohammed Abdelkader ◽  
Osama Moselhi ◽  
Mohamed Marzouk ◽  
Tarek Zayed
Keyword(s):  
Neural Network ◽  
Prediction Models ◽  
Statistical Significance ◽  
Optimization Method ◽  
Bridge Management ◽  
Invasive Weed Optimization ◽  
Invasive Weed ◽  
Elman Neural Network ◽  
Existing Bridges ◽  
Value Decomposition

Existing bridges are aging and deteriorating, raising concerns for public safety and the preservation of these valuable assets. Furthermore, the transportation networks that manage many bridges face budgetary constraints. This state of affairs necessitates the development of a computer vision-based method to alleviate shortcomings in visual inspection-based methods. In this context, the present study proposes a three-tier method for the automated detection and recognition of bridge defects. In the first tier, singular value decomposition ([Formula: see text]) is adopted to formulate the feature vector set through mapping the most dominant spatial domain features in images. The second tier encompasses a hybridization of the Elman neural network ([Formula: see text]) and the invasive weed optimization (I[Formula: see text]) algorithm to enhance the prediction performance of the ENN. This is accomplished by designing a variable optimization mechanism that aims at searching for the optimum exploration–exploitation trade-off in the neural network. The third tier involves validation through comparisons against a set of conventional machine-learning and deep-learning models capitalizing on performance prediction and statistical significance tests. A computerized platform was programmed in C#.net to facilitate implementation by the users. It was found that the method developed outperformed other prediction models achieving overall accuracy, F-measure, Kappa coefficient, balanced accuracy, Matthews’s correlation coefficient, and area under curve of 0.955, 0.955, 0.914, 0.965, 0.937, and 0.904, respectively as per cross validation. It is expected that the method developed can improve the decision-making process in bridge management systems.

scholarly journals Invasive Weed Optimization Based Ransom-Ware Detection in Cloud Environment

2021 ◽  
Vol 7 (09) ◽  
pp. 1-6
Author(s):  
Adil Hussain Mohammed
Keyword(s):  
Neural Network ◽  
Feature Reduction ◽  
Training Dataset ◽  
Invasive Weed Optimization ◽  
Invasive Weed ◽  
Detection Model ◽  
Network Training ◽  
Proposed Model ◽  
Trained Neural Network ◽  
Good Set

Cloud provide support to manage, control, monitor different organization. Due to flexible nature f cloud chance of attack on it increases by means of some software attack in form of ransomware. Many of researcher has proposed various model to prevent such attacks or to identify such activities. This paper has proposed a ransomware detection model by use of trained neural network. Training of neural network was done by filter or optimized feature set obtained from the feature reduction algorithm. Paper has proposed a Invasive Weed Optimization algorithm that filter good set of feature from the available input training dataset. Proposed model test was performed on real dataset, have set sessions related to cloud ransomware attacks. Result shows that proposed model has increase the comparing parameter values.

scholarly journals Hand–Eye Calibration Algorithm Based on an Optimized Neural Network

Actuators ◽  
2021 ◽  
Vol 10 (4) ◽  
pp. 85
Author(s):  
Jiang Hua ◽  
Liangcai Zeng
Keyword(s):  
Neural Network ◽  
Coordinate System ◽  
Calibration Method ◽  
Calibration Model ◽  
The Neural Network ◽  
Calibration Algorithm ◽  
Robot Grasping ◽  
Relationship Of ◽  
The Relationship ◽  
Camera Coordinate System

A robot can identify the position of a target and complete a grasping based on the hand–eye calibration algorithm, through which the relationship between the robot coordinate system and the camera coordinate system can be established. The accuracy of the hand–eye calibration algorithm affects the real-time performance of the visual servo system and the robot manipulation. The traditional calibration technique is based on a perfect mathematical model AX = XB, in which the X represents the relationship of (A) the camera coordinate system and (B) the robot coordinate system. The traditional solution to the transformation matrix has a certain extent of limitation and instability. To solve this problem, an optimized neural-network-based hand–eye calibration method was developed to establish a non-linear relationship between robotic coordinates and pixel coordinates that can compensate for the nonlinear distortion of the camera lens. The learning process of the hand–eye calibration model can be interpreted as B=fA, which is the coordinate transformation relationship trained by the neural network. An accurate hand–eye calibration model can finally be obtained by continuously optimizing the network structure and parameters via training. Finally, the accuracy and stability of the method were verified by experiments on a robot grasping system.

scholarly journals Cinemática Inversa com Redes Neurais Aplicadas em Robôs Manipuladores

2018 ◽  
Vol 1 (43) ◽  
pp. 49
Author(s):  
Gabriel Daltro Duarte ◽  
Claudio Pereira Mego Quinteros ◽  
Lincoln Machado Araújo
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Nonlinear Equations ◽  
Inverse Kinematics ◽  
Robot Manipulator ◽  
Kinematic Model ◽  
Online Training ◽  
Network Architectures ◽  
The Neural Network ◽  
Neural Network Architectures

Today’s world is going through what is known as the Fourth Industrial Revolution. Robots have been gaining more and more space in the industry and going beyond expectations. The use of robots in industry is related to the increasing production and the quality of the electronic products. For an accurate movement of a robot manipulator it is necessary to obtain its inverse kinematic model, however, obtaining this model requires the challenging solution of a set of nonlinear equations. For this system of nonlinear equations there is no generic solution method. In view of this matter, this research aims to solve the problem of the inverse kinematics of a robot manipulator using artificial neural networks without the need to model the robot’s direct kinematics. Two neural network training methodologies, called offline training and online training were used. The basic difference between these two is that in the offline methodology all training points are obtained before any training of the neural network occurs, whereas in online training the use of a training method is recurrent. As the robot moves, new training points are obtained and training processes with the new acquired points are executed, allowing a learning process of continuous inverse kinematics. To validate the proposed methodology a prototype of a manipulated planar robot with one degree of freedom was developed and several architectures of neural networks were tested to find the optimal architecture. The offline training methodology obtained very satisfactory results for most of the neural network architectures tested. The online training only achieved satisfactory results in neural network architectures with quantities of neurons much larger than the quantities used in the architectures used in offline training and still obtained inferior results. The neural networks trained in offline mode, when compared to the training networks in the online mode, presented a greater capacity of generalization and a smaller value of output error. The online training has only achieved satisfactory results in neural network architectures with quantities of neurons much larger than the quantities used in the trained architectures in offline mode.

scholarly journals A Robot Calibration Method Based on Joint Angle Division and an Artificial Neural Network

2019 ◽  
Vol 2019 ◽  
pp. 1-12 ◽  
Author(s):  
Zhirong Wang ◽  
Zhangwei Chen ◽  
Yuxiang Wang ◽  
Chentao Mao ◽  
Qiang Hang
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Joint Angle ◽  
Kinematic Model ◽  
Calibration Method ◽  
Robot Calibration ◽  
Positioning Accuracy ◽  
Robot Positioning ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Robot calibration is used to improve the accuracy of the kinematic model to achieve the higher positioning accuracy within the workspace. Due to some nongeometrical reasons such as joint and link flexibility, the errors are unevenly distributed in the workspace. In this case, it is difficult for the existing methods used to improve the absolute positioning accuracy to achieve good results in each region, especially for robots with large self-weights. In this paper, a novel calibration method is proposed, which deals with joint deflection dependent errors to enhance the robot positioning accuracy in the whole workspace. Firstly, the joint angle workspace is divided into several local regions according to the mass distribution of the robot. Then, its geometric parameters are modeled and identified using the Denavit–Hartenberg (DH) model in each region and in the whole workspace separately. Since the nongeometric error sources are difficult to model correctly, an artificial neural network (ANN) is applied to compensate for the nongeometric errors. Finally, the experiments using an 8 degree-of-freedom (DOF) engineering robot are conducted to demonstrate the validity of the proposed method. The combination of the joint angle division and ANN could be an effective solution for the robot calibration, especially for one with a large self-weight.

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