Investigation of Neural-Network-Based Inverse Kinematics for a 6-DOF Serial Manipulator With Non-Spherical Wrist

2018 ◽  
Author(s):  
Benjamin E. Hargis ◽  
Wesley A. Demirjian ◽  
Matthew W. Powelson ◽  
Stephen L. Canfield
Keyword(s):  
Neural Network ◽  
Inverse Kinematics ◽  
Computational Time ◽  
Robot Arm ◽  
Training Set ◽  
Test Set ◽  
Network Training ◽  
Inverse Kinematics Problem ◽  
Spherical Wrist ◽  
Artificial Neural Network Ann

This study proposes using an Artificial Neural Network (ANN) to train a 6-DOF serial manipulator with a non-spherical wrist to solve the inverse kinematics problem. In this approach, an ANN has been trained to determine the configuration parameters of a serial manipulator that correspond to the position and pose of its end effector. The network was modeled after the AUBO-i5 robot arm, and the experimental results have shown the ability to achieve millimeter accuracy in tool space position with significantly reduced computational time relative to an iterative kinematic solution when applied to a subset of the workspace. Furthermore, a separate investigation was conducted to quantify the relationship between training example density, training set error, and test set error. Testing indicates that, for a given network, sufficient example point density may be approximated by comparing the training set error with test set error. The neural network training was performed using the MATLAB Neural Network Toolbox.

scholarly journals Solution for Ill-Posed Inverse Kinematics of Robot Arm by Network Inversion

2010 ◽  
Vol 2010 ◽  
pp. 1-9 ◽  
Author(s):  
Takehiko Ogawa ◽  
Hajime Kanada
Keyword(s):  
Neural Network ◽  
Inverse Problems ◽  
Inverse Kinematics ◽  
Robot Arm ◽  
Joint Angles ◽  
End Effector ◽  
Inverse Kinematics Problem ◽  
Ill Posed ◽  
The Given

In the context of controlling a robot arm with multiple joints, the method of estimating the joint angles from the given end-effector coordinates is called inverse kinematics, which is a type of inverse problems. Network inversion has been proposed as a method for solving inverse problems by using a multilayer neural network. In this paper, network inversion is introduced as a method to solve the inverse kinematics problem of a robot arm with multiple joints, where the joint angles are estimated from the given end-effector coordinates. In general, inverse problems are affected by ill-posedness, which implies that the existence, uniqueness, and stability of their solutions are not guaranteed. In this paper, we show the effectiveness of applying network inversion with regularization, by which ill-posedness can be reduced, to the ill-posed inverse kinematics of an actual robot arm with multiple joints.

scholarly journals Cerebellum-inspired neural network solution of the inverse kinematics problem

2015 ◽  
Vol 109 (6) ◽  
pp. 561-574 ◽  
Author(s):  
Mitra Asadi-Eydivand ◽  
Mohammad Mehdi Ebadzadeh ◽  
Mehran Solati-Hashjin ◽  
Christian Darlot ◽  
Noor Azuan Abu Osman
Keyword(s):  
Neural Network ◽  
Inverse Kinematics ◽  
Network Solution ◽  
Inverse Kinematics Problem

scholarly journals PATTERN RECOGNITION BASED SPEED FORECASTING METHODOLOGY FOR URBAN TRAFFIC NETWORK

Transport ◽  
2018 ◽  
Vol 33 (4) ◽  
pp. 959-970 ◽  
Author(s):  
Tamás Tettamanti ◽  
Alfréd Csikós ◽  
Krisztián Balázs Kis ◽  
Zsolt János Viharos ◽  
István Varga
Keyword(s):  
Neural Network ◽  
Input Data ◽  
Road Traffic ◽  
Urban Traffic ◽  
Traffic Network ◽  
Data Handling ◽  
Urban Road ◽  
Traffic Sensors ◽  
Network Training ◽  
Artificial Neural Network Ann

A full methodology of short-term traffic prediction is proposed for urban road traffic network via Artificial Neural Network (ANN). The goal of the forecasting is to provide speed estimation forward by 5, 15 and 30 min. Unlike similar research results in this field, the investigated method aims to predict traffic speed for signalized urban road links and not for highway or arterial roads. The methodology contains an efficient feature selection algorithm in order to determine the appropriate input parameters required for neural network training. As another contribution of the paper, a built-in incomplete data handling is provided as input data (originating from traffic sensors or Floating Car Data (FCD)) might be absent or biased in practice. Therefore, input data handling can assure a robust operation of speed forecasting also in case of missing data. The proposed algorithm is trained, tested and analysed in a test network built-up in a microscopic traffic simulator by using daily course of real-world traffic.

Improved Artificial Potential Field Method Manipulator Inverse Kinematics

2013 ◽  
Vol 273 ◽  
pp. 119-123
Author(s):  
Ding Jin Huang ◽  
Teng Liu
Keyword(s):  
Potential Field ◽  
Inverse Kinematics ◽  
Search Space ◽  
Field Method ◽  
Robotic Arm ◽  
Artificial Potential Field ◽  
Robot Arm ◽  
Potential Field Method ◽  
Inverse Kinematics Problem ◽  
Artificial Potential Field Method

The use of traditional analytical method for manipulator inverse kinematics is able to get a display solution with the limitations of the application, only when the robotic arm has a specific structure. In view of the insufficient, this paper presents an improved artificial potential field method to solve the inverse kinematics problem of the manipulator which does not have a special structure. Firstly, establish the standard DH model for the robot arm. Then the strategy that improves search space of artificial potential field method and motion control standard is presented by combining artificial potential field method with the manipulator. Finally, the simulation results show that the proposed method is effective.

scholarly journals Colored Object Sorting using 5 DoF Robot Arm based Artificial Neural Network (ANN) Method

2018 ◽  
Vol 1090 ◽  
pp. 012070
Author(s):  
W. S. Mada Sanjaya ◽  
Dyah Anggraeni ◽  
Madinatul Munawwaroh ◽  
M. Yusuf S. Nurasyidiek ◽  
Darmawan Setia Rahayu ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Robot Arm ◽  
Object Sorting ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

scholarly journals Radial Basis Function Artificial Neural Network for the Investigation of Thyroid Cytological Lesions

2020 ◽  
Vol 2020 ◽  
pp. 1-14
Author(s):  
Christos Fragopoulos ◽  
Abraham Pouliakis ◽  
Christos Meristoudis ◽  
Emmanouil Mastorakis ◽  
Niki Margari ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Radial Basis Function ◽  
Basis Function ◽  
Histological Evaluation ◽  
Image Analysis System ◽  
Training Set ◽  
Test Set ◽  
Radial Basis ◽  
Artificial Neural

Objective. This study investigates the potential of an artificial intelligence (AI) methodology, the radial basis function (RBF) artificial neural network (ANN), in the evaluation of thyroid lesions. Study Design. The study was performed on 447 patients who had both cytological and histological evaluation in agreement. Cytological specimens were prepared using liquid-based cytology, and the histological result was based on subsequent surgical samples. Each specimen was digitized; on these images, nuclear morphology features were measured by the use of an image analysis system. The extracted measurements (41,324 nuclei) were separated into two sets: the training set that was used to create the RBF ANN and the test set that was used to evaluate the RBF performance. The system aimed to predict the histological status as benign or malignant. Results. The RBF ANN obtained in the training set has sensitivity 82.5%, specificity 94.6%, and overall accuracy 90.3%, while in the test set, these indices were 81.4%, 90.0%, and 86.9%, respectively. Algorithm was used to classify patients on the basis of the RBF ANN, the overall sensitivity was 95.0%, the specificity was 95.5%, and no statistically significant difference was observed. Conclusion. AI techniques and especially ANNs, only in the recent years, have been studied extensively. The proposed approach is promising to avoid misdiagnoses and assists the everyday practice of the cytopathology. The major drawback in this approach is the automation of a procedure to accurately detect and measure cell nuclei from the digitized images.

Prediction of the maximum nonseizure load of lubricant additives

2017 ◽  
Vol 16 (02) ◽  
pp. 1750014 ◽  
Author(s):  
Xinliang Yu ◽  
Rimeng Zhan ◽  
Jiyong Deng ◽  
Xianwei Huang
Keyword(s):  
Lubricant Additives ◽  
Structure Property ◽  
Ann Model ◽  
Training Set ◽  
Qspr Model ◽  
Test Set ◽  
Base Oil ◽  
Chemical Structures ◽  
Artificial Neural Network Ann ◽  
Backward Propagation

Lubricating additives can improve the lubricant performance of base oil in reducing friction and wear and minimizing loss of energy. It is of great significance to study the relationship between chemical structures and lubrication properties of lubricant additives. This paper reports a quantitative structure–property relationship (QSPR) model of the maximum nonseizure loads ([Formula: see text]) of 79 lubricant additives by applying artificial neural network (ANN) based on the algorithm of backward propagation of errors. Six molecular descriptors appearing in the multiple linear regression (MLR) model were used as vectors to develop the ANN model. The optimal condition of ANN with network structure of [6-4-1] was obtained by adjusting various parameters by trial-and-error. The root-mean-square (rms) errors from ANN model are [Formula: see text] ([Formula: see text]) for the training set and [Formula: see text] ([Formula: see text]) for the test set, which are superior to the MLR results of [Formula: see text] ([Formula: see text]) for the training set and [Formula: see text] ([Formula: see text]) for the test set. Compared to the existing model for [Formula: see text], our model has better statistical quality. The results indicate that our ANN model can be applied to predict the [Formula: see text] values for lubricant additives.

Neuronet-Based Approach for Assessing Liquefaction Potential of Soils

1998 ◽  
Vol 1633 (1) ◽  
pp. 3-8 ◽  
Author(s):  
Hossam Eldin Ali ◽  
Yacoub M. Najjar
Keyword(s):  
Neural Network ◽  
Numerical Approach ◽  
Soil Liquefaction ◽  
Data Sets ◽  
Ann Model ◽  
Neural Network Approach ◽  
Liquefaction Potential ◽  
Network Training ◽  
Artificial Neural Network Ann ◽  
Hidden Layer

A backpropagation artificial neural network (ANN) algorithm with one hidden layer was used as a new numerical approach to characterize the soil liquefaction potential. For this purpose, 61 field data sets representing various earthquake sites from around the world were used. To develop the most accurate prediction model for liquefaction potential, alternating combinations of input parameters were used during the training and testing phases of the developed network. The accuracy of the designed network was validated against an additional 44 records not used previously in either the network training or testing stages. The prediction accuracy of the neural network approach–based model is compared with predictions obtained by using fuzzy logic and statistically based approaches. Overall, the ANN model outperformed all other investigated approaches.

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