scholarly journals SELF-LEARNING OF DELTA ROBOT USING INVERSE KINEMATICS AND ARTIFICIAL NEURAL NETWORKS

SINERGI ◽  
2021 ◽  
Vol 25 (3) ◽  
pp. 237
Author(s):  
Zendi Iklima ◽  
Muhammad Imam Muthahhar ◽  
Asif Khan ◽  
Arifiansyah Zody
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Inverse Kinematic ◽  
End Effector ◽  
Motion Data ◽  
Average Accuracy ◽  
Stepper Motors ◽  
Average Loss ◽  
Artificial Neural ◽  
Delta Robot

As known as Parallel-Link Robot, Delta Robot is a kind of Manipulator Robot that consists of three arms mounted in parallel. Delta Robot has a central joint constructed as an end-effector represented as a gripper. An Analysis of Inverse Kinematic (IK) used to convert the end-effector trajectory (X, Y) into rotations of stepper motors (ZA, ZB and ZC). The proposed method used Artificial Neural Networks (ANNs) to simplify the process of IK solver. The IK solver generated the datasets contain motion data of the Delta robot. There are 11 KB Datasets consist of 200 motion data used to be trained. The proposed method was trained in 58.78 seconds in 5000 iterations. Using a learning rate (α) 0.05 and produced the average accuracy was 97.48%, and the average loss was 0.43%. The proposed method was also tested to transfer motion data over Socket.IO with 115.58B in 6.68ms.

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.

scholarly journals IMPLEMENTASI JARINGAN SYARAF TIRUAN BACKPROPAGATION DENGAN ALGORITMA CONJUGATE GRADIENT UNTUK KLASIFIKASI KONDISI RUMAH (Studi Kasus di Kabupaten Cilacap Tahun 2018)

2020 ◽  
Vol 9 (1) ◽  
pp. 41-49
Author(s):  
Johanes Roisa Prabowo ◽  
Rukun Santoso ◽  
Hasbi Yasin
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Conjugate Gradient ◽  
Training Data ◽  
Gradient Algorithm ◽  
Output Layer ◽  
Average Accuracy ◽  
Testing Data ◽  
Artificial Neural ◽  
Hidden Layer

House is one aspect of the welfare of society that must be met, because house is the main need for human life besides clothing and food. The condition of the house as a good shelter can be known from the structure and facilities of buildings. This research aims to analyze the classification of house conditions is livable or not livable. The method used is artificial neural networks (ANN). ANN is a system information processing that has characteristics similar to biological neural networks. In this research the optimization method used is the conjugate gradient algorithm. The data used are data of Survei Sosial Ekonomi Nasional (Susenas) March 2018 Kor Keterangan Perumahan for Cilacap Regency. The data is divided into training data and testing data with the proportion that gives the highest average accuracy is 90% for training data and 10% for testing data. The best architecture obtained a model consisting of 8 neurons in input layer, 10 neurons in hidden layer and 1 neuron in output layer. The activation function used are bipolar sigmoid in the hidden layer and binary sigmoid in the output layer. The results of the analysis showed that ANN works very well for classification on house conditions in Cilacap Regency with an average accuracy of 98.96% at the training stage and 97.58% at the testing stage.Keywords: House, Classification, Artificial Neural Networks, Conjugate Gradient

Inverse Kinematic Solutions Using Artificial Neural Networks

2014 ◽  
Vol 534 ◽  
pp. 137-143 ◽  
Author(s):  
Iskandar Petra ◽  
Liyanage C. de Silva
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Inverse Kinematics ◽  
Joint Angle ◽  
Inverse Kinematic ◽  
Task Execution ◽  
Advantages And Disadvantages ◽  
Complex Process ◽  
Inverse Kinematics Problem ◽  
Artificial Neural

Inverse Kinematics solutions are needed for control of robotic manipulators for successful task execution. It is the process of obtaining the required manipulator joint angle values for a given desired end point position and orientation. In general the process of obtaining these joint angle values is a complex process that may require some higher computational power in the hardware. Mainly there are three traditional methods used to solve inverse kinematics problem, namely; geometric methods, algebraic methods and iterative methods. Apart from these traditional techniques researchers have looked into the use of Artificial Neural Networks (ANNs). In this paper we re-visit these non-traditional techniques and compare the advantages and disadvantages of each method.

scholarly journals Artificial Neural Networks in Classification of Steel Grades Based on Non-Destructive Tests

Materials ◽  
2020 ◽  
Vol 13 (11) ◽  
pp. 2445 ◽  
Author(s):  
Alexey Beskopylny ◽  
Alexandr Lyapin ◽  
Hubert Anysz ◽  
Besarion Meskhi ◽  
Andrey Veremeenko ◽  
...  
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Experimental Studies ◽  
Steel Grade ◽  
Special Device ◽  
Average Accuracy ◽  
Long Time ◽  
Steel Grades ◽  
Artificial Neural ◽  
Machinery Manufacturing

Assessment of the mechanical properties of structural steels characterizing their strength and deformation parameters is an essential problem in the monitoring of structures that have been in operation for quite a long time. The properties of steel can change under the influence of loads, deformations, or temperatures. There is a problem of express determination of the steel grade used in structures—often met in the practice of civil engineering or machinery manufacturing. The article proposes the use of artificial neural networks for the classification and clustering of steel according to strength characteristics. The experimental studies of the mechanical characteristics of various steel grades were carried out, and a special device was developed for conducting tests by shock indentation of a conical indenter. A technique based on a neural network was built. The developed algorithm allows with average accuracy—over 95%—to attribute the results to the corresponding steel grade.

scholarly journals Artificial Neural Networks for Resources Optimization in Energetic Environment

2021 ◽  
Author(s):  
Gianni D'Angelo ◽  
Francesco Palmieri ◽  
Antonio Robustelli
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Resource Planning ◽  
Training Dataset ◽  
Software Products ◽  
Planning Optimization ◽  
Average Accuracy ◽  
Common Task ◽  
Artificial Neural ◽  
Using Data

Abstract Resource Planning Optimization (RPO) is a common task that many companies need to face to obtain several benefits, like budget improvements and run-time analyses. It is often addressed by using several software products and tools, based on sophisticated mathematical artifacts. However, these tools are not able to provide a practical solution because they are often expensive and time-consuming. On the other hand, Artificial Intelligence-based approaches have been increasingly used in many industrial and scientific fields in last decades, and have demonstrated to be a valid alternative to the classical mathematical-based methods. For this purpose, the following paper aims to investigate the use of multiple Artificial Neural Networks (ANNs) for solving a RPO problem related to the scheduling of different Combined Heat & Power (CHP) generators. The experimental results, carried out by using data extracted by considering a real Microgrid system, have confirmed the effectiveness of the proposed approach. Additionally, we show that multiple neural networks achieve up to a 6% improvement in average accuracy over Naive Bayes classifier, up to a 12% over Multi-Layer Perceptron classifier and up to a 13% over state-of-the-art ANNs in the presence of unbalanced training dataset.

scholarly journals DEVELOPMENT OF 5 DOF ROBOTIC ARM PROTOTYPE CONTROL SYSTEM IN BRACHYTHERAPY PREPARATION BASED ON ARTIFICIAL NEURAL NETWORK

2021 ◽  
Vol 15 (2) ◽  
pp. 42
Author(s):  
Adi Abimanyu, M.Eng ◽  
Misbah Habib Putra ◽  
Muhtadan Muhtadan
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Artificial Neural Networks ◽  
Control System ◽  
Inverse Kinematic ◽  
Robotic Arm ◽  
Cancer Tissue ◽  
Artificial Neural ◽  
Radioactive Sources

Brachytherapy is a cancer treatment that uses radioactive sources with temporary or permanent implantation in cancer tissue. The theraphy uses a radioactive Ir-192 source wrapped in a stainless steel capsule with a diameter of 0.5 mm and a length of 4 mm. The Center for Radioisotopes and Radiopharmaceutical Technology applies a remote manipulator to manufacture microcapsules, which affects the accuracy and risks of the radiation received by the operator. Therefore, to solve this problem, it is necessary to design a 5 DoF robotic arm based on artificial neural networks as a radioactive source transfer tool to improve the precision and safety of operators in preparing the radioactive sources. In developing the 5 DoF robotic arm control system, the NImyRIO was employed, which can control the servo motor, relay pump and valve reality, image processing, and inverse kinematic. The inverse kinematic uses the neural network method with a forward kinematic validation. The inverse kinematic test obtains the RMSE value of 2.78932 for x, 5.05205 for y, and 12.641 for z in the inverse kinematic test of artificial neural networks. Therefore, the inverse kinematic accuracy of the artificial neural network needs to be redeveloped.

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