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

SINERGI

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Field Value
 
Title SELF-LEARNING OF DELTA ROBOT USING INVERSE KINEMATICS AND ARTIFICIAL NEURAL NETWORKS
 
Creator Iklima, Zendi
Muthahhar, Muhammad Imam
Khan, Asif
Zody, Arifiansyah
 
Subject
Artificial Neural Network; Delta Robot; Inverse Kinematics; Motion Data; Socket.IO;
 
Description 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.
 
Publisher Universitas Mercu Buana
 
Contributor
 
Date 2021-07-02
 
Type info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
Peer-reviewed Article
 
Format application/pdf
 
Identifier https://publikasi.mercubuana.ac.id/index.php/sinergi/article/view/8346
10.22441/sinergi.2021.3.001
 
Source SINERGI; Vol 25, No 3 (2021); 237-244
24601217
14102331
 
Language eng
 
Relation https://publikasi.mercubuana.ac.id/index.php/sinergi/article/view/8346/4862
https://publikasi.mercubuana.ac.id/index.php/sinergi/article/downloadSuppFile/8346/1196
 
Rights Copyright (c) 2021 SINERGI
 

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