Sinergi
Vol 25, No 3 (2021)

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

Zendi Iklima (Department of Electrical Engineering, Faculty of Engineering, Universitas Mercu Buana)
Muhammad Imam Muthahhar (Department of Electrical Engineering, Faculty of Engineering, Universitas Mercu Buana)
Asif Khan (School of Computer Science and Technology, Beijing Institute of Technology)
Arifiansyah Zody (Department of Information Technology, Faculty of Computer Science, Esa Unggul University)

Article Info

Publish Date
02 Jul 2021

Abstract

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.

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Journal Info
Sinergi
Website

Abbrev

sinergi

Publisher

Universitas Mercu Buana

Subject

Civil Engineering, Building, Construction & Architecture Control & Systems Engineering Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering

Description

SINERGI is a peer-reviewed international journal published three times a year in February, June, and October. The journal is published by Faculty of Engineering, Universitas Mercu Buana. Each publication contains articles comprising high quality theoretical and empirical original research papers, ...