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

Zendi Iklima; Muhammad Imam Muthahhar; Asif Khan; Arifiansyah Zody

doi:10.22441/sinergi.2021.3.001

Authors

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

DOI:

https://doi.org/10.22441/sinergi.2021.3.001

Keywords:

Artificial Neural Network, Delta Robot, Inverse Kinematics, Motion Data, Socket.IO,

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 (Z_A, Z_B and Z_C). 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.