SELF-LEARNING ROBOT DELTA DENGAN METODE INVERSE KINEMATICS-ARTIFICIAL NEURAL NETWORK (IK-ANN)

MUTHAHHAR, MUHAMMAD IMAM (2020) SELF-LEARNING ROBOT DELTA DENGAN METODE INVERSE KINEMATICS-ARTIFICIAL NEURAL NETWORK (IK-ANN). S1 thesis, Universitas Mercu Buana Jakarta.

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Abstract

Delta Robot or known as Parallel-Link Robot is an arm robot that consist of three arms mounted in parallel with the central joint which is an effector in the form of a gripper that is useful as a moving object in the robot workspace. Delta Robot mechanism for moving the end-effector position on the X, Y, and Z axis by calculating inverse kinematics that occur on each robot arm that is useful for converting the displacement of the end-effector position into a rotation of stepper motors as an actuator. The Artificial Neural Network method is used instead of dataset-based inverse kinematics calculations. The dataset for the ANN training process is taken from the inverse kinematics calculation beforehand and the results of the training are the height of the slider on the Z-axis to be converted into a stepper motor rotation as a robot actuator. The end-effector position that programed by ANN was tested with the accuracy of the XY-axis position of 95.72% and the Z-axis of 95.15%. Keywords: Delta Robot, Inverse Kinematics, Artificial Neural Network, Arduino. Robot Delta atau lebih dikenal dengan istilah Parallel-Link Robot adalah robot lengan yang memiliki tiga lengan yang dipasang secara paralel dengan sambungan tengah (Central Joint) yang merupakan efektor berupa gripper yang berguna sebagai pemindah benda di area kerja robot. Mekanisme Robot Delta untuk melakukan perpindahan posisi end-effector pada sumbu X, Y, dan Z dengan cara memperhitungkan inverse kinematics yang terjadi pada tiap-tiap lengan robot yang berguna untuk mengkonversi perpindahan posisi end-effector menjadi putaran yang harus dilakukan oleh motor stepper sebagai aktuator. Metode Artificial Neural Network digunakan sebagai pengganti kalkulasi inverse kinematics berbasis dataset. Dataset untuk proses training ANN diambil dari kalkulasi inverse kinematics sebelumnya dan hasil training berupa nilai ketinggian slider pada sumbu-Z yang akan dikonversi menjadi putaran motor stepper sebagai aktuator robot. Posisi end-effector yang terprogram oleh ANN diuji dengan tingkat keakuratan posisi sumbu-XY 95,72% dan sumbu-Z 95,15%. Kata Kunci: Robot Delta, Inverse Kinematics, Artifical Neural Network, Arduino.

Item Type:	Thesis (S1)
Call Number CD:	FT/ELK. 20 293
Call Number:	ST/14/21/111
NIM/NIDN Creators:	41418110082
Uncontrolled Keywords:	Robot Delta, Inverse Kinematics, Artifical Neural Network, Arduino.
Subjects:	000 Computer Science, Information and General Works/Ilmu Komputer, Informasi, dan Karya Umum > 000. Computer Science, Information and General Works/Ilmu Komputer, Informasi, dan Karya Umum > 006 Special Computer Methods/Metode Komputer Tertentu 000 Computer Science, Information and General Works/Ilmu Komputer, Informasi, dan Karya Umum > 000. Computer Science, Information and General Works/Ilmu Komputer, Informasi, dan Karya Umum > 006 Special Computer Methods/Metode Komputer Tertentu > 006.3 Artificial Intelligence/Kecerdasan Buatan 100 Philosophy and Psychology/Filsafat dan Psikologi > 150 Psychology/Psikologi > 153 Conscious Mental Process and Intelligence/Intelegensia, Kecerdasan Proses Intelektual dan Mental > 153.1 Memory and Learning/Memori dan Pembelajaran > 153.15 Learning/Pembelajaran 600 Technology/Teknologi > 620 Engineering and Applied Operations/Ilmu Teknik dan operasi Terapan > 629 Other Branches of Engineering/Cabang Teknik Lainnya > 629.8 Automatic Control Engineering/Teknik Kontrol Otomatis
Divisions:	Fakultas Teknik > Teknik Elektro
Depositing User:	Putra Arsy Anugrah
Date Deposited:	14 Feb 2022 07:43
Last Modified:	02 Nov 2023 03:57
URI:	http://repository.mercubuana.ac.id/id/eprint/52531

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