PERANCANGAN SISTEM PENGENALAN OBJEK PADA HUMANOID ROBOT SECARA REAL TIME BERBASIS DEEP LEARNING

MARISTRATAMA, M YEVANDRY and Hikmarika, Hera (2023) PERANCANGAN SISTEM PENGENALAN OBJEK PADA HUMANOID ROBOT SECARA REAL TIME BERBASIS DEEP LEARNING. Undergraduate thesis, Sriwijaya University.

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Abstract

The development of today's robots is very fast, there are even robots that have shapes and abilities that are very similar to humans, such as interacting with humans, as well as the ability to recognize objects around the robot. Recognition of objects carried out by robots usually uses the help of a camera. However, at this time the object recognition has not been realized in real time on humanoid robots. So, in this study, an object recognition system will be developed around the robot, especially in the control system and robotics laboratory at the Unsri Indralaya campus. The algorithm used in this research is using the Convolutional Neural Network (CNN) algorithm. In this study, the objects detected were divided into 12 classes, namely bags, books, bottles, chairs, doors, eggs, laptops, glasses, people, tables, blackboards, and windows. The data used for object recognition is primary data taken at the Control and Robotics Engineering laboratory, which in this experiment uses 15,000 training data and 4,000 as test data. There were two experiments in this study, namely object recognition at a close distance of one meter and object recognition at a distance of two meters. The results showed that the accuracy in the simulation experiment was 60% and 100% at epoch 200 and 500. Then the accuracy in real time conditions was 60% at close range and 90% at long distance. This shows that the Convolutional Neural Network (CNN) algorithm using the ssd_mobilenet_v1_pets model has good performance in object recognition systems around humanoid robots

Item Type:	Thesis (Undergraduate)
Uncontrolled Keywords:	humanoid robot, convolutional neural network, accuracy, object detection and recognition.
Subjects:	T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK1-9971 Electrical engineering. Electronics. Nuclear engineering > TK1 Electrical engineering--Periodicals. Automatic control--Periodicals. Computer science--Periodicals. Information technology--Periodicals. Automatic control. Computer science. Electrical engineering. Information technology.
Divisions:	03-Faculty of Engineering > 20201-Electrical Engineering (S1)
Depositing User:	M Yevandry Maristratama
Date Deposited:	07 Feb 2024 04:19
Last Modified:	07 Feb 2024 04:21
URI:	http://repository.unsri.ac.id/id/eprint/140731

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