Article Per Year (5 Year)
p-Index From 2020 - 2025
0.23
P-Index
This Author published in this journals
All Journal Tematik : Jurnal Teknologi Informasi Komunikasi
Fauzan Nursalma Mawardi
Unknown Affiliation
Computer Science & IT

Published : 1 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search

 1 
Penghindaran Rintangan Otomatis Pada Agen Otonom Berbasis End-to-End Deep Imitation Learning Supeno, Handoko; Muhammad Tirta Mulia; Mellia Liyanthy; Kevin Anggara Putra; Fauzan Nursalma Mawardi
TEMATIK Vol. 10 No. 2 (2023): Tematik : Jurnal Teknologi Informasi Komunikasi (e-Journal) - Desember 2023
Publisher : LPPM POLITEKNIK LP3I BANDUNG

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

This research addresses the issue of automatic obstacle avoidance during navigation by autonomous agents. Designing a traditional, programmed automatic obstacle avoidance system would be difficult and expensive. Therefore, a neural network-based approach is proposed, known as end-to-end deep imitation learning, where the approach is data-driven and thus relatively easier and more cost-effective compared to traditional methods. The research also proposes the architecture of a convolutional neural network design and image processing techniques for effective and efficient machine learning training. Testing is conducted on a path with randomly placed obstacles in the Webots simulator. Gradual performance evaluations demonstrate that the proposed architecture successfully trains autonomous agents to maneuver when encountering dynamic obstacles with a relatively small training dataset.
Co-Authors Kevin Anggara Putra Liyanthy, Mellia Muhammad Tirta Mulia Supeno, Handoko