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Implementation of Robot Operating System on Autonomous Surface Vehicle for Trajectory Localization with You Only Look Once Method
Abstract
The development of robotics technology, especially in the field of autonomous vehicles, has made rapid progress in recent years. This study focuses on the development of a trajectory detection and localization system on an Autonomous Surface Vehicle (ASV) using the Robot Operating System (ROS) and the You Only Look Once algorithm version five (YOLOv5). ASV is an autonomous surface vehicle used for various applications, such as underwater mapping and environmental monitoring. In this study, ROS is implemented as a hardware and software integration platform to improve the accuracy of object detection and localization, especially the red and green buoys as trajectory boundaries. Testing was carried out in a real environment to assess the performance of the system, which was previously only based on simulation. The results showed that the integration of ROS and YOLOv5 increased the navigation speed of the ASV, with an increase in the average travel time from 1 minute 16.2 seconds to 1 minute 11.2 seconds, and the success of object detection reached 70% out of 50 trials. This study contributes to the development of ASV technology by increasing the accuracy, efficiency, and reliability of the system in detecting and localizing objects in complex trajectory areas.
Bahasa Indonesia
Perkembangan teknologi robotika, terutama dalam bidang kendaraan otonom, telah mengalami kemajuan pesat dalam beberapa tahun terakhir. Pada penelitian ini berfokus terhadap pengembangan sistem deteksi dan pelokalan lintasan pada Autonomous Surface Vehicle (ASV) menggunakan Robot Operating System (ROS) dan algoritma You Only Look Once versi kelima (YOLOv5). ASV merupakan kendaraan permukaan otonom yang digunakan untuk berbagai aplikasi, seperti pemetaan bawah laut dan pemantauan lingkungan. Dalam penelitian ini, ROS diimplementasikan sebagai platform integrasi perangkat keras dan lunak untuk meningkatkan akurasi deteksi dan lokalisasi objek, khususnya buoy merah dan hijau sebagai pembatas lintasan. Pengujian dilakukan dalam lingkungan nyata untuk menilai performa sistem, yang sebelumnya hanya berbasis simulasi. Hasil penelitian menunjukkan bahwa integrasi ROS dan YOLOv5 meningkatkan kecepatan navigasi ASV, dengan peningkatan rata-rata waktu tempuh dari 1 menit 16,2 detik menjadi 1 menit 11,2 detik, serta keberhasilan deteksi objek mencapai 70% dari 50 percobaan. Penelitian ini berkontribusi pada pengembangan teknologi ASV dengan peningkatan akurasi, efisiensi, dan keandalan sistem dalam mendeteksi dan melokalisasi objek di area lintasan yang kompleks.
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DOI: https://doi.org/10.21107/triac.v11i2.28070
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