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All Journal Populer: Jurnal Penelitian Mahasiswa
Muhammad Ridwan
Universitas Islam Sultan Agung
Humanities Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Social Sciences Other

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Robot Pelayan Restoran Berbasis Nav2 dan YOLOv11 dengan Sistem Navigasi Cerdas serta Deteksi Objek Real-Time Muhammad Ridwan; Ghufron Ghufron
Populer: Jurnal Penelitian Mahasiswa Vol. 5 No. 3 (2026): September : Populer: Jurnal Penelitian Mahasiswa
Publisher : Universitas Maritim AMNI Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58192/populer.v5i3.5462

Abstract

The context-blind limitation of 2D LiDAR distance sensors in distinguishing different types of obstacles poses a major challenge in the navigation of restaurant service mobile robots. This study aims to design and implement an intelligent autonomous navigation system based on the Navigation 2 (Nav2) framework in ROS 2, integrated with real-time visual perception using YOLOv11-based Deep Learning. The system is built using a dual-processor architecture, in which an NVIDIA Jetson Orin Nano acts as the High-Level Controller, while an ESP32-S3 microcontroller serves as the Low-Level Controller. The YOLOv11 module is trained to classify human postures into person_standing and person_sitting, which are used as additional semantic information in the Nav2 decision-making process managed by a custom Behavior Tree. Experimental results show that both systems achieved a Success Rate of 100% with zero collision incidents. The Intelligent Navigation system achieved an average Task Completion Time of 12.83 seconds, resulting in an average reduction of 1.70 seconds compared to the Standard Navigation system.
Co-Authors Ghufron Ghufron