Robot Pelayan Restoran Berbasis Nav2 dan YOLOv11 dengan Sistem Navigasi Cerdas serta Deteksi Objek Real-Time
DOI:
https://doi.org/10.58192/populer.v5i3.5462Keywords:
Nav2, YOLOv11, Behavior Trees, Socially Aware Navigation, ROS 2Abstract
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.
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