JU-EYE: AI-Based Assistive Navigation System Using RGB-D Perception and Intelligent Path Planning

Abstract

Within the framework of assistive technologies for visually impaired users, this paper introduces JU-EYE robot, a smart simulation-based navigation system that is meant to be used on the University of Jordan campus. The proposed system combines a depth-enabled camera (RGB-D) with a YOLO detection model to categorize obstacles while also using depth data to estimate their distances and a scene sensing layer linked to an AI-assistant to generate context-aware decision making. The system uses graph algorithms to plan paths, which help users to find the best and safest routes around the campus independently. By providing real-time audio feedback through an AI-based assistant supported with bilingual voice interaction, the system provided impactful navigation and robust scene awareness.