An Autonomous Guided Vehicle System for Smart Campus with Optimal Path Planning and Voice Interaction Using YOLO Network and LiDAR

Abstract

Navigating large, unfamiliar campuses can be challenging for visitors, even with campus maps available. To address this issue, this study proposes an intelligent, autonomous campus navigation system to help users reach their destinations efficiently. The proposed system integrates computer vision, LiDAR, speech recognition, global positioning, and path-planning technologies to provide accurate, user-friendly guidance in complex environments. A YOLO-based neural network performs real-time building recognition, while a speech recognition module interprets users’ spoken destination requests and commands. GPS data are mapped to campus map coordinates to improve localization accuracy, and Dijkstra’s algorithm computes optimal navigation paths. All components are integrated into a graphical user interface that provides real-time visual feedback, including recognized building names and current location. Experimental results demonstrate that the proposed system achieves reliable building recognition, accurate speech understanding, and effective route planning, significantly reducing navigation time for users unfamiliar with the campus. The proposed framework not only enhances smart campus navigation but also shows strong potential for extension to other large-scale environments such as hospitals and shopping malls.