CNAV: AI-Assisted Campus Navigation System and Resource Finding

M. Sharanya, D. Sricharan, D.S. Sai Darahas, Mr. M.Praveen Kumar

Abstract


Anyone who has wandered through an unfamiliar academic building knows the frustration — wrong floor, wrong wing, late for class. What a pain. Large engineering campuses make this problem significantly more troublesome, and almost every institutions still haven’t found a good digital solution for it. Introducing CNAV, an AI-Assisted Campus Navigation System we built for College campuses to tackle exactly this challenge. CNAV paints the campus as a weighted graph in PostgreSQL, where nodes are locations and edges carry traversal distances. Dijkstra’s algorithm, which is running on Go, figures out the best possible routes across floors and picks the fastest route to the destination. Users interact with a React/TypeScript frontend that renders floor plans, draws path overlays, supports fuzzy room search, and walks users through directions step by step. On the admin side, a JWT-authenticated panel lets campus staff annotate floor plans visually by dropping nodes, drawing connections, uploading new images, all without touching the database directly. The paper walks through our motivation, the technical choices we made, how everything fits together, and where we plan to take it next, including an AI natural language interface and automated room detection via computer vision.


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