Inspiration
The idea for Robotag was born from a painful personal experience: losing 300k worth of valuable items during the first week on campus. This inspired a mission to solve a common problem for students and faculty: the frequent loss or misplacement of critical belongings in complex, large-scale environments like a university campus. The inspiration is simple: create a reliable, simple system that guarantees an item is never lost again.
What it does
Robotag is a precision location and tracking system designed to keep track of critical belongings. • Simple Tags: Users receive a set of simple, trackable tags (5 tags per user) to attach to high-value items like laptops, books, or equipment. • Real-Time Campus Map: The system displays the real-time location of all tagged items on an interactive Campus Map interface. • Active Tracking: Users can view the status of their tags ("Active") and see the last known location of their belongings instantly. • Core Value: It helps students and staff keep track of their personal property, dramatically reducing loss and the associated cost and stress.
How we built it
We aimed for a system that was both low-cost and highly scalable for a university environment. • Front-end Interface (Web/Mobile): A clean interface featuring the Campus Map. This interface handles user authentication and displays the location data using a mapping library Leaflet • Location Tracking Network: The system relies on NB-IoT tags. We deployed or simulated tags antennae across the campus to act as scanners/nodes that constantly report the proximity of the attached tags. • Back-end and Data Management: The back end, built on Python, manages the user database, tag assignment, and processes the raw proximity data from the scanners. Firebase Database stores the user-to-tag mapping and the latest location coordinates. • 3D Mapping Logic: We implemented a triangulation or RSSI-based localization algorithm to convert the proximity readings into precise, real-time coordinates displayed on the 3D campus map.
Challenges we ran into
• Indoor Accuracy: Achieving high location precision (sub-meter accuracy) within complex multi-story buildings proved difficult due to signal interference • Battery Optimization: Balancing the required update frequency for real-time tracking with maximizing the battery life of the small, passive tracking tags. • Data Handling: Managing and processing the large volume of real-time location updates generated by numerous tags and scanners simultaneously.
Accomplishments that we're proud of
• • Functional MVP: Successfully deploying a minimum viable product that can track 5 items across a large, simulated campus area in real-time. • Intuitive UX: Developing a simple, clean Campus Map interface that users found immediately easy to navigate and understand. • Successful Proof of Concept: Proving that our chosen hardware and software integration can reliably prevent the loss of high-value items in a test environment.
What we learned
• The complexity of deploying a reliable indoor positioning system requires meticulous planning of beacon placement and significant calibration time. • User feedback revealed a higher priority for long battery life over continuous real-time updates • We significantly underestimated the time required for data visualization and making the map look both accurate and aesthetically pleasing.
What's next for robotag
• Scale and Integration: Integrate Robotag with existing university IT systems and scale the deployment to cover a full campus building. • Automated Inventory: Develop a feature that allows departments (not just students) to use Robotag for automatic inventory tracking of shared resources and equipment. • Predictive Alerts: Implement machine learning to detect unusual movement patterns (e.g., an item leaving its typical zone late at night) and send proactive security alerts.
Log in or sign up for Devpost to join the conversation.