AI driven automated library assistance using pick-to-light system

Abstract

This paper presents the design and implementation of a library assistance system that uses a pick-to-light mechanism and integrates AI-based book recommendations. Book retrieval done manually in libraries is frequently time-consuming, prone to errors, and inefficient. As a solution, we will suggest a hybrid library assistance system, which would be based on an ATmega328 microcontroller, user identification by RFID, pick-to-light, and an AI-based collaborative filtering recommender. The user is directed to the preferred books through the LEDs placed on the shelves, and the AI module gives the user personalized suggestions. The experimental outcomes of 100 users show significant advances over the old traditional manual processes. The time spent searching for a book dropped by 96.4 seconds to 34.8 seconds, the error rate dropped to 3.2%, user satisfaction went up to 4.6, and the success rate of the entire book search process went up to 98.1%. The 500 retrieval cycles of the stress testing showed stability, reliability, and uniformity of the system, and the performance of the LED-RFID. These findings suggest that the proposed system is highly efficient for the library, minimizes human error, and positively impacts the user experience, making it applicable to real-life library environments.