AI-Powered Robotic Systems for Intelligent Retail Shelf Management

Abstract

Efficient retail shelf organization is crucial for maintaining product visibility and optimizing customer experience. This study presents an AI-driven robotic system designed to autonomously detect, rearrange, and replenish items in dynamic retail environments. By integrating advanced perception models with knowledge-based reasoning, the system enables precise object recognition and strategic placement, even in cluttered spaces. A multi-layered planning framework ensures seamless manipulation and adaptation to real-time shelf conditions. Experimental evaluations demonstrate the robot’s ability to execute intelligent restocking and organization strategies, offering a scalable solution for automated retail management.