Mobile Manipulation System for In-Store Product Stocking, Disposal, and Customer Detection with Minimal Environment Modification

In recent years, competitions such as the World Robot Summit (WRS) have spurred the development of robotic systems aimed at automating operations in convenience stores and other retail outlets. However, many existing methods require extensive modifications to products and shelves, and are highly dependent on the environment, making their deployment in brick-and-mortar stores problematic. In this study, we propose a mobile manipulation system that performs highly accurate product stocking, disposal, and customer detection while minimizing environmental modifications. The proposed system consists of three elements: (1) highly accurate product position recognition using an industrial camera and ArUco markers; (2) robot arm control with variable grasping strategy, coordinate correction, collision prevention, and retry functions; and (3) customer approach detection using 2D LiDAR. Testing in a competition scenario simulating a real-world environment demonstrated improved grasping success rates, detection of discarded products under obstructed visibility, and safety, confirming both practicality and competition performance.