用现成硬件和算法打造可自动补货的机器人系统。
From Pixels to Shelf: An Integrated Robotic System for Autonomous Supermarket Stocking with a Mobile Manipulator
- 用行为树规划任务，视觉模型识别商品，双步控制精准上架。
- 700多次补货测试成功率达98%以上，接近实用水平。
- 适合零售自动化研究者，也揭示了离商用还差一步。
自主超市补货面临动态人机交互、空间受限及商品形状多样等挑战。本文提出一种高效模块化机器人系统，整合商用硬件与可扩展算法架构，实现零售场景下的全自动货架补货。核心贡献在于将现成硬件与基于ROS2的感知、规划、控制集成到统一部署平台。系统采用行为树（BTs）进行任务调度，使用微调视觉模型完成物体检测，并通过两步模型预测控制（MPC）框架结合ArUco标记实现精确货架导航。实验室环境下复现真实超市场景的实验表明，系统在超过700次补货操作中成功率超过98%。然而，对比基准显示当前系统性能与成本效益仍低于人工，凸显关键改进方向，量化了迈向大规模商业部署尚需突破的技术差距。
原文摘要 · Abstract (English)
Autonomous stocking in retail environments, particularly supermarkets, presents challenges due to dynamic human interactions, constrained spaces, and diverse product geometries. This paper introduces an efficient modular robotic system for autonomous shelf stocking, integrating commercially available hardware with a scalable algorithmic architecture. A major contribution of this work is the system integration of off-the-shelf hardware and ROS2-based perception, planning, and control into a single deployable platform for retail environments. Our solution leverages Behavior Trees (BTs) for task planning, fine-tuned vision models for object detection, and a two-step Model Predictive Control (MPC) framework for precise shelf navigation using ArUco markers. Laboratory experiments replicating realistic supermarket conditions demonstrate reliable performance, achieving over 98% success in pick-and-place operations across a total of more than 700 stocking events. However, our comparative benchmarks indicate that the performance and cost-effectiveness of current autonomous systems remain inferior to that of human workers, which we use to highlight key improvement areas and quantify the progress still required before widespread commercial deployment can realistically be achieved.
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