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.

翻译：零售环境中的自主补货，特别是超市场景，因动态人际交互、空间受限以及商品几何形状多样而面临诸多挑战。本文提出一种高效的模块化机器人系统用于自主货架补货，该系统集成了商用现成硬件与可扩展的算法架构。本研究的主要贡献在于，将商用硬件与基于ROS2的感知、规划和控制模块系统集成到一个可直接部署于零售环境的平台中。我们利用行为树（BTs）进行任务规划，采用微调视觉模型进行目标检测，并设计了两步模型预测控制（MPC）框架，借助ArUco标记实现精确的货架导航。在模拟真实超市条件的实验室实验中，该方法展现了可靠性能：在总计超过700次补货事件中，拾放操作成功率超过98%。然而，对比基准测试表明，当前自主系统的性能和成本效益仍低于人类工人。基于此，我们指出关键改进方向，并量化了在实现大规模商业部署前仍需取得的进展。