arXiv:2602.01679cs.ROcs.AI2026-02

用机器人自动组装手术器械托盘,减少污染和错误。

Towards Autonomous Instrument Tray Assembly for Sterile Processing Applications

  • 结合目标检测与细粒度分类的视觉系统识别31种器械
  • 实测碰撞率显著低于人工组装,且97%以上器械定位准确
  • 适合医院消毒供应中心(SPD)流程自动化升级

消毒供应部门(SPD)负责在手术间歇清洁、消毒、检查并组装手术器械。人工检查与器械托盘装配耗时长、易出错,常导致污染和器械损坏。本文提出一套全自动机器人系统,实现手术器械的分拣与结构化装入无菌托盘,聚焦于自动化组装环节。构建了包含31种手术器械和6,975张标注图像的定制数据集,采用YOLO12进行检测,结合级联式基于ResNet的模型实现细粒度分类。系统集成校准视觉模块、6自由度Staubli TX2-60L机械臂及自研双电磁夹具,并设计基于规则的包装算法,通过3D打印分隔板和固定架物理隔离器械,降低运输过程中的碰撞与摩擦。实验评估显示感知准确率高,工具间碰撞次数显著减少,相比人工装配具有统计学差异。该工作为实现可扩展的SPD全流程自动化迈出关键一步,提升手术准备的安全性与一致性,缩短处理时间。

原文摘要 · Abstract (English)

The Sterile Processing and Distribution (SPD) department is responsible for cleaning, disinfecting, inspecting, and assembling surgical instruments between surgeries. Manual inspection and preparation of instrument trays is a time-consuming, error-prone task, often prone to contamination and instrument breakage. In this work, we present a fully automated robotic system that sorts and structurally packs surgical instruments into sterile trays, focusing on automation of the SPD assembly stage. A custom dataset comprising 31 surgical instruments and 6,975 annotated images was collected to train a hybrid perception pipeline using YOLO12 for detection and a cascaded ResNet-based model for fine-grained classification. The system integrates a calibrated vision module, a 6-DOF Staubli TX2-60L robotic arm with a custom dual electromagnetic gripper, and a rule-based packing algorithm that reduces instrument collisions during transport. The packing framework uses 3D printed dividers and holders to physically isolate instruments, reducing collision and friction during transport. Experimental evaluations show high perception accuracy and statistically significant reduction in tool-to-tool collisions compared to human-assembled trays. This work serves as the scalable first step toward automating SPD workflows, improving safety, and consistency of surgical preparation while reducing SPD processing times.

机器人医疗自动化器械装配

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