迈向无菌处理应用的自主器械托盘组装
Towards Autonomous Instrument Tray Assembly for Sterile Processing Applications
AI总结：
本研究提出了一种自动化机器人系统，用于无菌处理应用中的器械托盘组装，通过混合感知管道和规则打包算法提高效率和安全性。
摘要：
无菌处理与分配（SPD）部门负责在手术之间清洁、消毒、检查和组装手术器械。手动检查和准备器械托盘是一项耗时且容易出错的任务，常易导致污染和器械损坏。在本工作中，我们提出了一种完全自动化的机器人系统，用于对手术器械进行排序和结构化打包，重点在于自动化SPD组装阶段。一个包含31种手术器械和6975张标注图像的定制数据集被收集，用于训练使用YOLO12进行检测和基于级联ResNet的模型进行细粒度分类的混合感知管道。该系统集成了校准的视觉模块、配备定制双电磁夹爪的6自由度Staubli TX2-60L机械臂以及基于规则的打包算法，该算法在运输过程中减少器械碰撞。打包框架使用3D打印的隔板和支架来物理隔离器械，在运输过程中减少碰撞和摩擦。实验评估显示，感知准确率高，并且与人工组装托盘相比，工具到工具的碰撞显著减少。本工作是实现自动化SPD工作流程的可扩展第一步，提高了手术准备的安全性和一致性，同时减少了SPD处理时间。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
