发表机构
UCLA（加州大学洛杉矶分校）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
提出一种被动视觉系统Labware Setup Checker，通过外部摄像头和独立软件在数秒内验证自动化液体处理工作站的台面设置，实现高准确率且不干扰运行。
AI 中文摘要
自动化液体处理工作站执行数字协议，但操作员必须组装台面并确认物理设置与预期实验相符。我们提出了Labware Setup Checker，一种被动视觉系统，在不修改Opentrons Flex硬件或固件的情况下验证台面准备情况。安装在机器人外部的一台消费级网络摄像头向浏览器应用程序提供图像。该检查器将台面分割为槽位，利用已知台面几何形状纠正槽位分配，对实验室器具进行分类，并将观察到的设置与参考协议库进行比较，以报告缺失、错放或意外的实验室器具。它独立于机器人控制器运行，不会中断或门控运行。在SwabSeq呼吸道病毒面板（RVP）PCR前台面准备期间，检查器跟踪了一个槽位从空到基底再到完整的板-基底组件的状态，在机器人运动前的全部29帧中识别出完整组件。在这些帧中，需要精确匹配的槽位的348次实验室器具分类中，95.1%与协议参考一致。在480帧部分遮挡的储液器图像中，全部6,720次槽位分配均正确。在三种相机位置和两种光照条件下，槽位分配100%正确，97.73%的实验室器具分类与协议参考匹配。在仅使用CPU的笔记本电脑上，中位验证时间为2.10秒；在实验室AWS部署中，从帧捕获到收到结果的时间为4.24秒。这些结果表明，外部相机和独立软件可以在数秒内根据协议要求验证台面设置，允许操作员在开始运行前审查物理设置。
英文摘要
Automated liquid handlers execute digital protocols, but operators must assemble the deck and confirm that the physical setup matches the intended experiment. We present the Labware Setup Checker, a passive vision system that verifies deck preparation on an Opentrons Flex without modifying its hardware or firmware. A consumer webcam mounted outside the robot supplies images to a browser application. The checker segments the deck into slots, corrects slot assignments using the known deck geometry, classifies the labware, and compares the observed setup with a reference protocol library to report missing, misplaced, or unexpected labware. It operates independently of the robot controller and does not interrupt or gate a run. During SwabSeq Respiratory Viral Panel (RVP) pre-PCR deck preparation, the checker tracked a slot from empty to a base and then to the completed plate-and-base assembly, identifying the completed assembly in all 29 frames before robot motion. Across these frames, 95.1% of the 348 labware classifications for slots requiring exact matches agreed with the protocol reference. In 480 frames with partially occluded reservoirs, all 6,720 slot assignments were correct. Across three camera positions and two lighting conditions, slot assignment was 100% correct, and 97.73% of labware classifications matched the protocol reference. Median verification time was 2.10 s on a laptop using only its CPU and 4.24 s from frame capture to receipt of results during AWS deployment in the laboratory. These results show that an external camera and independent software can provide verification of deck setup against protocol requirements within seconds, allowing operators to review the physical setup before starting a run.
