AI驱动的协作装配线检测：系统集成与部署挑战
AI-Driven Collaborative Assembly Line Inspection: System Integration and Deployment Challenges
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中文总结 AI 辅助
本文介绍AI-PRISM项目中装配线协作检测单元的集成与部署，结合UR10e和Comau Racer-5机器人，通过ROS 2协调，解决安全、AI鲁棒性等挑战，将质检时间缩短25%，显著降低操作员负荷。
中文摘要 AI 辅助
人工目视检测在装配线上是一个持续的制造瓶颈：长时间轮班导致的操作员疲劳降低了缺陷检测率。本文介绍了在AI-PRISM项目框架内，于Silverline厨具工厂设计和部署的一个AI辅助协作检测单元。该单元将一台搭载机器视觉缺陷检测管线的Universal Robots UR 10e协作机器人，与一台用于功能测试的Comau Racer-5协作机器人相结合，通过Ubuntu 22.04 LTS服务器上的ROS 2 Humble进行协调。多模态数据（Basler相机图像、TIA麦克风声学数据以及SPS电气安全测量数据）在本地记录，并通过Grafana实时可视化。我们报告了实际部署中的挑战（近距离安全、眩光和反射下的AI鲁棒性、两个协作机器人之间的ROS 2命名空间冲突，以及操作系统和依赖性问题），以及所采用的工程解决方案，并通过四级人机交互分析来构建集成框架。部署后的检测单元将单件质量检查时间从82秒缩短至61秒（约减少25%），将最终控制资源效率从0.75提升至0.88，将操作员目视检测观看时间减少82%，并显著降低了操作员的脑力需求（p = 0.005，NASA-TLX）。
英文摘要
Manual visual inspection on assembly lines is a persistent manufacturing bottleneck: operator fatigue over extended shifts lowers defect-detection rates. This paper presents the design, integration, and field deployment of an AI-assisted collaborative inspection cell at the Silverline kitchen-appliance factory, developed within the AI-PRISM project. The cell couples a Universal Robots UR 10e cobot carrying a machine-vision defect-detection pipeline with a Comau Racer-5 cobot for functional tests, coordinated through ROS 2 Humble on an Ubuntu 22.04 LTS server. Multi-modal data (Basler camera imagery, TIA microphone acoustics, and SPS electrical-safety measurements) are logged locally and visualised in real time with Grafana. We report the practical deployment challenges (close-proximity safety, AI robustness under glare and reflections, ROS 2 namespace collisions across two cobots, and operating-system and dependency issues) together with the engineering solutions adopted, and structure the integration through a four-level Human-Robot Interaction analysis. The deployed cell cuts per-unit quality-check time from 82 s to 61 s (about 25%), raises final-control resource efficiency from 0.75 to 0.88, reduces operator visual-inspection viewing time by 82%, and significantly lowers operator mental demand (p = 0.005, NASA-TLX).
发表机构
- TEKNOPAR（泰克诺帕尔）
机构由 AI 辅助整理，请以论文原文为准。
