5G环境下基于深度学习的云化PLC物料识别与定位系统  

Material recognition and location system with cloud programmable logic controller based on deep learning in 5G environment

作　　者：付美霞 王健全 王曲 孙雷 马彰超 张超一 管婉青 李卫 FU Meixia;WANG Jianquan;WANG Qu;SUN Lei;MA Zhangchao;ZHANG Chaoyi;GUAN Wanqing;LI Wei(School of Automation and Electrical Engineering,University of Science and Technology Beijing,Beijing 100083,China;Institute of Industrial Internet,University of Science and Technology Beijing,Beijing 100083,China;School of Computer and Communication Engineering,University of Science and Technology Beijing,Beijing 100083,China)

机构地区：[1]北京科技大学自动化学院,北京100083 [2]北京科技大学工业互联网研究院,北京100083 [3]北京科技大学计算机与通信工程学院,北京100083

出　　处：《工程科学学报》2023年第10期1666-1673,共8页Chinese Journal of Engineering

基　　金：国家重点研发计划资助项目(2020YFB1708800);广东省重点研究与开发计划资助项目(2020B010113007);中央高校基本科研业务费专项资金资助项目(FRF-IDRY-21-005);广东省基础与应用基础研究基金联合基金资助项目(2021A1515110577);中央高校基础研究基金资助项目(FRF-MP-20-37);中国博士后科学基金资助项目(2021M700385)。

摘　　要：为了解决智能制造领域中云化控制与视觉分选应用相结合的问题,提出了基于深度学习的云化可编程逻辑控制器(Programmable logic controller,PLC)物料识别与定位系统,并在端到端5G与时间敏感网络(Time sensitive networking,TSN)传输网络环境下,实现了对云化PLC架构和控制功能有效性的验证.首先,将传统PLC系统控制功能容器虚拟化,实现PLC的本地和云端自由部署;其次,在云端设计人工智能学习平台,采用基于You only look once v5(YOLOv5)的目标检测算法实现物料的定位和分类,获取目标的像素坐标和类别信息;然后,利用相机标定方法把像素坐标转换成物理世界坐标,并将目标类别、坐标、时间戳信息传输到云化PLC;最后,在5G和TSN融合网络环境下,实现云化PLC对天车设备的实时控制与复杂计算功能整合.结果表明,该系统能够有效的对多天车进行协同控制,物料定位均值平均精度(Mean average precision,mAP)达到99.65%,分选准确率达到96.67%,平均消耗时间225.99 s,满足工业低时延、高精度的视觉分选需求.Intellectualization and unmanned manufacturing have been an inevitable trend in industrial development.The landing of intelligent applications is one of the current challenges in the industry.Due to the hierarchical architecture of the industrial automation pyramid,traditional programmable logic controllers(PLCs)that are usually employed in the field cannot cooperate with artificial intelligence(AI)algorithms that require massive data and computing resources.Therefore,it is necessary to research the virtualization of traditional PLCs as dockers,which can be deployed in the cloud,edge,or field.Cloud PLCs can be easily integrated with AI,big data,and cloud computing to achieve intelligent decision-making and control and break down data islands.The visual sorting system has attracted increasing attention for its ability to accurately detect the position of objects.Many deep learning–based methods have achieved remarkable performance in computer vision.Additionally,the requirement of a network is fundamental for guaranteeing data transmission with low latency and high reliability.The combination of 5G and time-sensitive networking(TSN)can achieve the deterministic transmission of several industrial applications.According to the above challenges,joint control between cloud PLCs of low-level devices and visual sorting systems in a reliable network is critical and has industry potential.In this study,we propose a deep learning–based material recognition and location system with a cloud PLC,which is demonstrated in a 5G-TSN network.First,traditional PLC is virtualized to realize flexible PLC function deployment in the field and cloud.Second,we establish a cloud-based AI platform and design a You only look once v5(YOLOv5)-based object detection algorithm to locate the position and recognize the types of materials to obtain pixel coordinates.Third,the camera calibration method is used to transform pixel and world coordinates,and the material information consists of the world coordinates,types,and timestamps that are se

关 键 词：智能制造 视觉分选 深度学习 云化可编程逻辑控制器 5G 时间敏感网络 

分 类 号：TG142.71[一般工业技术—材料科学与工程]