基于深度学习的认知机器人细胞系统部件分类

Sabin Rosioru, G. Stamatescu, Iulia Stamatescu, I. Fagarasan, D. Popescu
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引用次数: 2

摘要

先进的制造系统越来越依赖智能算法来区分、建模和预测导致生产力提高的系统行为。边缘智能允许工业系统根据过程数据收集、计算和行动,同时减少与分层控制系统相关的延迟和成本,在分层控制系统中,复杂的决策是在自动化层次结构的上层生成的。更大的本地计算能力允许这种算法的在线操作,同时考虑到增加的性能要求和更低的控制回路采样周期。在这项工作中,我们提出了一种认知机器人细胞的概念,它可以在现场收集、存储和处理数据,以便在生产环境中控制机械臂。机器人单元的主要特征是嵌入式计算、开放接口和基于标准的工业通信,这些通信与硬件外设和用于验证的数字孪生模型有关。介绍了一种基于YOLOv4图像处理算法的零件分类应用,用于实时在线评估,指导ABB IRB120C机械臂的控制。结果表明了该方法的可行性和鲁棒性。定量评价强调了所实施系统的绩效。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Deep Learning based Parts Classification in a Cognitive Robotic Cell System
Advanced manufacturing systems increasingly rely on intelligent algorithms to discriminate, model and predict system behaviours that lead to increased productivity. Edge intelligence allows the industrial systems to collect, compute and act based on process data while reducing the latency and cost associated to an hierarchical control system in which complex decisions are generated in the upper layers of the automation hierarchy. Greater local computing capabilities allow the online operation of such algorithms while accounting for increased performance requirements and lower sampling periods of the control loops. In this work we present the concept of a cognitive robotic cell that collects, stores and processes data in situ for enabling the control of a robotic arm in a production setting. The main features that characterise the robotic cell are embedded computing, open interfaces, and standards-based industrial communication with hardware peripherals and digital twin models for validation. An application of part classification is presented that uses the YOLOv4 image processing algorithm for real-time and online assessment that guides the control of an ABB IRB120C robotic arm. Results illustrate the feasibility and robustness of the approach in a real application. Quantitative evaluation underlines the performance of the implemented system.
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