使用可编程逻辑控制器进行深度学习的工业机器人抓取（PLC）
Industrial Robot Grasping with Deep Learning using a Programmable Logic Controller (PLC)
摘要：
从堆中普遍抓取各种以前看不见的物体是电子商务订单履行，制造和家庭服务机器人的巨大挑战。最近，基于深度学习的抓取方法已经证明了结果，使它们对工业部署越来越感兴趣。本文从自动化系统的角度探讨了这个问题。我们使用Dex-Net开发了一个机器人抓取系统，该系统在控制器级别完全集成。两个神经网络部署在一个靠近PLC的新型工业AI硬件加速模块上，整个系统的功率占用小于10 W。软件与硬件紧密集成，可实现快速高效的数据处理和实时通信。如果物体和接收箱非常接近，则从箱中抓取物体的成功率高达95%，每小时拾取超过350次。该系统在2019年汉诺威工业博览会（世界上最大的工业贸易博览会）和其他活动上展出，每次活动的抓取次数超过5，000次。
英文摘要：
Universal grasping of a diverse range of previously unseen objects from heaps is a grand challenge in e-commerce order fulfillment, manufacturing, and home service robotics. Recently, deep learning based grasping approaches have demonstrated results that make them increasingly interesting for industrial deployments. This paper explores the problem from an automation systems point-of-view. We develop a robotics grasping system using Dex-Net, which is fully integrated at the controller level. Two neural networks are deployed on a novel industrial AI hardware acceleration module close to a PLC with a power footprint of less than 10 W for the overall system. The software is tightly integrated with the hardware allowing for fast and efficient data processing and real-time communication. The success rate of grasping an object form a bin is up to 95 percent with more than 350 picks per hour, if object and receptive bins are in close proximity. The system was presented at the Hannover Fair 2019 (world s largest industrial trade fair) and other events, where it performed over 5,000 grasps per event.
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。
