论文标题
工业机器人使用可编程逻辑控制器（PLC）进行深度学习抓住
Industrial Robot Grasping with Deep Learning using a Programmable Logic Controller (PLC)
论文作者
论文摘要
在电子商务订单实现，制造和家庭服务机器人方面，普遍抓住各种以前看不见的物体是一个巨大的挑战。最近，基于深度学习的掌握方法表明了结果，使它们在工业部署中变得越来越有趣。本文探讨了自动化系统观点的问题。我们使用DEX-NET开发机器人抓握系统，该系统已在控制器级别完全集成。两个神经网络被部署在一个新型的工业AI硬件加速模块上，靠近PLC，对于整个系统，功率足迹小于10 W。该软件与硬件紧密整合，允许快速有效的数据处理和实时通信。如果物体和接受垃圾箱紧邻，则抓住物体形式的成功率高达95％，每小时超过350次。该系统是在2019年汉诺威博览会（Hannover Fair）（世界上最大的工业贸易博览会）和其他活动中介绍的，每个活动都进行了超过5,000个grasps。
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.
