人工智能
可编程逻辑控制器
机器人学
计算机科学
自动化
深度学习
机器人
控制器(灌溉)
对象(语法)
服务(商务)
嵌入式系统
控制工程
实时计算
工程类
操作系统
生物
农学
经济
机械工程
经济
作者
Eugen Solowjow,Ines Ugalde,Yash Shahapurkar,Juan Pablo Aparicio,Jeff Mahler,Vishal Satish,Ken Goldberg,Heiko Claussen
标识
DOI:10.1109/case48305.2020.9216902
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% 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.
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