US Ignite: Collaborative Research: Track 1: Industrial Cloud Robotics across Software Defined Networks

目前，工业机器人对于重复性和大批量的任务（如焊接和喷漆）具有成本效益，但对于小批量的混合部件生产则不具有成本效益。 非结构化工业应用对机器人零件处理的需求是多种多样的。在仓库货物配送中心，多个箱子被呈现给操作员，需要一个人来处理必须装箱和装运的一系列零件。在回收和再循环行业中，人们在传送带上对混合产品的废物流进行分类。生产中的装配和配套操作被称为？机器人的机会？但是它们需要一种用于在同一工作单元中处理多种零件类型的解决方案。该项目将研究和整合技术，使工业机器人能够用于小批量混合零件生产任务。拟议的解决方案将包括3D图像传感器和高速灵活的网络，云计算和工业机器人。包括尖端的新软件，如机器人操作系统工业（ROS-I）和云计算平台为本科生和研究生提供了良好的教育机会。该项目开发的软件将广泛分发，以使其他团队能够进一步创新。该项目的目标是开发利用高性能计算和高速软件定义网络（SDN）的云机器人应用程序。具体来说，目标应用程序将传感器数据的大数据分析（从工厂车间收集的类型）与工业机器人的控制相结合，以完成小批量混合零件生产任务。位于相对于工业机器人操作的工厂车间的远程设施的云计算机可用于计算密集型应用，例如从3D传感器数据识别对象，以及机器人执行对象操作的抓取规划。 项目方法将包括（i）集成ROS-I组件并根据需要开发新软件，以将3D传感器数据传输到远程计算机，运行对象识别和抓取规划应用程序，并将机器人指令返回到原始站点，（ii）在地理分布的计算云上运行该软件，（iii）收集测量结果并增强软件以满足实时延迟要求。技术挑战在于满足这些严格的实时要求。例如，需要具有连接任意工厂车间和车间的灵活性的高速网络，以将3D传感器数据快速传输到远程云计算机，并提供计算的机器人指令（因此，SDN）。

Currently, industrial robots are cost-effective for repetitive and high-volume tasks such as welding and painting, but not for lower-volume, mixed-part production. The need for robotic part handling for unstructured industrial applications is diverse. In manufactured-goods distribution centers, where multiple bins are presented to an operator, a human is required to handle a range of parts that must be boxed and shipped. In the reclamation and recycling industry, humans sort waste streams of mixed products on conveyor belts. Assembly and kitting operations in manufacturing are termed ?robotic opportunities? but they require a solution for handling many part types in the same work-cell. This project will research and integrate technologies to enable the use of industrial robots for low-volume mixed-part production tasks. The proposed solution will include 3D image sensors and high-speed flexible networking, cloud computing, and industrial robots. The inclusion of cutting-edge new software such as the Robot-Operating System Industrial (ROS-I) and Cloud Computing platforms offer excellent educational opportunities for both undergraduate and graduate students. The software developed in this project will be widely distributed to enable further innovations by other teams.The project objective is to develop cloud robotics applications that leverage high-performance computing and high-speed software-defined networks (SDN). Specifically, the target applications combine big-data analytics of sensor data (of the type collected from factory floors) with the control of industrial robots for low-volume, mixed-part production tasks. Cloud computers located at a remote facility relative to the factory floor on which industrial robots operate can be used for compute-intensive applications such as object identification from 3D sensor data, and grasp planning for the robots to perform object manipulation. The project methods will consist of (i) integrating ROS-I components and developing new software as required to transmit the 3D sensor data to remote computers, running the object identification and grasp planning applications, and returning robot instructions to the original site, (ii) running this software on geographically distributed compute clouds, (iii) collecting measurements and enhancing the software to meet real-time delay requirements. The technical challenge lies in meeting these stringent real-time requirements. For example, high-speed networks with the flexibility to connect arbitrary factory floors and datacenters are needed to transfer the 3D sensor data quickly to the remote cloud computers and to deliver the computed robot instructions(hence, SDN).