FogROS：一个用于自动化雾天机器人部署的自适应框架
FogROS: An Adaptive Framework for Automating Fog Robotics Deployment
摘要：
随着许多机器人自动化应用越来越依赖于多核处理或深度学习模型，对于不包含高计算能力的系统，云计算正成为一种有吸引力且经济可行的资源。尽管它具有巨大的计算能力，但由于缺乏云计算和基于云的基础设施方面的专业知识，机器人和自动化社区往往没有充分利用它。Fog Robotics在云边缘设备之间平衡计算和数据。我们提出了一个软件框架，FogROS，作为机器人操作系统（ROS），用于创建机器人自动化应用程序和组件的事实标准的扩展。它允许研究人员以最小的努力将其软件组件部署到云中，并相应地获得对其他计算核心，GPU，FPGA和TPU以及其他研究人员提供的预部署软件的访问。FogROS允许研究人员指定其软件的哪些组件将部署到云以及什么类型的计算硬件。我们在3个示例上评估FogROS：（1）同时定位与地图构建（ORB-SLAM 2），（2）基于GPU的敏捷网络（Dex-Net）抓取规划，以及（3）使用96核云服务器的多核运动规划。在所有三个示例中，组件被部署到云端并通过系统启动配置的微小变化进行加速，同时由于网络通信而产生1.2 s，0.6 s和0.5 s的额外延迟，计算速度分别提高了2.6倍，6.0倍和34.2倍。代码、视频和补充材料可以在https://github.com/BerkeleyAutomation/FogROS上找到。
英文摘要：
As many robot automation applications increasingly rely on multi-core processing or deep-learning models, cloud computing is becoming an attractive and economically viable resource for systems that do not contain high computing power onboard. Despite its immense computing capacity, it is often underused by the robotics and automation community due to lack of expertise in cloud computing and cloud-based infrastructure. Fog Robotics balances computing and data between cloud edge devices. We propose a software framework, FogROS, as an extension of the Robot Operating System (ROS), the de-facto standard for creating robot automation applications and components. It allows researchers to deploy components of their software to the cloud with minimal effort, and correspondingly gain access to additional computing cores, GPUs, FPGAs, and TPUs, as well as predeployed software made available by other researchers. FogROS allows a researcher to specify which components of their software will be deployed to the cloud and to what type of computing hardware. We evaluate FogROS on 3 examples: (1) simultaneous localization and mapping (ORB-SLAM2), (2) Dexterity Network (Dex-Net) GPU-based grasp planning, and (3) multi-core motion planning using a 96-core cloud-based server. In all three examples, a component is deployed to the cloud and accelerated with a small change in system launch configuration, while incurring additional latency of 1.2 s, 0.6 s, and 0.5 s due to network communication, the computation speed is improved by 2.6x, 6.0x and 34.2x, respectively. Code, videos, and supplementary material can be found at https://github.com/BerkeleyAutomation/FogROS.
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。
