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As many robot automation applications increasingly rely on multi-coreprocessing or deep-learning models, cloud computing is becoming an attractiveand economically viable resource for systems that do not contain high computingpower onboard. Despite its immense computing capacity, it is often underused bythe robotics and automation community due to lack of expertise in cloudcomputing and cloud-based infrastructure. Fog Robotics balances computing anddata between cloud edge devices. We propose a software framework, FogROS, as anextension of the Robot Operating System (ROS), the de-facto standard forcreating robot automation applications and components. It allows researchers todeploy components of their software to the cloud with minimal effort, andcorrespondingly gain access to additional computing cores, GPUs, FPGAs, andTPUs, as well as predeployed software made available by other researchers.FogROS allows a researcher to specify which components of their software willbe deployed to the cloud and to what type of computing hardware. We evaluateFogROS on 3 examples: (1) simultaneous localization and mapping (ORB-SLAM2),(2) Dexterity Network (Dex-Net) GPU-based grasp planning, and (3) multi-coremotion planning using a 96-core cloud-based server. In all three examples, acomponent is deployed to the cloud and accelerated with a small change insystem launch configuration, while incurring additional latency of 1.2 s, 0.6s, and 0.5 s due to network communication, the computation speed is improved by2.6x, 6.0x and 34.2x, respectively. Code, videos, and supplementary materialcan be found at https://github.com/BerkeleyAutomation/FogROS.

FogROS: An Adaptive Framework for Automating Fog Robotics Deployment