FogROS: An Adaptive Framework for Automating Fog Robotics Deployment
Kaiyuan (Eric) Chen, Yafei Liang, Nikhil Jha, Jeffrey Ichnowski,, Michael Danielczuk, Joseph Gonzalez, John Kubiatowicz, Ken Goldberg

TL;DR
FogROS is a framework extending ROS to enable easy deployment of robot software components to cloud infrastructure, improving computational performance while managing latency, thus facilitating scalable fog robotics applications.
Contribution
It introduces FogROS, a novel extension of ROS that simplifies cloud deployment of robot software components with minimal effort and configurable hardware targeting.
Findings
Computation speed improved by up to 34.2x.
Additional latency incurred is around 0.5 to 1.2 seconds.
Successfully deployed and accelerated three robotic applications.
Abstract
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…
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Taxonomy
TopicsRobotics and Automated Systems · Modular Robots and Swarm Intelligence · Robotics and Sensor-Based Localization
