FogROS2: An Adaptive Platform for Cloud and Fog Robotics Using ROS 2

TL;DR

FogROS2 is an open-source platform integrated with ROS 2 that enhances cloud and fog robotics by reducing latency, overhead, and startup times, enabling more efficient robot applications with improved performance and usability.

Contribution

The paper introduces FogROS2, a significantly improved platform over FogROS1, with lower latency, better usability, automation features, and enhanced performance in cloud and fog robotics applications.

Findings

Reduces SLAM latency by 50%

Decreases grasp planning time from 14s to 1.2s

Speeds up motion planning by 45x

Abstract

Mobility, power, and price points often dictate that robots do not have sufficient computing power on board to run contemporary robot algorithms at desired rates. Cloud computing providers such as AWS, GCP, and Azure offer immense computing power and increasingly low latency on demand, but tapping into that power from a robot is non-trivial. We present FogROS2, an open-source platform to facilitate cloud and fog robotics that is included in the Robot Operating System 2 (ROS 2) distribution. FogROS2 is distinct from its predecessor FogROS1 in 9 ways, including lower latency, overhead, and startup times; improved usability, and additional automation, such as region and computer type selection. Additionally, FogROS2 gains performance, timing, and additional improvements associated with ROS 2. In common robot applications, FogROS2 reduces SLAM latency by 50 %, reduces grasp planning time from 14 s to 1.2 s, and speeds up motion planning 45x. When compared to FogROS1, FogROS2 reduces network utilization by up to 3.8x, improves startup time by 63 %, and network round-trip latency by 97 % for images using video compression. The source code, examples, and documentation for FogROS2 are available at https://github.com/BerkeleyAutomation/FogROS2, and is available through the official ROS 2 repository at https://index.ros.org/p/fogros2/.