  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/.
