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/.

翻译：移动性、功耗及价格因素常导致机器人无法搭载充足的计算资源以按预期速率运行当代机器人算法。AWS、GCP、Azure等云计算服务提供商可提供强大的计算能力及日益降低的按需延迟，但机器人如何接入这些算力仍非易事。本文提出FogROS2——一个开源平台，旨在支持云与雾机器人技术，并已纳入机器人操作系统2（ROS 2）发行版。FogROS2相较于其前身FogROS1在九个维度具有显著区别，包括更低的延迟、开销与启动时间，更强的易用性，以及额外的自动化功能（如区域与计算机类型选择）。此外，FogROS2继承了ROS 2的性能优化、时序提升及其他改进。在典型机器人应用中，FogROS2使SLAM延迟降低50%，抓取规划时间从14秒缩短至1.2秒，运动规划速度提升45倍。与FogROS1相比，FogROS2的网络利用率最高降低3.8倍，启动时间缩短63%，利用视频压缩传输图像时的网络往返延迟减少97%。FogROS2的源代码、示例及文档详见https://github.com/BerkeleyAutomation/FogROS2，亦可通过官方ROS 2仓库https://index.ros.org/p/fogros2/获取。