论文标题
fogros2：使用ROS 2的云和雾机器人技术的自适应平台
FogROS2: An Adaptive Platform for Cloud and Fog Robotics Using ROS 2
论文作者
论文摘要
移动性，功率和价格点通常表明机器人在板上没有足够的计算能力来以所需的价格运行现代机器人算法。 AWS，GCP和Azure等云计算提供商提供了巨大的计算能力和需求越来越低的潜伏期，但是从机器人中攻入该功率是非凡的。我们提出了Fogros2，这是一个开源平台，可促进机器人操作系统2（ROS 2）分布中包含的云和雾化机器人技术。 FogroS2在9种方式中与其前任Fogros1不同，包括较低的潜伏期，开销和启动时间；提高了可用性和其他自动化，例如区域和计算机类型选择。此外，FogroS2获得了与ROS 2相关的效果，时机和其他改进。在常见的机器人应用中，FogroS2可将SLAM延迟降低50％，将GRASP的计划时间从14 s减少到1.2 s，并加快运动计划45倍。与FogroS1相比，FogroS2将网络利用率降低了3.8倍，将启动时间提高了63％，并且使用视频压缩的图像，网络往返潜伏期降低了97％。 fogros2的源代码，示例和文档可在https://github.com/berkeleyautomation/fogros2上找到，可通过https://index.ros.org/p/fogros2/的官方ROS 2存储库获得。
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/.
