FogROS2-LS: A Location-Independent Fog Robotics Framework for Latency Sensitive ROS2 Applications
Kaiyuan Chen, Michael Wang, Marcus Gualtieri, Nan Tian, Christian Juette, Liu Ren, Jeffrey Ichnowski, John Kubiatowicz, Ken Goldberg
Abstract
In Cloud Robotics, long system latency due to varying network conditions can cause instability and collisions. However, this can be minimized in the almost univeral case where there are multiple sources available for cloud servers. By extending anycast routing, we introduce FogROS2-Latency-Sensitive, a Fog Robotics framework that offers secure, location-independent connections between robots and latency-sensitive cloud-based servers. FogROS2-LS offloads conventional on-board state estimators and feedback controllers to Cloud and Edge compute hardware without modifying existing applications in ROS2. In the presence of multiple identical services, FogROS2-LS dynamically identifies and transitions to the optimal service deployment that meets latency requirements, thereby empowering robots with limited on-board computing capacity to safely and efficiently navigate dynamic, human-dense environments. We evaluate FogROS2-LS with two latency sensitive case studies: (1) Collision Avoidance: a robot arm guided by visual feedback from consistent distance estimation and collision checking on Cloud and Edge. FogROS2-LS reduces collision failures by up to 8.5x by selecting the best available server, and (2) Target Tracking: FogROS2-LS enables robust and continuous target following and can recover from network failures. Videos and code are available on the website https://sites.google.com/view/fogros2-ls.