FogROS2-FT:
Fault Tolerant Cloud Robotics

Cloud robotics enables robots to offload complex computational tasks to cloud servers for improved performance and easier management. However, cloud computing can be costly, cloud services may experience occasional downtime, and the connection between the robot and the cloud can be affected by fluctuations in network Quality-of-Service (QoS). To address these challenges, we present FogROS2-FT (Fault Tolerant), a multi-cloud extension that automatically replicates independent stateless robotic services, routes requests to these replicas, and returns the first response. This replication allows robots to benefit from cloud computations even when a cloud service provider experiences downtime or there is low QoS. Additionally, many cloud providers offer low-cost "spot" computing instances that may shut down unpredictably. Normally, these instances would not be suitable for cloud robotics, but FogROS2-FT's fault-tolerant design enables reliable use of such resources. We demonstrate FogROS2-FT’s fault tolerance capabilities in three simulated cloud-robotics scenarios—visual object detection, semantic segmentation, and motion planning—and one physical robot experiment involving scan-pick-and-place. Using the same hardware specifications, FogROS2-FT achieves up to a 2.2x cost reduction for motion planning and up to a 5.53x reduction in 99th percentile (P99) long-tail latency. For object detection and semantic segmentation, FogROS2-FT reduces P99 long-tail latency by 2.0x and 2.1x, respectively, under conditions of network slowdown and resource contention.

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