FogROS: An Adaptive Framework for Automating Fog Robotics Deployment

Chen, Kaiyuan Eric; Liang, Yafei; Jha, Nikhil; Ichnowski, Jeffrey; Danielczuk, Michael; Kubiatowicz, John; Goldberg, Ken

doi:10.1109/case49439.2021.9551628

Cited by 23 publications

(7 citation statements)

References 39 publications

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“…We perform all evaluations with cloud nodes deployed to AWS. This setup differs from the examples in FogROS [2], thus results from the prior paper are not directly comparable.…”

Section: E Va L U At I O Nmentioning

confidence: 87%

FogROS 2: An Adaptive and Extensible Platform for Cloud and Fog Robotics Using ROS 2

Ichnowski¹,

Chen²,

Dharmarajan³

et al. 2022

Preprint

Self Cite

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Mobility, power, and price points often dictate that robots do not have sufficient computing power on board to run modern robot algorithms at desired rates. Cloud computing providers such as AWS, GCP, and Azure offer immense computing power on demand, but tapping into that power from a robot is non-trivial. In this paper, we present FogROS 2, an easy-to-use, open-source platform to facilitate cloud and fog robotics that is compatible with the emerging Robot Operating System 2 (ROS 2) standard. FogROS 2 provisions a cloud computer, deploys and launches ROS 2 nodes to the cloud computer, sets up secure networking between the robot and cloud, and starts the application running. FogROS 2 is completely redesigned and distinct from its predecessor to support ROS 2 applications, transparent video compression and communication, improved performance and security, support for multiple cloud-computing providers, and remote monitoring and visualization. We demonstrate in example applications that the performance gained by using cloud computers can overcome the network latency to significantly speed up robot performance. In examples, FogROS 2 reduces SLAM latency by 50%, reduces grasp planning time from 14s to 1.2s, and speeds up motion planning 28x. When compared to alternatives, FogROS 2 reduces network utilization by up to 3.8x. FogROS 2, source, examples, and documentation is available at https: //github.com/BerkeleyAutomation/FogROS2.

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“…We perform all evaluations with cloud nodes deployed to AWS. This setup differs from the examples in FogROS [2], thus results from the prior paper are not directly comparable.…”

Section: E Va L U At I O Nmentioning

confidence: 87%

FogROS 2: An Adaptive and Extensible Platform for Cloud and Fog Robotics Using ROS 2

Ichnowski¹,

Chen²,

Dharmarajan³

et al. 2022

Preprint

Self Cite

View full text Add to dashboard Cite

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“…There has been previous work that has focused on ways to accelerate robotics applications by developing tools and methodologies to help roboticists leverage hardware acceleration for selected ROS Nodes and to optimize the ROS computational graph through adaptive computing [29], [30], [31], [32], [33], [34], [35], [36], [37], [38], [39], [40], [41], [42], [43], [24]. There has also been some work to accelerate the scheduling and communication layers used by ROS and ROS 2 [44], [45], [46], [47], [48], [49], [50], [51].…”

Section: B Hardware Acceleration For Ros and Rosmentioning

confidence: 99%

RobotCore: An Open Architecture for Hardware Acceleration in ROS 2

Mayoral-Vilches¹,

Neuman²,

Plancher³

et al. 2022

Preprint

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Hardware acceleration can revolutionize robotics, enabling new applications by speeding up robot response times while remaining power-efficient. However, the diversity of acceleration options makes it difficult for roboticists to easily deploy accelerated systems without expertise in each specific hardware platform. In this work, we address this challenge with RobotCore, an architecture to integrate hardware acceleration in the widely-used ROS 2 robotics software framework. This architecture is target-agnostic (supports edge, workstation, data center, or cloud targets) and accelerator-agnostic (supports both FPGAs and GPUs). It builds on top of the common ROS 2 build system and tools and is easily portable across different research and commercial solutions through a new firmware layer. We also leverage the Linux Tracing Toolkit next generation (LTTng) for low-overhead real-time tracing and benchmarking. To demonstrate the acceleration enabled by this architecture, we use it to deploy a ROS 2 perception computational graph on a CPU and FPGA.We employ our integrated tracing and benchmarking to analyze bottlenecks, uncovering insights that guide us to improve FPGA communication efficiency. In particular, we design an intra-FPGA ROS 2 node communication queue to enable faster data flows, and use it in conjunction with FPGA-accelerated nodes to achieve a 24.42% speedup over a CPU.

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“…The main drawbacks of this solution were 1) the fact that we had to manage student VMs individually and they would be pinned (constantly preventing others from accessing) to GPUs; 2) students had to configure networking manually often having trouble with the concept of floating IP; 3) each VM had to be preconfigured 9 to enable GPU acceleration of VNC with VirtualGL 10 and each container would have to be built specifically to make use of that 11 .…”

Section: A Spring 2021: Vms and Flat Networkmentioning

confidence: 99%

“…This is a fairly common restriction also in public cloud "managed K8S cluster" offerings. To circumvent this issue, we resorted to using web sockets as done in [11]: the rosbridge suite was running in the K8S pods and the robots would use rosduct to connect to it as in Figure 2. We customized both components to support bidirectional CBOR (Concise Binary Object Representation).…”

Section: B Spring 2022: K8s and Rosbridgementioning

confidence: 99%

Cloud Native Robotic Applications with GPU Sharing on Kubernetes

Toffetti¹,

Militano²,

Murphy³

et al. 2022

Preprint

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In this paper we discuss our experience in teaching the Robotic Applications Programming course at ZHAW combining the use of a Kubernetes (k8s) cluster and real, heterogeneous, robotic hardware. We discuss the main advantages of our solutions in terms of seamless "simulation to real" experience for students and the main shortcomings we encountered with networking and sharing GPUs to support deep learning workloads. We describe the current and foreseen alternatives to avoid these drawbacks in future course editions and propose a more cloud-native approach to deploying multiple robotics applications on a k8s cluster.

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FogROS: An Adaptive Framework for Automating Fog Robotics Deployment

Cited by 23 publications

References 39 publications

FogROS 2: An Adaptive and Extensible Platform for Cloud and Fog Robotics Using ROS 2

FogROS 2: An Adaptive and Extensible Platform for Cloud and Fog Robotics Using ROS 2

RobotCore: An Open Architecture for Hardware Acceleration in ROS 2

Cloud Native Robotic Applications with GPU Sharing on Kubernetes

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