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arXiv 2024年

作者： Mayoral-Vilches, Víctor Reina-Muñoz, Juan Manuel Álvarez, Martiño Crespo Mayoral-Vilches, David Acceleration Robotics

The Robot Operating System (ROS) pubsub model played a pivotal role in developing sophisticated robotic applications. However, the complexities and real-time demands of modern robotics necessitate more efficient communication solutions that are deterministic and isochronous. This article introduces a groundbreaking approach: embedding ROS 2 message-passing infrastructure directly onto a specialized hardware chip, significantly enhancing speed and efficiency in robotic communications. Our FPGA prototypes of the chip design can send or receive packages in less than 2.5 microseconds, accelerating networking communications by more than 62× on average and improving energy consumption by more than 500× when compared to traditional ROS 2 software implementations on modern CPUs. Additionally, it dramatically reduces maximum latency in ROS 2 networking communication by more than 30,000×. In situations of peak latency, our design guarantees an isochronous response within 11 microseconds, a stark improvement over the potential hundreds of milliseconds reported by modern CPU systems under similar conditions. © 2024, CC0.

关键词： Robot Operating System

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Augmented Reality Manipulator

Augmented Reality Manipulator

引用

Innovation in Computing and Engineering (ICE), International Conference on

作者： Naman Malik Vaibhav Nijhawan Sr. Robotics Developer Acceleration Robotics Delhi India ECE Department Maharaja Agrasen Institute of Technology (GGSIPU) Delhi India

ISBN: (数字)9798350380460

ISBN: (纸本)9798350380477

This paper aims to present an implementation overview of an Android-based application to spawn a UR5 robotic arm in augmented reality and allows the user to remotely operate the real-life version of the UR5 arm to act as a test bench for robotic manipulator systems. The system utilizes the Unity Game Engine for app development and ROS Noetic with Moveit! for controlling the arm. The proposed system enables safe operation of the arm by allowing the user to simulate and validate operation on an virtual AR replica before testing it out with the real arm. This keeps the arm as well as any property in its workspace safe from unwanted damage, allowing for use cases in industrial and research-oriented applications. The interface can also be used as developing software for people who do not have access to the real arm to avoid creating time-consuming worlds for simulators like Gazebo for prototyping. Instead, the interface can provide a real-life simulator to provide a more realistic feel while testing. Once the user is satisfied with the results, the trajectory plan/joint angles can be stored and can be used on the real UR5 for similar operations.

关键词： Technological innovation Games Manipulators Software Trajectory Augmented reality Engines Testing

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FogROS2: An Adaptive Platform for Cloud and Fog robotics Using ROS 2

FogROS2: An Adaptive Platform for Cloud and Fog Robotics Usi...

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IEEE International Conference on robotics and Automation (ICRA)

作者： Jeffrey Ichnowski Kaiyuan Chen Karthik Dharmarajan Simeon Adebola Michael Danielczuk Víctor Mayoral-Vilches Nikhil Jha Hugo Zhan Edith Llontop Derek Xu Camilo Buscaron John Kubiatowicz Ion Stoica Joseph Gonzalez Ken Goldberg Department of Electrical Engineering and Computer Science University of California Berkeley CA USA Robotics Institute Carnegie Mellon University The AUTOLab UC Berkeley Acceleration Robotics Vitoria Álava Spain System Security Group Universität Klagenfurt Klagenfurt Austria Department of Industrial Engineering and Operations Research University of California Berkeley CA USA

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://***/BerkeleyAutomation/FogROS2, and is available through the official ROS 2 repository at https://***/p/FogROS2/.

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RobotPerf: An Open-Source, Vendor-Agnostic, Benchmarking Suite for Evaluating robotics Computing System Performance

RobotPerf: An Open-Source, Vendor-Agnostic, Benchmarking Sui...

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IEEE International Conference on robotics and Automation (ICRA)

作者： Víctor Mayoral-Vilches Jason Jabbour Yu-Shun Hsiao Zishen Wan Martiño Crespo-Álvarez Matthew Stewart Juan Manuel Reina-Muñoz Prateek Nagras Gaurav Vikhe Mohammad Bakhshalipour Martin Pinzger Stefan Rass Smruti Panigrahi Giulio Corradi Niladri Roy Phillip B. Gibbons Sabrina M. Neuman Brian Plancher Vijay Janapa Reddi Acceleration Robotics Spain Alpen-Adria-Universität Klagenfurt Austria Harvard University USA Georgia Institute of Technology USA Carnegie Mellon University USA Johannes Kepler University Linz Austria Ford Motor Company USA AMD USA Intel USA Boston University USA Barnard College Columbia University USA

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

We introduce RobotPerf, a vendor-agnostic bench-marking suite designed to evaluate robotics computing performance across a diverse range of hardware platforms using ROS 2 as its common baseline. The suite encompasses ROS 2 packages covering the full robotics pipeline and integrates two distinct benchmarking approaches: black-box testing, which measures performance by eliminating upper layers and replacing them with a test application, and grey-box testing, an application-specific measure that observes internal system states with minimal interference. Our benchmarking framework provides ready-to-use tools and is easily adaptable for the assessment of custom ROS 2 computational graphs. Drawing from the knowledge of leading robot architects and system architecture experts, RobotPerf establishes a standardized approach to robotics benchmarking. As an open-source initiative, RobotPerf remains committed to evolving with community input to advance the future of hardware-accelerated robotics.

关键词： System performance Pipelines Systems architecture Closed box Interference Benchmark testing Hardware

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RobotPerf: An Open-Source, Vendor-Agnostic, Benchmarking Suite for Evaluating robotics Computing System Performance

arXiv

引用

arXiv 2023年

作者： Mayoral-Vilches, Víctor Jabbour, Jason Hsiao, Yu-Shun Wan, Zishen Crespo-Álvarez, Martiño Stewart, Matthew Reina-Muñoz, Juan Manuel Nagras, Prateek Vikhe, Gaurav Bakhshalipour, Mohammad Pinzger, Martin Rass, Stefan Panigrahi, Smruti Corradi, Giulio Roy, Niladri Gibbons, Phillip B. Neuman, Sabrina M. Plancher, Brian Reddi, Vijay Janapa Acceleration Robotics Spain Alpen-Adria-Universität Klagenfurt Austria Harvard University United States Georgia Institute of Technology United States Carnegie Mellon University United States Johannes Kepler University Linz Austria Ford Motor Company United States AMD United States Intel United States Boston University United States Barnard College Columbia University United States

We introduce RobotPerf, a vendor-agnostic benchmarking suite designed to evaluate robotics computing performance across a diverse range of hardware platforms using ROS 2 as its common baseline. The suite encompasses ROS 2 packages covering the full robotics pipeline and integrates two distinct benchmarking approaches: black-box testing, which measures performance by eliminating upper layers and replacing them with a test application, and grey-box testing, an application-specific measure that observes internal system states with minimal interference. Our benchmarking framework provides ready-to-use tools and is easily adaptable for the assessment of custom ROS 2 computational graphs. Drawing from the knowledge of leading robot architects and system architecture experts, RobotPerf establishes a standardized approach to robotics benchmarking. As an open-source initiative, RobotPerf remains committed to evolving with community input to advance the future of hardware-accelerated robotics. © 2023, CC0.

关键词： Benchmarking

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FogROS2: An Adaptive Platform for Cloud and Fog robotics Using ROS 2

arXiv

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arXiv 2022年

作者： Ichnowski, Jeffrey Chen, Kaiyuan Dharmarajan, Karthik Adebola, Simeon Danielczuk, Michael Mayoral-Vilches, Víctor Jha, Nikhil Zhan, Hugo LLontop, Edith Xu, Derek Buscaron, Camilo Kubiatowicz, John Stoica, Ion Gonzalez, Joseph Goldberg, Ken Department of Electrical Engineering and Computer Science University of California BerkeleyCA United States The AUTOLab UC Berkeley United States Department of Industrial Engineering and Operations Research University of California BerkeleyCA United States Robotics Institute Carnegie Mellon University United States Acceleration Robotics Ecuador 3 1 I Álava Vitoria Spain System Security Group Universität Klagenfurt Universitätsstr. 65-67 Klagenfurt9020 Austria

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 nontrivial. 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://***/BerkeleyAutomation/FogROS2, and is available through the official ROS 2 repository at https://***/p/fogros2/. © 2022, CC BY.

关键词： Robot Operating System

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RobotCore: An Open Architecture for Hardware acceleration in ROS 2

arXiv

引用

arXiv 2022年

作者： Mayoral-Vilches, Víctor Neuman, Sabrina M. Plancher, Brian Reddi, Vijay Janapa Acceleration Robotics Ecuador 3 Alava Vitoria1 I Spain The System Security Group Universität Klagenfurt Universitätsstr. Klagenfurt65-67 9020 Austria Alias Robotics Venta de la Estrella 6 pab 130 Vitoria01006 Spain The John A. Paulson School of Engineering and Applied Sciences Harvard University CambridgeMA United States Barnard College Columbia University New YorkNY United States

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) to enable low-overhead real-time tracing and benchmarking of accelerated ROS 2 systems. 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 also 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 template and use it in conjunction with FPGA-accelerated nodes to achieve a 24.42% speedup over a CPU. © 2022, CC0.

关键词： Field programmable gate arrays (FPGA)

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