FEATURE Robotics
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Hardware acceleration for robots with ROS 2
Proposed enhancements to the ROS 2 framework let roboticists use familiar skills to build the hardware needed for tomorrow’s high-performance industrial robots, writes Víctor Mayoral-Vilches, Robotics System Architect at the Adaptive & Embedded Computing Group of AMD
W
hen developing a robot, system integration consumes arguably the largest proportion of the
project’s resources – much more so than developing the end application. With the use of lower-end industrial collaborative robots, companies have emerged that focus solely on developing software to run on existing hardware. However, there is a critical relationship between the hardware and the software capabilities in a robot. Retaining design control over the computing hardware is needed to create more specialised, power effi cient, secure and high-performing robots.
Hardware challenge, software skills But, there is an obstacle that must be overcome if roboticists are to deliver the better and faster robots that will be required in the future. In today’s post- Moore computing world, upgrading hardware to adopt the latest-generation microprocessor cannot deliver the desired application-performance upgrade. Hardware acceleration is often the only way to achieve the necessary gains. This complicates life for developers in disciplines like robotics, whose skills tend to be software biased. It means they must face the prospect of designing adaptive computational hardware if they are to satisfy market demands for new industrial robots.
The main principle underlying hardware acceleration in robotics is that a mixed control- and data-driven approach for software development, unlike the traditional control-driven approach, lets teams design custom compute architectures that allocate the optimal amount of hardware resources for an application.
As far as implementation is concerned,
a heterogeneous computational model is needed. This takes advantage of the strengths of CPUs and GPUs, which
10 September 2022 | Automation
roboticists take advantage of the hardware acceleration technologies available. Ideally, such an approach should let them create their custom hardware working within a familiar development environment – such as ROS – and using familiar tools for simulation.
ROS is the de facto industry standard AMD-Xilinx Kria SOM Kria K26 System-on-Module
excel in control-fl ow computations, whilst leveraging the strengths of fi eld- programmable gate arrays (FPGAs) to handle data-fl ow computations. This approach simultaneously delivers the fl exibility and full control of CPUs/GPUs to implement complex computations, with the low power, high performance, low latency and deterministic nature of hardware acceleration. Various vendors are now off ering adaptive System-on- Chip (SoC) and System-on-Module (SOM) solutions – like the AMD-Xilinx Kria SOM and its robotics stack – that deliver the advantages of such a mixed computational model.
The adaptive SoCs and SOMs allow
roboticists to build machine behaviour by programming an architecture that creates the right data paths and control mechanisms. However, complex engineering skills are needed to program such architectures using established tools and techniques.
Building on ROS Suitable expertise in hardware and embedded design is scarce among roboticists, who are accustomed to building behaviour in the form of computational graphs that solve the robotic task at hand. They often use C++ to create complex real-time deterministic systems through advanced software engineering practices. A diff erent approach is now needed to help
for robot application development and even more so since the arrival of ROS 2 in 2020. This has become the default software development kit for robotic applications across industries. Previous initiatives to integrate adaptive computing into ROS have tackled the challenge from a hardware engineer’s perspective. They have assumed that the user has previous experiences with embedded and hardware fl ows and therefore is familiar with concepts like RTL, HDL and HLS and the design tools used to manipulate them. Similarly, deployment into embedded targets requires familiarity with Yocto, OpenEmbedded and related tools. Understanding that most roboticists do not come from this background, the ROS 2 Hardware Acceleration Working Group (HAWG) is taking a ROS-centric approach to integrate embedded fl ows directly into the ROS ecosystem. The proposed architecture aims to be not only platform agnostic and therefore suitable for edge, workstation, data centre and cloud targets, but also technology agnostic to allow targeting FPGAs, CPUs and GPUs, as well as being easily portable into various modules and boards. Ultimately, this work should enable the majority of roboticists to take advantage of the opportunities for hardware acceleration to realise future generations of advanced and sophisticated robots.
CONTACT:
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www.AMD.com
automationmagazine.co.uk
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