New-Tech Europe Magazine | Q2 2022

Realizing Adaptive Computing in Robotics Robots are networks of networks that exchange data on a continuous basis throughout the entire machine, from sensors, to compute engines, and back to the actuators at their extremities. We can visualize these networks as the nervous system of the robot, which facilitates exchanging information. As in the human nervous system, these exchanges are critically dependent upon deterministic performance and real-time responsiveness if the robot is to behave coherently. This is difficult to guarantee using scalar and vector processors, with their fixed architectures. The customized, highly paral lel architectures implemented in FPGAs and ASICs offer the opportunity to overcome these limitations. The FPGA, in particular, by enabling software- defined hardware for robots, introduces a fundamental shift in the approach to software development in robotics. Instead of programming functionality in the CPU, working within the limitations imposed by the CPU’s pre-defined architecture and constraints, building a robotic behaviour with FPGAs is about programming an architecture that performs the desired task. Roboticists need suitable tools and hardware to properly leverage the flexibility of FPGAs when building adaptab l e robot s that exh i b i t deterministic, real-time behaviour. A System-on-Module (SOM) l ike the Xilinx Kria K26 is one example, designed for edge applications and with high-speed interfaces, memory, and power on-board. It contains a Zynq® UltraScale+™ MPSoC system-on-chip (SoC) that provides programmable logic cells and DSP slices while handling scalar and vector processing workloads with a quad-core application processor complex, dual-core real-time processor, and a 2D/3D GPU. In addition to the SOM, appropriate libraries and utilities are needed to

Image: AMD Robotics credit: Xilinx

build industrial-grade robotics solutions. The Kria Robotics Stack (KRS) (figure 2) is tightly integrated with the Robot Operating System (ROS), which is the de facto framework for robot application development, and simplifies the use of hardware acceleration. The SOM provides native support for ROS 2, which boosts performance in robotics and industrial automation applications. This stack uses the ROS 2 Software Development Kit (SDK) and works with the ROS 2 ecosystem to help build robot systems with deterministic, real-time performance using a modular approach. It leverages known techniques like Quality of Service (QoS) mechanisms and Time Sensitive Networking (TSN) and includes application-level acceleration kernels, ROS communication middleware, and a runtime tool that facilitates interactions with the FPGA. A hypervisor helps support mixed criticality using virtual machines. Conclusion Adaptive, accelerated computing leveraging FPGAs can enhance the performance of industrial robots while also improving energy efficiency and permitting future-proof flexibility and security. Realising these next generations of machines requires suitable hardware,

such as SOMs that combine FPGA logic with scalar processors and GPUs, as well as software and tools that can be easily used with a framework familiar to roboticists, such as ROS 2. For more info, please visit: h t t p s : / /www . x i l i n x . c om/ products/som/kria.html

Víctor Mayoral-Vilches

New-Tech Magazine Europe l 29