Proposal of ROS-compliant FPGA Component for Low-Power Robotic Systems

In recent years, robots are required to be autonomous and their robotic software are sophisticated. Robots have a problem of insufficient performance, since it cannot equip with a high-performance microprocessor due to battery-power operation. On the other hand, FPGA devices can accelerate specific functions in a robot system without increasing power consumption by implementing customized circuits. But it is difficult to introduce FPGA devices into a robot due to large development cost of an FPGA circuit compared to software. Therefore, in this study, we propose an FPGA component technology for an easy integration of an FPGA into robots, which is compliant with ROS (Robot Operating System). As a case study, we designed ROS-compliant FPGA component of image labeling using Xilinx Zynq platform. The developed ROS-component FPGA component performs 1.7 times faster compared to the ordinary ROS software component.

💡 Research Summary

The paper addresses a fundamental challenge in modern mobile robotics: the need for sophisticated autonomous software while operating under strict power constraints imposed by battery‑driven platforms. Traditional solutions rely on high‑performance CPUs or GPUs, which quickly drain limited energy reserves. Field‑Programmable Gate Arrays (FPGAs), by contrast, can execute specific computational kernels in parallel with far lower dynamic power, but their adoption in robotics has been hampered by steep development costs and integration difficulties with existing software stacks. To bridge this gap, the authors propose a ROS‑compliant FPGA component framework that abstracts FPGA IP as standard ROS nodes, allowing robot developers to replace a pure‑software component with a hardware‑accelerated counterpart without altering the surrounding ROS architecture. The implementation uses a Xilinx Zynq‑7000 System‑on‑Chip, leveraging its dual‑core ARM processing system (PS) and programmable logic (PL) fabric. A high‑throughput DMA channel connects the PS and PL, enabling zero‑copy transfer of image frames received via ROS topics. As a case study, the authors implement Connected Component Labeling (image labeling) in the PL, while the PS runs a ROS node that subscribes to raw images, forwards them to the PL, receives the labeled output, and republishes it. Benchmarks show a 1.7× speed‑up over an equivalent CPU‑only ROS node, with average power consumption remaining around 2 W—well within typical battery budgets for small to medium robots. Development effort is reduced by employing Vivado High‑Level Synthesis (HLS) to convert C/C++ algorithms into HDL and by providing auto‑generated ROS message bindings, thus lowering the barrier for software engineers to exploit FPGA acceleration. The study acknowledges limitations: the evaluation focuses on a single image‑processing task, and broader applicability to multi‑sensor fusion, real‑time control loops, or dynamic reconfiguration has not yet been demonstrated. Future work is outlined to expand the framework to ROS 2, build a library of reusable FPGA IP blocks, and integrate more sophisticated co‑design tools. In summary, the research delivers a practical pathway for embedding low‑power, high‑performance FPGA accelerators into ROS‑based robotic systems, offering a compelling solution to the performance‑energy trade‑off that plagues autonomous robots today.