Overview
Description
The Robot Operating System (ROS) comprises various software libraries and tools for developing robot applications. ROS2 is a next-generation version of ROS and adapts to various platforms such as embedded MCUs. ROS2 target applications include service robot, robot arm, AGV (Auto Guided Vehicle), AMR (Autonomous Mobile Robot), and other applications.
The RZ/V2H ROS2 Package provides the ROS2 installation procedure on the RZ/V2H MPU. It can run ROS2 standard applications (e.g. cartographer) and a sample application (darknet_ros) with the AI accelerator DRP-AI on the RZ/V2H.
When running darknet_ros, you should install "Case1: ROS2 Core (Yocto Native) / Humble" from the ROS2 Support Package.
The operations for ROS2 (OSS part) are not supported by Renesas. Renesas supports only the operations written in the application note provided in each package.
Software List
The ROS2 package provides the following packages.
| RZ/V2H ROS2 Sample Application Package The RZ/V2H ROS2 Sample Application Package provides applications running on the RZ/V2H. | Software Package | Renesas |
| RZ/V2H ROS2 Support Package The RZ/V2H ROS2 Support Package provides the ROS2 installation procedure on the RZ/V2H. | Software Package | Renesas |
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Documentation
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| Application Note | ROS2 Installation Guide for the RZ/V2H
AI-generated Summary:
Instructions detail installing ROS2 on the RZ/V2H platform using two methods: ros2-galactic with Yocto and ros2-humble via Docker containers. It covers required hardware and software environments, including Linux host PC specifications, microSD card needs, and OS versions. The guide explains building environments, software configurations, and operation verification steps for both ROS2 versions. It also clarifies command environments for Linux host, RZ/V2H board, and Docker containers, ensuring seamless setup and communication between host and target systems.
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| Application Note | ROS2 Sample Application Guide (darknet_ros with DRP-AI) for the RZ/V2H
AI-generated Summary:
Run the ROS2 sample application using DRP-AI on the RZ/V2H platform to perform AI-based image recognition with YOLOX. The application processes ROS2 image topics, converting input images to YUYV format for model compatibility, and publishes detected object data and bounding boxes. It requires setting up a compatible Yocto environment, installing ROS2 and AI SDK, and copying trained model data to the target device. The guide details parameters, subscribed and published ROS2 topics, and environment preparation steps.
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Design & Development
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