ROS ノードの生成と展開
ROS Toolbox では、ROS ノード用の C++ および CUDA® コードを生成し、ローカルまたはリモート デバイス ターゲットに展開できます。ROS インターフェイスが含まれる MATLAB® コードの場合、MATLAB Coder™ (Generate a Standalone ROS Nodeを参照) を使用して C++ コードを生成し ROS ノードとして展開できます。Simulink® モデルの場合、Simulink Coder (Generate Standalone ROS Node from Simulinkを参照) を使用して C++ コードを生成するか、GPU Coder™ を使用して最適化された CUDA コードを生成し、ROS ノードとして展開できます。
メモ
ROS ノードを展開するには、SSH 接続を介してターゲット ROS デバイスにファイルを転送する必要があります。展開する前に OpenSSH がターゲット ROS デバイスにインストールされていることを確認してください。
ブロック
トピック
MATLAB からの ROS ノードの生成および展開
- MATLAB Programming for Code Generation
Recommended workflow for generating a standalone executable from MATLAB code that contains ROS interfaces. - Generate a Standalone ROS Node
Generate C++ code for a standalone ROS node from a MATLAB function. - Configure MATLAB Coder for ROS Node Generation
Configure MATLAB Coder configuration object for customizing ROS Node generation. - Generate CUDA ROS and CUDA ROS 2 Nodes Using MATLAB Coder and GPU Coder
Generate and build CUDA ROS and CUDA ROS 2 nodes from a MATLAB function.
Simulink からの ROS ノードの生成および展開
- Generate Standalone ROS Node from Simulink
Generate and build a standalone ROS node from a Simulink model. - Generate CUDA ROS Node from Simulink
Configure Simulink Coder to generate and build a CUDA ROS node from a Simulink model. - Generate ROS or ROS 2 Packages for Model Reference Hierarchy in Simulink
Model references in Simulink enable modular development and code reuse in large components. In robotics and autonomous systems applications, using a model reference hierarchy facilitates seamless integration during parallel development efforts for perception, motion-planning, and control algorithms. During code generation and deployment of Simulink model reference hierarchies as ROS or ROS 2 packages, you can package the code in one of these ways: - Generate a ROS Control Plugin from Simulink
Generate and build aros_controlplugin from a Simulink model. - Enable External Mode for ROS Toolbox Models
External mode enables Simulink on your host computer to communicate with a deployed model on your robotics hardware during runtime. External mode allows you to tune block mask parameters and to visualize signals on your model while your model is running. For ROS Toolbox, deployed models are ROS nodes running on the target hardware that communicates with Simulink over TCP/IP. - Generate Code to Manually Deploy a ROS Node from Simulink
This example shows you how to generate C++ code from a Simulink model to deploy as a standalone ROS node. The code is generated on your computer and must be manually transferred to the target ROS device. No connection to the hardware is necessary for generated the code. For an automated deployment of a ROS node, see Generate Standalone ROS Node from Simulink.
注目の例
Automated Parking Valet with ROS in MATLAB
Distribute the Automated Parking Valet application among various nodes in a ROS network using MATLAB.
Automated Parking Valet with ROS in Simulink
Distribute an automated parking valet application among various nodes in a ROS network in Simulink.
Generate Automated ros_control Plugin for 3-D Shape Tracing Manipulator
Generate and deploy a 3-D shape tracing contoller as a ros_control plugin from Simulink. Perform 3-D shape tracing using UR manipulator and depth point cloud.
Generate ROS Node for UAV Waypoint Follower
Use MATLAB code generation to create a ROS node to move a UAV along a predefined circular path and a set of specified custom waypoints.
Deploy ROS Node for Sign Following Robot with Time Synchronization Using Simulink
Use Simulink® to generate a ROS stepping enabled node and control a simulated robot running on a separate ROS-based simulator.
Build a Map Using Lidar SLAM with ROS in MATLAB
Use 2-D lidar data from a simulated robot in Gazebo to build a map.
Build and Deploy Visual SLAM Algorithm with ROS in MATLAB
In this example, you implement a visual simultaneous localization and mapping (SLAM) algorithm to estimate the camera poses for the TUM RGB-D Benchmark [1] dataset. You then generate C++ code for the visual SLAM algorithm and deploy it as a ROS node to a remote device using MATLAB®.
Lidar Object Detection in ROS Using PointPillars Deep Learning
Lidar Object Detection In ROS Using PointPillars Deep Learning
