URDF-Importer
urdf-loaders
| URDF-Importer | urdf-loaders | |
|---|---|---|
| 1 | 1 | |
| 197 | 381 | |
| 2.0% | - | |
| 0.0 | 6.1 | |
| 7 months ago | 13 days ago | |
| C# | JavaScript | |
| Apache License 2.0 | Apache License 2.0 |
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URDF-Importer
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Help! Meet problems about connecting the ros with unity based on ros sharp
From my experience, ROS# is not the recommended way to connect Unity and ROS anymore. You probably want to use the ROS-TCP-Connector which is officially supported by Unity and its corresponding ROS package. That page has quite a few tutorials to help you get the connection setup. I currently maintain a plugin that implements a set of commonly used sensors here which might be of some use to you. Another useful resource maintained by Unity is their URDF importer, but it is still a bit buggy. Let me know if you need any other resources or help, I'm trying to make Unity a bit more livable for ROS developers.
urdf-loaders
What are some alternatives?
Unity-Robotics-Hub - Central repository for tools, tutorials, resources, and documentation for robotics simulation in Unity.
modelrobot - showing robots described by URDF in browser
Anki.Vector.SDK - The Anki Vector .NET SDK provides programmicatic access to the Vector Robot from .NET and is published as a NuGet package.
Odoo - Javascript and SVG odometer effect library with motion blur
webots_ros2 - Webots ROS 2 packages
closed-chain-ik-js - A generalized inverse kinematics solver that supports closed chains for parallel kinematics systems, dynamic reconfiguration, and arbitrary joint configuration based on damped least squares error minimization techniques
webots - Webots Robot Simulator
transitive - An open-source framework for full-stack robotics
ros-sharp - ROS# is a set of open source software libraries and tools in C# for communicating with ROS from .NET applications, in particular Unity3D
source-engine-model-loader - Three.js loader for parsing Valve's Source Engine models
ROS-TCP-Connector
Robotics-Object-Pose-Estimation - A complete end-to-end demonstration in which we collect training data in Unity and use that data to train a deep neural network to predict the pose of a cube. This model is then deployed in a simulated robotic pick-and-place task.