ur5_ROS-Gazebo VS onrobot

Compare ur5_ROS-Gazebo vs onrobot and see what are their differences.

ur5_ROS-Gazebo

Universal Robot (UR5) Pick and Place Simulation in ROS-Gazebo with a USB Cam and Vacuum Grippers (by lihuang3)

onrobot

OnRobot packages (https://wiki.ros.org/onrobot) (by Osaka-University-Harada-Laboratory)
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ur5_ROS-Gazebo onrobot
1 1
387 28
- -
0.0 4.2
over 2 years ago 7 months ago
Python Python
- MIT License
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
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ur5_ROS-Gazebo

Posts with mentions or reviews of ur5_ROS-Gazebo. We have used some of these posts to build our list of alternatives and similar projects.

onrobot

Posts with mentions or reviews of onrobot. We have used some of these posts to build our list of alternatives and similar projects.
  • ROS2 driver for OnRobot grippers.
    1 project | /r/ROS | 18 May 2023
    Do you know any ROS2 driver for OnRobot grippers? I know this repository exists: https://github.com/Osaka-University-Harada-Laboratory/onrobot But I couldn't find any for ROS2. What do you think should I migrate it to ROS2 or try to use ROS/ROS2 bridge to use it. Thank you in advance!

What are some alternatives?

When comparing ur5_ROS-Gazebo and onrobot you can also consider the following projects:

ur3 - ROS-based UR3/UR3e controller with simulation in Gazebo. Adaptable to other UR robots

rpaframework - Collection of open-source libraries and tools for Robotic Process Automation (RPA), designed to be used with both Robot Framework and Python

linorobot2 - Autonomous mobile robots (2WD, 4WD, Mecanum Drive)

turtlebot3 - ROS packages for Turtlebot3

PythonRobotics - Python sample codes for robotics algorithms.

ros2_documentation - ROS 2 docs repository

Automagica - AI-powered Smart Robotic Process Automation 🤖

rosbot-docker - Docker images for ROSbot 2 platform

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.