linorobot2 VS ur5_ROS-Gazebo

Compare linorobot2 vs ur5_ROS-Gazebo 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)
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linorobot2 ur5_ROS-Gazebo
3 1
402 387
7.3% -
8.8 0.0
13 days ago over 2 years ago
Python Python
Apache License 2.0 -
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.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.

linorobot2

Posts with mentions or reviews of linorobot2. We have used some of these posts to build our list of alternatives and similar projects.

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.

What are some alternatives?

When comparing linorobot2 and ur5_ROS-Gazebo you can also consider the following projects:

linorobot - Autonomous ground robots (2WD, 4WD, Ackermann Steering, Mecanum Drive)

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

r_e_c_u_r - an open diy py/pi based video sampler

turtlebot3 - ROS packages for Turtlebot3

roschart - ROS Chart Specification

onrobot - OnRobot packages (https://wiki.ros.org/onrobot)

OpenWeedLocator - An open-source, low-cost, image-based weed detection device for in-crop and fallow scenarios.

ros2_documentation - ROS 2 docs repository

project-spotmicro-ai - Controlling the MicriSpotAI robot from scratch

rosbot-docker - Docker images for ROSbot 2 platform

yertle - A 3D Printed Quadrupedal Robot for Locomotion Research. :turtle:

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.