turtlebot3 VS ur5_ROS-Gazebo

Compare turtlebot3 vs ur5_ROS-Gazebo and see what are their differences.

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turtlebot3 ur5_ROS-Gazebo
8 1
1,399 387
2.5% -
0.0 0.0
2 months 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.

turtlebot3

Posts with mentions or reviews of turtlebot3. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2022-04-11.

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 turtlebot3 and ur5_ROS-Gazebo you can also consider the following projects:

navigation2 - ROS 2 Navigation Framework and System

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

turtlebot3_simulations - Simulations for TurtleBot3

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

turtleplotbot3 - The TurtlePlotBot3 is a MicroPython based 3D printed drawing robot powered by a TTGO T-Display ESP32 Module

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

mycobot_ros - A ros package for myCobot.

ros2_documentation - ROS 2 docs repository

rosbot-docker - Docker images for ROSbot 2 platform

tortoisebot - TortoiseBot is an extremely learner-friendly and cost-efficient ROS-based Open-sourced Mobile Robot that is capable of doing Teleoperation, Manual as well as Autonomous Mapping, Navigation, Simulation, etc.

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.