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an open-source differential drive ROS2-based educational robot (similar to the popular Turtlebot, Andino bot, limo robot, etc.) for learning advanced mobile robotics and navigation concepts in ROS2.

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samuko-things/mobo_bot

 
 

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mobo-bot

mobo_bot_pic This is an an open-source differential drive ROS2-based educational robot (similar to the popular Turtlebot, Andino bot, limo robot, etc.), created by Samuel Obiagba (aka samuko-things) founder of robocre8, to learn advanced mobile robotics concepts such as control, sensor-fusion, localization, navigation, perception, Artificial Intelligence, etc. using the ROS2 framework as well as Arduino also.

This will ensure the longevity and future of open-source robotics in Nigeria and Africa, as well as the world, by aiding the learning and building of advanced mobile robots, leveraging the use of the ROS2 and Arduino Frameworks.

  • It uses the EPMC module and EIMU Module for its base control.
  • It has a gazebo ign simulation environment where algorithms can be tested before being implemented on the robot.
  • It is both easy to work with and build.
  • Learning robotics has never been more easy.

GETTING STARTED WITH SIMULATION (mobo_bot_sim)

mobo_bot_sim is the mobo_bot ROS2 simulation with Gazebo Ignition.

mobo_bot_sim

WORKING WITH THE ACTUAL ROBOT (mobo_bot_base and mobo_bot_rviz)

mobo_bot_base is the base control package for the actual physical mobo_bot robot. it launches all the controls, sensor-fusion, lidar and camera, to get started with working with the robot.

the mobo_bot_base package is meant to run on the Raspberry Pi 4 on the robot.

mobo_bot_rviz is meant to run on your Dev PC which will be connected to the robot via ssh. For visualiging the robot state, sensor data, navigation, etc.

mobo_bot_base

  • to get started with the actual physical mobo_bot robot (which uses the Raspberry Pi 4), follow this mobo_bot_base tutorial

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an open-source differential drive ROS2-based educational robot (similar to the popular Turtlebot, Andino bot, limo robot, etc.) for learning advanced mobile robotics and navigation concepts in ROS2.

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  • Python 93.9%
  • CMake 6.1%