Checkpoint 3

Last Updated: Dec 15, 2025

Using the SLAM algorithm you implemented previously, you can now construct a map of an environment with the MBot. In this checkpoint, you will add path planning and autonomous exploration capabilities.

We also provide a guide on how to use slam_toolbox! If you’re not satisfied with your mapping performance and need a map to test your A* or exploration algorithms, feel free to take a look.

You may also use slam_toolbox for mapping in Competition Event 2 and Event 3 (with point deductions). For details, please check the competition page.

Contents

• Contents
• Task 3.1 Path Planning
  • TODO
• Task 3.2 Map Exploration
  • TODO
• Task 3.3 Localization with Estimated and Unknown Starting Position
• Checkpoint Submission

Task 3.1 Path Planning

Write an A* path planner. The A* skeleton is provided in the mbot_nav package.

TODO

1. Check the mbot_ros_labs upstream for any new commits to pull.
2. All work for this task is in the package mbot_nav. Start with navigation_node.cpp, search for TODOs. All the actual code writing is in astar.cpp.
   • You also need to complete obstacle_distance_grid.cpp. The TODOs match the earlier tasks, so you can reuse your previous implementations.
   • You don’t need to follow the TODOs strictly, feel free to implement them in your own preferred way.
3. When finished, compile your code:

    cd ~/mbot_ros_labs
    colcon build --packages-select mbot_nav
    source install/setup.bash
   

   • Important: You must source the workspace in every relevant terminal after each build. If you don’t, ROS will keep using the old code, and your changes will not take effect.

How to test?

• Testing Mode: In this mode, the navigation node listens to /initialpose and /goal_pose topics. Setting both in RViz triggers the A* planner, and the planned path will be displayed in RViz if successful. No real robot movement occurs, this is purely a software path calculation. Start with Testing Mode to verify that your A* algorithm works correctly.
  1. Run launch file to publish map and run nagivation node in VSCode Terminal:

     ros2 launch mbot_nav path_planning.launch.py map_name:=maze1
     

  2. Open Rviz to set initial pose and goal pose in NoMachine Terminal:

     cd ~/mbot_ros_labs/src/mbot_nav/rviz
     ros2 run rviz2 rviz2 -d path_planning.rviz
     

  Video Demo

• Real-world mode (with localization): After validating your planner in the previous tests, run in the real maze.
  1. Construct a map and save it in mbot_ros_labs/src/mbot_nav/maps. You may use your own mapping code or slam_toolbox.
  2. Then compile the mbot_nav package:

     cd ~/mbot_ros_labs
     colcon build --packages-select mbot_nav
     source install/setup.bash
     

  3. Launch the robot model, TF, LiDAR node in VSCode Terminal #1.

     ros2 launch mbot_bringup mbot_bringup.launch.py 
     

  4. Run launch file to publish map and run nagivation node in VSCode Terminal #2:

     ros2 launch mbot_nav path_planning.launch.py map_name:=your_map pose_source:=tf
     

  5. Run localization node in VSCode Terminal #3:

     ros2 run mbot_localization localization_node
     

  6. Start rviz and set initial pose in NoMachine Terminal #1, localization node needs it to initialize particles.

     cd ~/mbot_ros_labs/src/mbot_nav/rviz
     ros2 run rviz2 rviz2 -d path_planning.rviz
     

  7. Run motion controller in VSCode Terminal #4:

     ros2 run mbot_setpoint motion_controller_diff --ros-args -p use_localization:=true
     

  8. Then set the goal pose on rviz.

  Video Demo

  Some of the website content shown in the video may look different. Please follow the current version of the website, the video is only meant to demonstrate how to set the pose in RViz and what the expected results should look like.

Provide a figure showing the planned path in the map.

Task 3.2 Map Exploration

Until now, the MBot has only moved using teleop commands or manually set goal poses. For this task, you will implement a frontier-based exploration algorithm that allows the MBot to autonomously select targets and explore the full environment.

This task is useful for competition but not required for Checkpoint 3 submission.

TODO

1. All work is in mbot_nav. Start with exploration_node.cpp, search for TODOs. All the actual code writing is in frontier_explorer.cpp.
   • You don’t need to follow the TODOs strictly, feel free to implement them in your own preferred way.
2. When finished, compile your code:

    cd ~/mbot_ros_labs
    colcon build --packages-select mbot_nav
    source install/setup.bash
   

   • Important: You must source the workspace in every relevant terminal after each build. If you don’t, ROS will keep using the old code, and your changes will not take effect.

How to test?

1. Start rviz in NoMachine Terminal #1:

   cd ~/mbot_ros_labs/src/mbot_nav/rviz
   ros2 run rviz2 rviz2 -d path_planning.rviz
   

2. Launch the robot model, TF, LiDAR node in VSCode Terminal #1.

   ros2 launch mbot_bringup mbot_bringup.launch.py 
   

3. Run slam in VSCode Terminal #2:

   ros2 run mbot_slam slam_node
   

4. Run motion controller in VSCode Terminal #3:

   ros2 run mbot_setpoint motion_controller_diff --ros-args -p use_localization:=true
   

5. Run the exploration node in VSCode Terminal #4:

   ros2 run mbot_nav exploration_node
   

Explain the strategy used for finding frontiers and any other details about your implementation that you found important for making your algorithm work.

Task 3.3 Localization with Estimated and Unknown Starting Position

For advanced competition levels, the MBot must localize itself in a known map without knowing its initial pose. This will require initializing your particles in some distribution in open space on the map, and converging on a pose. This is useful in the competition but does not required any submission in the Checkpoint 3.

Details please check competition event 2 - level 3.

Explain the methods used for initial localization.

Checkpoint Submission

Demonstrate your path planner (task 3.1) by showing your robot navigating a maze.

• Submit a video of your robot autonomously navigating in a maze environment.
  • Your video should include the following:
    • Set goal pose, then the robot driving in the real-world lab maze.
    • Your visualization tool (RViz or Foxglove) displaying the map and planned path.