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Instructions To Set Up & Run The Code

SETUP

Install Packages

After successfully installing and running Ubuntu:

  1. Install ROS Noetic following the instructions at this link.
  2. Install all Turtlebot2 files necassary.
  3. Open New Terminal
  4. Install RealSense packages:

$ sudo apt-get install ros-noetic-realsense2-camera

  1. Install Tf2 sensor message packages:

$ sudo apt-get install ros-noetic-tf2-sensor-msgs

Create Workspace

  1. Open New Terminal
  2. Setup working directory:

$ mkdir Directory_Name
$ cd Directory_Name
$ mkdir src
$ cd src

  1. Clone this repository:

$ git clone https://github.com/UoA-CARES/mobile_robot_learning_testbed.git

  1. Clone other required repositories:

$ git clone https://github.com/UoA-CARES/cares_msgs
$ git clone https://github.com/maraatech/aruco_detector

  1. Build and compile workspace:

$ cd ..
$ catkin_make

  1. Define source (so you don't have to do it everytime you open a terminal):

$ echo "~/Directory_Name/devel/setup.bash" >> ~/.bashrc

Add Models/World/Launch Files

  1. Copy the mobile_robot.world file from the world folder into the folder Home/turtlebot2/src/turtlebot_simulator/turtlebot_gazebo/worlds
  2. Copy the mobile_robot.launch file from the launch folder into the folder Home/turtlebot2/src/turtlebot_simulator/turtlebot_gazebo/launch (keep real_robot.launch file as it is)
  3. Open the Home folder in file explorer and show hidden files (CTRL+H)
  4. Copy all folders in the aruco_marker_models into the folder Home/.gazebo/models

Training/Simulation Testing

  1. Open New Terminal
  2. Launch the mobile robot world:

$ roslaunch turtlebot_gazebo mobile_robot.launch

  1. Run the main.py script in the src folder either through VS Code or a new terminal.

Real world testing

  1. Create Aruco marker models printed from this aruco marker genertor.
  2. Plce them with odd numbers on the left side and even numbers on the right side precisly space to match simualtion, where 1 simulation unit is 0.85m in the real world
  3. Connect Turtlebot2 platform and RealSense camera to a laptop that can be placed on the robotic platform, and place robot on track
  4. Open a new Terminal
  5. Launch camera and turtlebot, you should see a window pop up showing the camera view, and outlining Aruco markers it sees:

$ roslaunch mobile_robot_learning_testbed real_robot.launch

  1. To run the TD3 or DQN testing open the relevant algorithm_real.py file in VS Code
  2. Change the path of the loaded model, to match the model you wish to load
  3. Run the TD3_real.py or DQN_real.py file directly