autonomous-driving-turtlebot-with-reinforcement-learning

Implementation of Q-learning algorithm and Feedback control for the mobile robot (turtlebot3_burger) in ROS.

• Algorithm is implemented from scratch.
• To run the code in gazebo simulator, export the model (burger) and roslaunch turtlebot3_world.launch file.
• To run the code live on a physical TurtleBot, the ROS_MASTER_URI and ROS_HOSTNAME need to be set via the terminal by editing the ~/.bashrc script.
• After setting up the environment, rosrun the desired nodes!
• Link to video: https://www.youtube.com/watch?v=zw1BCfku1Dc&t=3s

Content

• Thesis -> master thesis and learning phase flow chart
• Log_feedback_1, 2, 3 -> folders containing data and parameters from Feedback Control algorithm testing
• Log_learning ... -> folder containing data and parameters from the learning phase, as well as the Q-table
• rqt_graphs -> folder containing rqt graphs in ROS
• scripts -> python scripts
  • Control.py -> functions for robot control, Odometry message processing and setting robot's initial position
  • Lidar.py -> functions for Lidar message processing and discretization
  • Qlearning.py -> functions for Q-learning algorithm
  • Plots.py -> plotting the data from learning phase and Q-table
  • scan_node.py -> initializing the node for displaying the Lidar measurements and the current state of the agent
  • learning_node.py -> initializing the node for learning session
  • feedback_control_node.py -> initializing the node for applying Feedback Control algorithm
  • control_node.py -> initializing the node for applying the Q-learning algorithm combined with Feedback control
  • learning phase flow chart -> flow chart of the learning phase of the algorithm