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
