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

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lukovicaleksa/autonomous-driving-turtlebot-with-reinforcement-learning

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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

radno okruzenje 3

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