# autonomous-driving-turtlebot-with-reinforcement-learning

**Repository Path**: auto123/autonomous-driving-turtlebot-with-reinforcement-learning

## Basic Information

- **Project Name**: autonomous-driving-turtlebot-with-reinforcement-learning
- **Description**: No description available
- **Primary Language**: Unknown
- **License**: Not specified
- **Default Branch**: main
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2022-03-06
- **Last Updated**: 2022-03-06

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# 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](https://user-images.githubusercontent.com/72970001/98345474-02e69500-2015-11eb-8552-9949ddae6cab.jpeg)

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