Programming SuperTuxKart with ROS2

Contents

• Introduction
  • Course Overview
  • Prerequisites
  • Course Content
• Installation Instructions
  • Step 1: Install Ubuntu 22 for Windows (WSL)
  • Step 2: Update Ubuntu Packages
  • Step 3: Install Ansible
  • Step 4: Install Required Dependencies
• Session 1: Introduction to ROS2
  • Learning Goals
  • Opening Your Workspace
  • Workspace Setup
  • Building Your Package
  • ROS2 Node Communication
  • Chatter Example
  • Explanation
• Session 2: Turtlesim with ROS2
  • Learning Goals
  • Commands at a Glance
  • Let’s Get Turtlesim Moving!
  • Task: Turtlesim Adventure
  • Explanation
  • Task: Turtlesim Challenge - I
  • Task: Turtlesim Challenge - II
  • What’s Next?
• Session 3: Starting SuperTuxKart and controlling the car with ROS2
  • What We’ll Do
  • Downloading SuperTuxKart
  • Starting the Game with ROS2
  • Understanding the Output
• Session 4: Playing with Images in ROS2 Using OpenCV
  • Why Are We Doing This?
  • What We’ll Do
  • OpenCV Overview
  • Let’s Get Started
  • What’s Next?
• Session 5: Seeing SuperTuxKart Track with ROS2 and OpenCV
  • What We’ll Do
  • Making the Code See the Road
  • Code Explanation
  • HSV Value Detector with ROS2 and OpenCV
• Session 6: Self-Driving SuperTuxKart with ROS2 and OpenCV
  • What We’ll Do
  • Making the Car Drive Itself
  • Code Explanation
  • Lane Follower with ROS2 and OpenCV
  • Running the Final Autonomous Race
• Session 7: Introduction to Imitation Learning for Self-Driving SuperTuxKart
  • Why Are We Doing This?
  • What We’ll Do
  • Imitation Learning Explained
  • How Does the Car Learn?
  • Neural Networks and Imitation Learning
  • Real-Life Example of Imitation Learning
  • What is Reinforcement Learning?
  • Why Focus on Imitation Learning?
  • Imitation Learning vs OpenCV Approach
  • Real-Life Example of OpenCV in Self-Driving Cars
  • Conclusion
• Session 8: Capturing, Analyzing, and Balancing Training Data for Imitation Learning
  • Why Is This Important?
  • Libraries Used
  • Step 1: Save Images and Steering Angles
  • Explanation:
  • Step 2: Analyze the Dataset
  • Explanation:
  • Step 3: Balance the Dataset
  • Explanation:
  • Step 4: Visualize the Balanced Dataset
  • Summary
• Session 9: Preparing and Augmenting Training Images
  • Step 1: Why Preprocess Images?
  • Step 2: Augmenting Images
  • Step 3: Splitting the Dataset
  • Step 4: Creating a Batch Generator
• Session 10: Teaching the Self-Driving Car to Steer
  • What Will You Learn Today?
  • Step 1: Create the Brain for the Car (The Model)
  • Step 2: Teach the Brain to Steer (Training the Model)
  • Step 3: How We Teach the Brain to Drive
  • Step 4: Watch the Brain Learn (TensorBoard)
  • ncnn model
  • Summary
• Session 11: Getting the Car to Drive by Itself! 🚗💨
  • What’s Happening in the Code? 🧑‍💻
  • 1. Getting the Picture from the Camera 📸
  • 2. Making the Brain Work
  • 3. Driving the Car! 🚗
  • How Does the Car Learn to Steer? 🧠
  • Putting Everything Together
  • What Did We Do in This Code? 🤔
  • Summary 🏁