Control of Mobile Robots - Python Educational Repository

Open-source Python implementation of GeorgiaTech's "Control of Mobile Robots" course

A free, educational alternative to MATLAB for learning mobile robot control theory and implementation.

🎯 Project Goal

Provide students with:

• Interactive Jupyter notebooks covering control theory fundamentals
• Reusable Python modules for robot models and controllers
• Visual simulator to see algorithms in action
• Zero cost - completely free and open-source

📚 Course Modules

Module 1: PID Control

• Cruise controller examples
• PID tuning techniques
• Discrete-time implementation

Module 2: Robot Models

• Differential drive kinematics
• Unicycle model
• Odometry and wheel encoders
• Sensor models

Module 3: State-Space Control

• Linearization techniques
• Stability analysis (eigenvalues)
• Consensus algorithms for swarm robotics

Module 4: Control Design

• Pole placement
• Controllability & observability
• Luenberger observers

🚀 Getting Started

Quick Setup (Recommended: Use Virtual Environment)

Windows (Easy Mode):

1. Double-click setup_venv.bat
2. Wait for installation to complete
3. Done! 🎉

Manual Setup (All Platforms):

# Navigate to project
cd control_mobile_robots

# Create virtual environment
python -m venv venv

# Activate it
.\venv\Scripts\activate          # Windows
source venv/bin/activate         # Mac/Linux

# Install dependencies
pip install -r requirements.txt

See detailed instructions: SETUP_INSTRUCTIONS.md

For VSCode Users

1. Install the Jupyter extension in VSCode
2. Open any .ipynb file
3. Click "Select Kernel" → Choose your Python environment
4. Run cells with Shift+Enter

🎮 Interactive Simulator

NEW! 2D mobile robot simulator with go-to-goal controller:

Quick Launch (Windows):

• Double-click run_simulator.bat

Manual Launch:

cd simulator
python main.py

Features:

• Click to set goal positions
• Watch PID controller in action
• See odometry drift over time
• Add obstacles with right-click
• Real-time visualization

See simulator/README.md for full details.

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📖 Learning Path

Option 1: Start with Notebooks

1. notebooks/module_2_robot_models/01_differential_drive.ipynb
2. notebooks/module_2_robot_models/02_unicycle_model.ipynb
3. Then explore Module 1 (PID Control)

Option 2: Start with Simulator

1. Run run_simulator.bat and play with it
2. Then dive into notebooks to understand the theory
3. Come back to simulator to see concepts in action

🛠️ Tech Stack

• NumPy/SciPy - Numerical computing
• python-control - Control theory functions
• Matplotlib - Plotting and visualization
• Jupyter - Interactive notebooks
• Pygame - 2D robot simulator ✅

📝 Credits

Based on the Coursera course "Control of Mobile Robots" by Dr. Magnus Egerstedt (Georgia Tech).

This is an independent educational project - not affiliated with Georgia Tech or Coursera.

🤝 Contributing

Contributions welcome! This is a learning project.

📄 License

MIT License - Free for educational use