ahmed-mohiuddin-shah / maz-nav

MAZ-NAV

Welcome to the MAZ-NAV repository! This project aims to create an intelligent robot capable of autonomously detecting and solving mazes using computer vision technology powered by OpenCV.

Functionality

The project integrates OpenCV-based image processing to interpret maze layouts and navigate autonomously. Key components include:

• Python and Raylib App: Detects maze patterns and facilitates autonomous navigation.
• ESP32 Code: Controls the rover's movements based on visual data received from the app.
• 3D Models: Physical components for constructing the robot.
• Circuit Diagrams: Guides for assembling the hardware.

Project Structure

• 3D-MODELS: Contains 3D models for the robot components.
• CIRCUIT-DIAGRAM: Includes circuit diagrams for electronics.
• BOM: Includes the Bill Of Materials in an Excel sheet
• maz-nav-python: Python OpenCV app for maze detection and solving.
• MAZ-NAV-APP: Raylib CPP based OpenCV windows Aplication for maze detection, solving and communicating with Rover
• MAZ-NAV-ROVER: ESP32-based rover PlatformIO project.
• MAZ-NAV-WORKSPACE.code-workspace: Visual Studio Code workspace configuration.

Instructions for Users

Guide is a work in progress

Assembling the Robot

Guide is a work in progress

Contributing

To contribute to this project you must have Flutter and PlatformIO setup in VSCode

We welcome contributions to enhance the project! If you'd like to contribute:

1. Fork this repository and clone it to your local machine.
2. Open the MAZ-NAV-WORKSPACE.code-workspace file.
3. Make your modifications or additions.
4. Submit a pull request, detailing the changes and enhancements.