Chess Coach Project

This repository contains the implementation of a Chess Coach system designed to enhance the chess-playing experience using AI and robotics. The system provides support for both educational and normal game modes and integrates computer vision, chess engines, and robotics.

Features

1. Chessboard Detection

• Utilizes a YOLOv8 model to detect the chessboard layout and its grid.

2. Chess Piece Recognition

• A YOLOv11 model identifies the positions and types of chess pieces within each box on the board.

3. FEN Generation

• Converts the detected game state into Forsyth–Edwards Notation (FEN), representing the current chessboard status.

4. Stockfish Integration

• Uses the Stockfish chess engine to suggest the best moves:
  • ELO Rating: Adjusts suggestions based on the configured skill level.
  • Game Modes:
    • Normal: The robotic arm plays the move suggested by Stockfish.
    • Educational: A button allows the child to see and optionally play the suggested best move.

5. Game Logging

• Sends the complete game details to a backend server after the game ends for analysis and storage.

Installation

1. Clone the Repository:

   git clone https://github.com/RoboKnight-INSAT/ChessBotVision.git
   cd ChessBotVision

2. Install Dependencies:

   • Ensure Python 3.8+ is installed.
   • Install the required Python libraries:

     pip install -r requirements.txt

3. Download YOLO Models:

   • The models are expoted in "best_chesspiece_model.pt" and "best_corner_detection_model.pt"

4. Configure Stockfish:

   • Download the Stockfish engine and place it in the engines/ directory.
   • Update the configuration file to set the path to Stockfish.

5. Connect Hardware:

   • Ensure the robotic arm and any additional hardware are connected and configured.

Usage

1. Start the Chess Coach:

   python main.py

2. Select Game Mode:

   • Choose between Educational or Normal mode in the LCD Interface.

3. Play the Game:

   • Follow the prompts for move suggestions, or let the robotic arm play in normal mode.

4. End of Game:

   • Game data will be automatically sent to the backend server.

Project Structure

Directory structure:
└── roboknights-insat-chessbotvision/
    ├── README.md
    ├── requirements.txt
    ├── images/
    ├── models/
    │   ├── best_chesspiece_model.pt
    │   └── best_corner_detection_model.pt
    ├── notebooks/
    │   ├── app.ipynb
    │   └── chess_move_detection.ipynb
    ├── outputs/
    ├── src/
    │   ├── app.py
    │   ├── lcd.py
    │   ├── main.py
    │   └── stockfish_test.py
    └── tools/
        ├── ChessTools.py
        └── computer_vision_tools.py

License

This project is licensed under the MIT License.