A high-fidelity pathfinding ecosystem designed for visualizing and testing intelligent navigation algorithms. PathGrid WorldSearch provides both an interactive Web-based visualizer and a powerful Python-based simulation engine.
- Interactive A Visualizer*: Draw obstacles, move nodes, and watch the A* algorithm optimize paths in real-time.
- Swarm Intelligence: Integrated support for Ant Colony Optimization (ACO) principles.
- Genetic Algorithms: Modular environment for genetic path optimization research.
- Customizable Grids: Support for hex-encoded obstacles and random environment generation.
- RL Ready: A modular state-action-reward framework designed for Reinforcement Learning agents.
The web version is a high-performance, vanilla JavaScript implementation featuring a modern "Cyber-Tech" aesthetic.
- Clone this repository.
- Open
index.htmlin any modern web browser. - Use the navigation bar to jump to the Visualizer.
- Draw walls by clicking/dragging, then hit Start A Search*.
The core engine of the project, built for speed and research flexibility.
- Python 3.8+
- NumPy
python RunAStar.py- Frontend: Vanilla HTML5, CSS3 (Glassmorphism), JavaScript (Asynchronous A* Engine).
- Backend / Core: Python 3, Tkinter (UI), NumPy (Computation).
- Design: Cyberpunk-inspired dark mode with custom AI-generated assets.
This project is licensed under the MIT License - see the LICENSE file for details.
Feel free to fork this project and submit pull requests. For major changes, please open an issue first to discuss what you would like to change.
Built for high-performance pathfinding research. 2026 PathGrid WorldSearch Project.