An interactive visualization tool for the A* pathfinding algorithm built with Python and Pygame.

• Interactive grid to set start, end, and barrier nodes.
• A* search visualization.
• Option to reset the grid.
• Option to fill the grid randomly with barriers.
• Clear and simple user interface with buttons.

Make sure you have Python 3.x installed. You'll also need to install pygame.

pip install pygame

git clone [email protected]:shahriarshm/astar-search-visualization.git
cd astar-search-visualization

Run the main Python script:

python main.py

• Left Mouse Button: Click on an empty cell to set the start node (red). Click again to set the end node (blue). Subsequent clicks will create barrier nodes (black).

• Right Mouse Button: Click on a node to reset it to an empty state.

• Reset Button: Resets the entire grid to its initial state.

• Random Fill Button: Fills the grid randomly with barriers.

• Search Button: Initiates the A* search from the start node to the end node. If a path is found, it's displayed in purple.

The A* algorithm finds the shortest path between a start and end node based on two main factors:

1. G cost: The distance from the start node to the current node.

2. H cost (Heuristic): The estimated distance from the current node to the end node (often using Manhattan distance as the heuristic).

The algorithm selects the node with the lowest sum of its G and H costs and explores its neighbors. This process is repeated until the end node is reached, ensuring the shortest path is found.

Pull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.

MIT