https://github.com/maskedsyntax/intellipath

A python based visualizer for A* pathfinding algorithm.
https://github.com/maskedsyntax/intellipath

algorithms animation astar-algorithm data-structures priority-queue pygame python3 queue

Last synced: about 2 months ago
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A python based visualizer for A* pathfinding algorithm.

• Host: GitHub
• URL: https://github.com/maskedsyntax/intellipath
• Owner: MaskedSyntax
• License: mit
• Created: 2021-03-24T13:25:03.000Z (almost 4 years ago)
• Default Branch: main
• Last Pushed: 2024-11-29T07:02:20.000Z (2 months ago)
• Last Synced: 2024-11-29T08:19:39.161Z (2 months ago)
• Topics: algorithms, animation, astar-algorithm, data-structures, priority-queue, pygame, python3, queue
• Language: Python
• Homepage:
• Size: 19.5 KB
• Stars: 2
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# IntelliPath: A* Pathfinder

This repository contains an implementation of the A* (A-Star) pathfinding algorithm in Python. The A* algorithm is a popular search algorithm used in various applications such as AI, robotics, and game development to find the shortest path between two points.

## Features

- **A\* Algorithm**: Efficiently finds the shortest path between the start and goal nodes.

- **Visualization**: Provides a visual representation of the grid, obstacles, start, and goal nodes, and the path found by the algorithm.

- **Interactive UI**: Allows users to interact with the grid, set start and goal nodes, and add obstacles.

## Getting Started

### Prerequisites

- Python 3.x

- Pygame

### Installation

Clone the repository:

   ```bash

   git clone https://github.com/Aftaab25/AStarPathfinder.git

   cd AStarPathfinder

   ```

### Running the App

Run the A* pathfinding script using the following command:

```bash

python3 main.py

```

This will start the application, and you can interact with the grid to visualize the A* pathfinding algorithm.

## Before using A* Algorithm

Select the start and end point by clicking any of the box in the grid.

    _(First click corresponds to the start point and the Second click corresponds to the end point)_

Then click anywhere in the grid to draw obstacles in the path that the algorithm has to avoid.

![Before Image](screenshots/before_algo.png)

Press `Space` to start the visualization of the algorithm and see the result _(the shortest path possible)_ drawn by the algorithm.

## Result Path by the A* Algorithm

![Result Image](screenshots/result_path.png)

Press `Enter` to clear the grid and draw new maps to play with.

## Code Overview

### `main.py`

- **Imports**: Necessary libraries including Pygame and heapq.

- **Node Class**: Represents a single node in the grid with attributes like position, cost, heuristic, and parent.

- **A* Algorithm**: Implements the A* search algorithm to find the shortest path.

- **Visualization**: Uses Pygame to visualize the grid, obstacles, start and goal nodes, and the path found by the algorithm.

- **Event Handling**: Handles user input for setting start and goal nodes, adding obstacles, and resetting the grid.

## License

This project is licensed under the MIT License. See the [LICENSE](LICENSE) file for details.