AI Pathfinding Visualizer

An interactive Python + Pygame application that visualizes how classic AI search algorithms explore a grid to find a path between a start and a goal node. Built as part of an Artificial Intelligence course project to demonstrate and compare uninformed and informed search strategies.

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Overview

This project lets you draw a custom grid with walls, place a start and goal point, and then watch step by step how different AI search algorithms explore the grid to find a path. It's designed to make abstract search-algorithm concepts (BFS, DFS, A*, Greedy Best-First Search) visually intuitive, and to compare their efficiency and path quality side by side.

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Demo

Features

• Interactive grid — Left-click to place the Start node, Goal node, and Walls
• Right-click to reset any cell
• Multiple search algorithms implemented and run on the same grid for direct comparison
• Real-time animated visualization of node exploration and final path reconstruction
• On-screen control legend so all keybindings are visible at a glance
• Instant grid reset to quickly test different mazes

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Algorithms Implemented

Algorithm	Key	Type	Shortest Path Guaranteed?
Breadth-First Search (BFS)	B	Uninformed	Yes (unweighted grid)
Depth-First Search (DFS)	D	Uninformed	No
A* Search	A	Informed (heuristic+path cost)	Yes
Greedy Best-First Search	G	Informed (heuristic only)	No

How each one works

• BFS explores the grid layer by layer using a queue (FIFO), guaranteeing the shortest path on an unweighted grid.

• DFS explores as deep as possible along one path using a stack (LIFO) before backtracking it finds a path, but not necessarily the shortest one.

• A Search* uses both the actual cost from the start node (g) and a heuristic estimate to the goal (h). It selects nodes using f = g + h and, with an admissible heuristic such as Manhattan distance on this grid, guarantees the shortest path.

• Greedy Best-First Search uses only the heuristic (no accumulated cost) to decide which node to explore next — it's often faster than A*, but does not guarantee the shortest path.

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Controls

Action	Input
Set Start → Goal → Walls	Left Click
Reset a cell	Right Click
Run BFS	B
Run DFS	D
Run A*	A
Run Greedy Best-First Search	G
Clear the entire grid	C

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Project Structure

AI-Pathfinding-Visualizer/
│
├──assets/
│ └──pathfinding.demo.png
├── main.py             
├── bfs_algorithm.py         
├── dfs_algorithm.py             
├── a_star_algorithm.py     
├── greedy_algorithm.py       
├── .gitignore
└── README.md

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Getting Started

Installation

# Clone the repository
git clone https://github.com/mdhimel126/AI-Pathfinding-Visualizer.git
cd AI-Pathfinding-Visualizer

# create a virtual environment

python -m venv venv

#On windos
venv\Scripts\activate       
#On linux
source venv/bin/activate    

# Install dependencies
pip install pygame

Run

python main.py

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How to Use

1. Run python main.py — a grid window will open.
2. Left-click a cell to set the Start node (green).
3. Left-click another cell to set the Goal node (red).
4. Left-click additional cells to draw Walls (black).
5. Press a key (B, D, A, or G) to run that algorithm and watch it explore the grid in real time.
6. Press C to clear the grid and try a new maze.

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Author

Md. Himel GitHub: @mdhimel126