Pathfinding Algorithm Simulation

Features

The simulation currently implements and visualizes the following pathfinding algorithms:

• Breadth-First Search (BFS): Explores the maze layer by layer, guaranteeing the shortest path in terms of the number of steps.
• Depth-First Search (DFS): Explores as far as possible along each branch before backtracking. It does not guarantee the shortest path.
• Greedy Best-First Search: Uses a heuristic (Manhattan distance to the goal) to prioritize exploration. It's often fast but doesn't guarantee the shortest path.
• A Search (A-Star): Combines the cost from the start (g_score) and a heuristic (Manhattan distance to the goal, h_score) to find the shortest path efficiently. The heuristic must be admissible (never overestimate the true cost).
• Dijkstra's Algorithm: Explores based on the cumulative cost from the start node. Guarantees the shortest path in terms of cost (number of steps in this unweighted grid).

How to Run

Prerequisites

• Python 3.x
• NumPy
• Matplotlib
  • Ensure you have a suitable backend for Matplotlib that supports interactive plots (e.g., Tkinter, which is often included with Python, or others like Qt5Agg).

These dependencies can typically be installed using pip:

pip install numpy matplotlib

Running the Simulation

1. Clone this repository or download the 'pathfinding-algo-simulation.py' script.

2. Navigate to the directory containing the script in your terminal.

3. Execute the script using Python:

   python pathfinding-algo-simulation.py

This will open a window displaying the maze and a set of radio buttons to select the algorithm for simulation.

Future Improvements

UX Enhancements

1. Maze Generation

• Allow generating new random mazes.
• Allow a user to define their own mazes (maybe by clicking or loading from a simple text format).

2. Speed Control

• Add a slider or input to control the animation speed ('interval' in 'FuncAnimation').

3. Reset Button Add a dedicated "Reset Algorithm" button that clears the current algorithm's state (visited, frontier, path) and restarts it without needing to re-select it from the radio buttons. This would be more intuitive than the current implicit reset.
4. Execution Allow a user to step through the algorithm one step at a time.