Pathfinding Algorithm using A* in a 2D Matrix

This Python program demonstrates the A* algorithm for finding an optimal path in a randomly generated 2D matrix. The code is organized into three main files: distances.py, a_star.py, and main.py.

Files:

1. distances.py:

   • Contains a Functions class with methods for pathfinding-related functions.
   • Includes Manhattan and Euclidean distance calculations, along with a function to display a visual representation of a path on the matrix.

2. a_star.py:

   • Implements the A* algorithm within the AStar class for pathfinding on a 2D matrix.
   • Utilizes the Functions class from distances.py for distance calculations and path visualization.

3. main.py:

   • The main program file where the user interacts with the application.
   • Prompts the user for matrix dimensions, generates a random matrix with obstacles, and selects random start and goal positions.
   • Allows the user to choose between Manhattan and Euclidean distances for path calculation.
   • Uses the A* algorithm to find the optimal path and displays the matrix with the path on the console.

Instructions:

1. Run main.py to execute the program.
2. Input the number of rows and columns for the matrix when prompted.
3. The program generates a random matrix with obstacles and visually displays the optimal path using the A* algorithm.
4. Choose between Manhattan and Euclidean distances for path calculation.
5. The result, including the matrix with the path and key information, is printed on the console.

Authors:

• Carlos Arroyo
• Gabriel Nassri
• Juan Carlos Estefanía