A* Search Algorithm Implementation for UAV

A clean, efficient Python implementation of the A (A-Star)* pathfinding algorithm for UAV. This repository includes the core logic and a suite of benchmarks to evaluate performance across different scenarios.

🛠 Features

• Core A* Logic: Implemented in Astar.py with support for custom heuristics.
• Benchmarking: Multiple scripts to test execution time and memory usage.
• Extensible: Easy to integrate into grid-based games or robotic path-planning simulations.

🚀 Getting Started

Prerequisites

• Python 3.x

Installation

1. Clone the repository:

   git clone [https://github.com/Abhinav8899/AStar.git](https://github.com/Abhinav8899/AStar.git)
   cd AStar
   

💡 Usage

You can easily integrate the A* algorithm into your own projects. Here is a basic example of how to define a grid and find a path:

from Astar import astar

# Define a 2D grid: 0 is empty, 1 is a wall (obstacle)
grid = [
    [0, 0, 0, 0, 0],
    [0, 1, 1, 1, 0],
    [0, 0, 0, 1, 0],
    [0, 1, 0, 0, 0],
    [0, 0, 0, 0, 0]
]

start = (0, 0) # Top-left
goal = (4, 4)  # Bottom-right

# Find the shortest path
path = astar(grid, start, goal)

if path:
    print(f"Path found! Length: {len(path)} steps")
    print(path)
else:
    print("No path found.")

📊 Performance Benchmarking

This repository includes three pre-configured benchmark scenarios to evaluate the algorithm's performance in 3D environments. These scripts measure execution time, node expansion, and path optimality.

How to Run

Execute the benchmarks directly from your terminal:

# Scenario 1: Basic environment with low obstacle density
python benchmarksingle1.py

# Scenario 2: Moderate complexity with intersecting obstacles
python benchmarksingle2.py

# Scenario 3: High-complexity stress test with dense 3D obstacle fields
python benchmarksingle3.py

🤝 Contributing

Feel free to fork this repository and submit pull requests. For major changes, please open an issue first to discuss what you would like to change.

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.