This project focuses on A* Path Planning, a popular pathfinding algorithm used in robotics and artificial intelligence. A* is known for its efficiency and optimality in finding the shortest path from a start point to a goal point on a grid or graph.

The project includes an implementation of the A* algorithm and visualization of its performance on various maps, making it a valuable resource for understanding path planning in a simulated environment.

The A* algorithm is a search algorithm used to find the optimal path from a start node to a goal node in a weighted graph. It combines two essential components:

• G-Cost (Actual Cost): The cost to reach the current node from the start node.
• H-Cost (Heuristic Cost): An estimation of the cost from the current node to the goal node.

A* selects nodes to expand based on their total cost, which is the sum of G-Cost and H-Cost. By exploring nodes with lower total cost first, A* efficiently finds the shortest path while considering a heuristic to guide its search.

The flowchart above illustrates the key steps of the A* algorithm:

1. Start at the initial node.
2. Expand the node with the lowest total cost (G-Cost + H-Cost).
3. Add neighboring nodes to the open list.
4. Calculate G-Cost and H-Cost for each neighbor.
5. Repeat steps 2-4 until the goal node is reached or no more nodes are available.

map.png	map1.png	map2.png

A* algorithm has been evaluated on each map, demonstrating its ability to find optimal paths in various scenarios. The results are visualized in the following images:

map.png	map1.png	map2.png
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1. Clone this repository:

   git clone https://github.com/MostafaELFEEL/A-star-path-planning.git