Algorithm Workflow Diagram

This README.md explains the architecture represented in the flowchart. The architecture is designed to demonstrate the step-by-step workflow of a pathfinding algorithm, such as A (A-star)*. Below are the detailed steps:

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Workflow Steps

1. Initialization

• Start Node:

  • The algorithm begins with a specified start node.
  • This node is added to the Priority Queue for processing.

• Initialize g_score and f_score:

  • g_score: Represents the cost from the start node to the current node.
  • f_score: Estimated total cost (g_score + heuristic).
  • These scores are initialized for all nodes:
    • Start node: g_score = 0, f_score = heuristic(start).
    • Other nodes: g_score = ∞, f_score = ∞.

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2. Main Loop

• Dequeue Node:

  • The node with the lowest f_score is dequeued from the Priority Queue and set as the Current Node.

• Check Goal:

  • If the current node is the Goal Node, the algorithm terminates successfully:
    • The path is constructed.
    • A success message is returned.

• Expand Neighbors:

  • If the current node is not the goal, the algorithm explores its neighbors (adjacent nodes).

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3. Neighbor Processing

For each neighbor of the Current Node:

1. Calculate g_score and f_score:

   • Compute:
     • g_score: Tentative cost from the start node to the neighbor.
     • f_score: g_score + heuristic(neighbor).

2. Update Priority Queue:

   • If the new f_score is lower than the previously recorded f_score for the neighbor:
     • Update the neighbor's scores.
     • Add or update the neighbor in the Priority Queue.

3. Push Neighbor to Queue:

   • Push the neighbor into the queue for further exploration.

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4. Post-Processing

• Mark Node as Visited:

  • After processing all neighbors, mark the current node as visited.

• Queue Empty:

  • If the Priority Queue becomes empty and the goal has not been found:
    • The algorithm terminates with a failure.

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5. Visualization

• No Path Found:

  • If no path is found, display a failure message.

• Visualize Path:

  • If a path is found:
    • Construct and visualize the path on the grid.

• Update Grid UI:

  • Update the user interface with the final path or grid status.

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Diagram Overview

Below is the diagram representing the architecture of the algorithm.

(Insert Diagram Here)

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Technologies Used

• Algorithm: A* (A-star) or similar heuristic-based pathfinding algorithm.
• Data Structures:
  • Priority Queue for node selection.
  • Graph representation for nodes and edges.
• Visualization: Updates to the grid UI for user feedback.

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How to Use

1. Clone the repository and set up the project.
2. Refer to the implementation files:
   • Algorithm logic is in pathfinding_algorithm.py.
   • Visualization logic is in visualizer.py.
3. Run the application:

   python main.py