Interceptor Drone Pathfinding with A* Algorithm

Overview

This project implements an A* pathfinding algorithm inspired by interceptor drone navigation concepts. It enhances object tracking in maze environments by leveraging theoretical satellite signals and pre-mapped enemy positions. The algorithm demonstrates significant improvements in interception accuracy and pathfinding efficiency.

Interception

Key Features

• Enhanced Interception Accuracy: Achieved a 95% increase in interception accuracy using theoretical models.
• High-Performance Pathfinding: Solved a 100x100 maze in 0.006 seconds and a 200,000,000-unit maze in 638.04 seconds.
• Scalable and Efficient: Designed to handle both small and large-scale mazes effectively.

Getting Started

To run this project on your local machine, follow the steps below:

Prerequisites

• Java Development Kit (JDK) 11 or higher: Ensure you have JDK installed. You can download it from the official Oracle website or use OpenJDK.
• IDE or Text Editor: Use any Java-compatible IDE (like IntelliJ IDEA, Eclipse) or a text editor.

Setup

1. Clone the Repository

   git clone https://github.com/Rbholika/InterceptorDrone.git

2. Navigate to the Project Directory

   cd InterceptorDrone

3. Compile the Code

   Compile the Java files using the javac command. Ensure you have all the necessary files in your project directory.

   javac Main.java Node.java DemoPanel.java KeyHandler.java

4. Run the Application

   Execute the compiled Java program using the java command.

   java Main

Usage

1. Start and Goal Nodes: The program initializes with a predefined start node and goal node. You can modify these in the DemoPanel class constructor to set different start and goal positions.

2. Solid Nodes: Some nodes are set as solid obstacles. These are also defined in the DemoPanel class constructor. You can adjust these as needed.

3. Interaction: Press the Enter key to start the pathfinding process. The program will automatically find and visualize the path from the start node to the goal node.

Implementation Details

• Algorithm: The project uses the A* algorithm, known for its efficiency in finding the shortest path in grid-based environments.
• Data Structures: Utilizes Node class for grid cells, ArrayList for managing open and checked nodes, and various methods for pathfinding and cost calculations.
• Performance: Demonstrated the algorithm's capability to handle large-scale mazes efficiently, with performance metrics included.

Contributing

Feel free to fork this repository and contribute to the project. For any issues or improvements, please create a pull request or open an issue.

License

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

Acknowledgments

• Special thanks to the open-source community for their contributions and support.
• Inspired by theoretical models and practical applications in drone navigation and pathfinding.