https://github.com/souradeepdutta/drone-path-navigation-simulation

This project explores drone navigation in both simulated and real-world environments using two popular pathfinding algorithms: A* and Bellman-Ford. The implementation demonstrates the application of these algorithms to compute optimal paths while considering obstacles and penalties.
https://github.com/souradeepdutta/drone-path-navigation-simulation

astar-algorithm bellman-ford-algorithm drone drone-path-simulation matlab matlab-simulation openstreetmap pathfinding python python-project

Last synced: about 1 month ago
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This project explores drone navigation in both simulated and real-world environments using two popular pathfinding algorithms: A* and Bellman-Ford. The implementation demonstrates the application of these algorithms to compute optimal paths while considering obstacles and penalties.

• Host: GitHub
• URL: https://github.com/souradeepdutta/drone-path-navigation-simulation
• Owner: souradeepdutta
• License: apache-2.0
• Created: 2024-12-16T17:51:44.000Z (about 1 month ago)
• Default Branch: main
• Last Pushed: 2024-12-16T18:13:10.000Z (about 1 month ago)
• Last Synced: 2024-12-16T18:46:04.659Z (about 1 month ago)
• Topics: astar-algorithm, bellman-ford-algorithm, drone, drone-path-simulation, matlab, matlab-simulation, openstreetmap, pathfinding, python, python-project
• Language: MATLAB
• Homepage:
• Size: 0 Bytes
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# Drone Path Navigation Using A* and Bellman-Ford Algorithms

## Overview

This project explores drone navigation in both simulated and real-world environments using two popular pathfinding algorithms: **A*** and **Bellman-Ford**. The implementation demonstrates the application of these algorithms to compute optimal paths while considering obstacles and penalties. 

Simulations were conducted using:

- **MATLAB** for grid-based environments.

- **Python** (with OpenStreetMap data) for large-scale geographical maps of any location.

## Features

- **A***: Fast, heuristic-driven pathfinding optimized for real-time applications.

- **Bellman-Ford**: Versatile algorithm capable of handling graphs with negative weights.

- **Obstacle Avoidance**: Dynamic penalty assignment for obstacle nodes.

- **Visualization**: Graphical representation of paths and obstacles.

## Technologies Used

- **Programming Languages**: Python, MATLAB

- **Python Libraries**:

  - `OSMnx`: Map data extraction

  - `NetworkX`: Graph manipulation

  - `Matplotlib`: Visualization

  - `Heapq`: Priority queue implementation

## Installation

1. Clone this repository:

   ```bash

   git clone https://github.com/souradeepdutta/Drone-Path-Navigation-Simulation.git

   ```

2. Navigate to the project directory:

   ```bash

   cd python

   ```

3. Install Python dependencies:

   ```bash

   pip install -r requirements.txt

   ```

## Usage

### MATLAB

1. Open the MATLAB scripts in the `MATLAB` directory.

2. Run the `Main.m` to simulate pathfinding on a grid.

### Python

1. Prepare your environment:

   - Ensure Python 3.x is installed.

   - Install dependencies using the command above.

2. Run the Python scripts:

   ```bash

   python A_star.py

   ```

   or

   ```bash

   python Bellman_Ford.py

   ```

3. Visualize the paths on maps of Chennai or Manhattan.

4. Your can modify the location according to your preference.

## Experimental Setup

### Environments

- **Grid-based** (MATLAB): 10x10 grid with predefined obstacles.

- **Geographical Maps** (Python): Road networks extracted using OpenStreetMap.

### Obstacles

- High-weight nodes.

- Infinite-cost edges.

- Dynamic penalties for obstacle avoidance.

### Performance Metrics

- **Computational Time**: Measured in seconds.

- **Path Length**: Measured in number of nodes.

### Results

- **A***: Faster computation due to heuristic-driven approach.

- **Bellman-Ford**: More versatile but slower in complex environments.

## Example Visualizations

### Chennai, India

![Chennai A* Path](images/Chennai_1_astar.png)

### Manhattan, USA

![Manhattan Bellman-Ford Path](images/Manhatten_1_Bellman.png)

## Architecture Diagrams

![A* Architecture](images/Architecture-astar.png)

![Bellman Ford Architecture](images/Architecture-bellman.png)

## Matlab Simulations

![A*](images/astar.png)

![Bellman Ford](images/bellman.png)

## License

This project is licensed under the MIT License. See the [LICENSE](LICENSE) file for details.