{"id":23124860,"url":"https://github.com/souradeepdutta/drone-path-navigation-simulation","last_synced_at":"2026-04-20T13:07:13.253Z","repository":{"id":268433441,"uuid":"904344283","full_name":"souradeepdutta/Drone-Path-Navigation-Simulation","owner":"souradeepdutta","description":"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.","archived":false,"fork":false,"pushed_at":"2024-12-16T18:13:10.000Z","size":3164,"stargazers_count":0,"open_issues_count":0,"forks_count":1,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-04-04T05:09:37.927Z","etag":null,"topics":["astar-algorithm","bellman-ford-algorithm","drone","drone-path-simulation","matlab","matlab-simulation","openstreetmap","pathfinding","python","python-project"],"latest_commit_sha":null,"homepage":"","language":"MATLAB","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"apache-2.0","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/souradeepdutta.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2024-12-16T17:51:44.000Z","updated_at":"2024-12-16T18:16:13.000Z","dependencies_parsed_at":"2024-12-16T18:46:10.969Z","dependency_job_id":"ad6c5e3c-ea07-40d4-bd4f-8421c6fbc0e2","html_url":"https://github.com/souradeepdutta/Drone-Path-Navigation-Simulation","commit_stats":null,"previous_names":["souradeepdutta/drone-path-navigation-simulation"],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/souradeepdutta%2FDrone-Path-Navigation-Simulation","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/souradeepdutta%2FDrone-Path-Navigation-Simulation/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/souradeepdutta%2FDrone-Path-Navigation-Simulation/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/souradeepdutta%2FDrone-Path-Navigation-Simulation/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/souradeepdutta","download_url":"https://codeload.github.com/souradeepdutta/Drone-Path-Navigation-Simulation/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":247123107,"owners_count":20887261,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["astar-algorithm","bellman-ford-algorithm","drone","drone-path-simulation","matlab","matlab-simulation","openstreetmap","pathfinding","python","python-project"],"created_at":"2024-12-17T08:11:34.659Z","updated_at":"2026-04-20T13:07:13.220Z","avatar_url":"https://github.com/souradeepdutta.png","language":"MATLAB","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Drone Path Navigation Using A* and Bellman-Ford Algorithms\n\n## Overview\nThis 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. \n\nSimulations were conducted using:\n- **MATLAB** for grid-based environments.\n- **Python** (with OpenStreetMap data) for large-scale geographical maps of any location.\n\n## Features\n- **A***: Fast, heuristic-driven pathfinding optimized for real-time applications.\n- **Bellman-Ford**: Versatile algorithm capable of handling graphs with negative weights.\n- **Obstacle Avoidance**: Dynamic penalty assignment for obstacle nodes.\n- **Visualization**: Graphical representation of paths and obstacles.\n\n## Technologies Used\n- **Programming Languages**: Python, MATLAB\n- **Python Libraries**:\n  - `OSMnx`: Map data extraction\n  - `NetworkX`: Graph manipulation\n  - `Matplotlib`: Visualization\n  - `Heapq`: Priority queue implementation\n\n## Installation\n1. Clone this repository:\n   ```bash\n   git clone https://github.com/souradeepdutta/Drone-Path-Navigation-Simulation.git\n   ```\n2. Navigate to the project directory:\n   ```bash\n   cd python\n   ```\n3. Install Python dependencies:\n   ```bash\n   pip install -r requirements.txt\n   ```\n\n## Usage\n### MATLAB\n1. Open the MATLAB scripts in the `MATLAB` directory.\n2. Run the `Main.m` to simulate pathfinding on a grid.\n\n### Python\n1. Prepare your environment:\n   - Ensure Python 3.x is installed.\n   - Install dependencies using the command above.\n2. Run the Python scripts:\n   ```bash\n   python A_star.py\n   ```\n   or\n   ```bash\n   python Bellman_Ford.py\n   ```\n3. Visualize the paths on maps of Chennai or Manhattan.\n4. Your can modify the location according to your preference.\n\n## Experimental Setup\n### Environments\n- **Grid-based** (MATLAB): 10x10 grid with predefined obstacles.\n- **Geographical Maps** (Python): Road networks extracted using OpenStreetMap.\n\n### Obstacles\n- High-weight nodes.\n- Infinite-cost edges.\n- Dynamic penalties for obstacle avoidance.\n\n### Performance Metrics\n- **Computational Time**: Measured in seconds.\n- **Path Length**: Measured in number of nodes.\n\n### Results\n- **A***: Faster computation due to heuristic-driven approach.\n- **Bellman-Ford**: More versatile but slower in complex environments.\n\n## Example Visualizations\n### Chennai, India\n![Chennai A* Path](images/Chennai_1_astar.png)\n\n### Manhattan, USA\n![Manhattan Bellman-Ford Path](images/Manhatten_1_Bellman.png)\n\n## Architecture Diagrams\n\n![A* Architecture](images/Architecture-astar.png)\n\n![Bellman Ford Architecture](images/Architecture-bellman.png)\n\n## Matlab Simulations\n\n![A*](images/astar.png)\n\n![Bellman Ford](images/bellman.png)\n\n## License\nThis project is licensed under the MIT License. See the [LICENSE](LICENSE) file for details.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsouradeepdutta%2Fdrone-path-navigation-simulation","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fsouradeepdutta%2Fdrone-path-navigation-simulation","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsouradeepdutta%2Fdrone-path-navigation-simulation/lists"}