Here’s a polished README you can directly paste into your README.md.
Interactive AI search and autonomous navigation simulator built using Python and Streamlit.
This project demonstrates shortest-path planning, obstacle avoidance, and dynamic path replanning using Dijkstra’s Algorithm in multiple navigation environments.
- Computes shortest path between Indian cities
- Uses weighted graph representation
- Displays optimal route and total distance
- Simulates UGV movement in a 70x70 grid
- Random obstacle generation
- Finds shortest collision-free path
- Displays runtime and explored nodes
- Introduces obstacles during navigation
- Recalculates optimal route dynamically
- Compares original vs replanned paths
- Includes runtime and path-length analytics
- Python
- Streamlit
- NumPy
- Matplotlib
- Heap-based Priority Queue
- Dijkstra’s Algorithm
Used for:
- shortest path computation
- grid navigation
- dynamic replanning
When new obstacles appear:
- current path becomes invalid
- search space updates
- shortest path recalculated in real time
UGV-Pathfinding-Navigation-Simulator/
│
├── data/
├── results/
├── src/
├── screenshots/
│
├── ugv_visualizer.py
├── README.md
└── requirements.txt
git clone https://github.com/MARIO-SNH/UGV-Pathfinding-Navigation-Simulator.gitcd UGV-Pathfinding-Navigation-Simulatorpython -m venv venv.\venv\Scripts\Activate.ps1pip install -r requirements.txtstreamlit run ugv_visualizer.pyThe simulator evaluates:
- Path Length
- Runtime
- Explored Nodes
- Success Rate
- Autonomous Vehicles
- Robotics Navigation
- AI Search Algorithms
- Dynamic Route Planning
- Pathfinding Systems
Namratha Hannah