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🚗 AI-Based Autonomous Navigation System

📌 Project Overview

This project demonstrates an AI-Based Autonomous Navigation System built using Python. It simulates a virtual robot/vehicle that navigates from a start point to a destination while avoiding obstacles using intelligent path planning.

This project replicates the core logic used in real-world systems such as self-driving cars, warehouse robots, and delivery systems.

🎯 Problem Statement

In real-world environments, autonomous systems must:

• Navigate safely
• Detect and avoid obstacles
• Choose the most optimal path

This project solves this problem using a simulation-based approach, making it accessible without requiring expensive hardware.

🌍 Industry Relevance

Autonomous navigation systems are widely used in:

• 🚗 Self-driving cars
• 📦 Warehouse automation
• 🤖 Delivery robots
• 🚁 Drone navigation
• 🏙️ Smart city mobility systems

🧠 Key Features

• Grid-based virtual environment
• Obstacle detection system
• A* path planning algorithm
• Autonomous navigation logic
• Visualization using Matplotlib

🛠️ Tech Stack

• Python
• NumPy
• Matplotlib

🏗️ System Architecture

Input (Grid Environment) ↓ Obstacle Detection ↓ Path Planning (A*) ↓ Navigation Logic ↓ Visualization Output

⚙️ How It Works

1. A grid environment is created
2. Obstacles are placed in the grid
3. Start and goal positions are defined
4. A* algorithm calculates the shortest path
5. The agent follows the path avoiding obstacles
6. Final output is visualized

📁 Folder Structure

AI-Autonomous-Navigation-System/ │ ├── data/ ├── src/ ├── outputs/ │ ├── images/ │ ├── videos/ ├── docs/ ├── main.py ├── requirements.txt ├── README.md ├── .gitignore

⚙️ Installation

Step 1: Clone Repository

git clone https://github.com/Amiya-Krishna/AI-Autonomous-Navigation-System.git cd AI-Autonomous-Navigation-System

Step 2: Create Virtual Environment

python -m venv venv

Step 3: Activate Environment

Windows: venv\Scripts\activate

Mac/Linux: source venv/bin/activate

Step 4: Install Dependencies

pip install -r requirements.txt

▶️ How to Run

python main.py

🧪 Simulation Workflow

Step 1: Create grid environment Step 2: Add obstacles Step 3: Define start and goal Step 4: Apply A* algorithm Step 5: Generate shortest path Step 6: Visualize navigation

📊 Results

• Optimal path successfully generated
• Obstacles avoided efficiently
• Navigation visualized in 2D simulation

📸 Screenshots

🟢 Initial State

🚀 Final State

🚀 Comparison State

🎥 Demo Video

🚀 Future Improvements

• Real-time camera integration (OpenCV)
• ROS (Robot Operating System) integration
• Advanced simulation using CARLA
• Reinforcement Learning-based navigation
• Dynamic obstacle handling
• Multi-agent systems

📚 Learning Outcomes

• Understanding of autonomous navigation systems
• Implementation of A* path planning
• Simulation of real-world AI problems
• Data visualization using Python
• GitHub project structuring

💼 Resume Description

Developed an AI-based autonomous navigation system using A* path planning algorithm in a simulated environment. Implemented obstacle avoidance and optimal path generation using Python, NumPy, and Matplotlib.

👨‍💻 Author

Amiya Krishna Chaurasiya

GitHub: https://github.com/Amiya-Krishna

LinkedIn: www.linkedin.com/in/amiya-krishna

⭐ Support

If you found this project useful:

• ⭐ Star the repository
• 🍴 Fork it
• 🤝 Contribute

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AI-Autonomous-Navigation-System

AI-based autonomous navigation system using A* path planning and simulation