🚁 DroneNav-AI

Autonomous Drone Navigation & Path Planning Simulation Platform

DroneNav-AI is a simulation platform for autonomous drone navigation and path planning. It provides a real-time environment to benchmark navigation algorithms, visualize drone movement, and monitor telemetry during flight.

✨ Features

• 🛩️ Autonomous drone navigation simulation
• 🗺️ Real-time path planning and visualization
• 🔎 Multiple path-planning algorithms
• 📡 Real-time telemetry communication
• 🔌 REST APIs for simulation and navigation services
• 🌐 WebSocket-based real-time updates
• 📊 Algorithm benchmarking and comparison
• 🧭 Dynamic navigation and obstacle handling

🧠 Path Planning Algorithms

The project includes implementations and benchmarking of:

• A* — heuristic-based shortest path search
• RRT — Rapidly-exploring Random Tree for path planning
• D Lite* — dynamic path planning for changing environments

🛰️ Technologies Used

• Python
• FastAPI
• WebSockets
• REST APIs
• Path Planning Algorithms
• SLAM
• Kalman Filtering

🏗️ System Overview

The platform simulates an autonomous drone operating in an environment containing obstacles.

The general workflow is:

Environment
     ↓
Sensor / Telemetry Data
     ↓
Localization & Mapping
     ↓
Path Planning
     ↓
Trajectory Generation
     ↓
Drone Navigation
     ↓
Real-Time Telemetry

🔬 Navigation & Localization

The system uses localization and mapping techniques to support autonomous navigation.

Key concepts include:

• SLAM for simultaneous localization and mapping
• Kalman Filter for state estimation and sensor-noise handling
• Path planning for generating feasible navigation routes

⚡ Real-Time Communication

FastAPI is used to provide backend services and REST endpoints, while WebSockets enable real-time communication for telemetry and navigation updates.

This allows the simulation to continuously exchange navigation and flight information.

📊 Purpose

The main objective of DroneNav-AI is to provide a platform for experimenting with and comparing autonomous drone navigation algorithms in simulated environments.

It can be used to study:

• Path-planning performance
• Navigation efficiency
• Dynamic obstacle handling
• Real-time telemetry
• Autonomous navigation concepts

👩‍💻 Author

Simran Dora

B.Tech Computer Science & Engineering — 2026

GitHub

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⭐ If you find this project interesting, feel free to explore the repository.