TinyDrone-MicroNav

Jul 2, 2026·
Ömer Kurkutlu
Ömer Kurkutlu
· 1 min read
projects

Overview

TinyDrone-MicroNav is an autonomous navigation framework for nano aerial vehicles that enables safe and efficient navigation using onboard perception and lightweight planning algorithms. The project focuses on resource-constrained platforms such as the Crazyflie 2.1 and aims to bridge the gap between simulation and real-world deployment.

Motivation

Autonomous navigation remains challenging for ultra-lightweight drones because of their limited sensing, computation, memory, and battery capacity. This project investigates how lightweight perception, planning, and learning algorithms can enable reliable navigation in dynamic environments.

Key Features

  • Vision-based autonomous navigation
  • Dynamic obstacle avoidance
  • Local path planning
  • Global recovery strategy
  • Webots simulation
  • Crazyflie 2.1 deployment
  • Simulation-to-real transfer
  • Lightweight algorithms for embedded platforms

Hardware

  • Crazyflie 2.1
  • Multi-ranger Deck
  • Flow Deck
  • Onboard IMU

Software

  • Python
  • Webots
  • Crazyflie Python Library
  • OpenCV
  • NumPy

Current Research

Current work focuses on integrating:

  • Deep Reinforcement Learning
  • Vision-Language-Action Models
  • TinyML
  • Embedded AI
  • Efficient onboard perception

to improve autonomous navigation under severe computational constraints.

Future Work

Future directions include:

  • End-to-end visual navigation
  • Multi-drone collaboration
  • Real-world outdoor navigation
  • Embedded deployment of foundation models
  • Fully onboard autonomous flight

Project Status

Status: 🚧 Active Research Project

This project is currently under active development as part of my PhD research at the University of Illinois Chicago.

Robotics Tiny Drones Autonomous Navigation Reinforcement Learning Vision Crazyflie Webots Embedded AI
Ömer Kurkutlu
Authors
Ömer Kurkutlu
PhD Candidate in Electrical and Computer Engineering
I am a PhD Candidate in Electrical and Computer Engineering at the University of Illinois Chicago. My research focuses on autonomous robotics, vision-based navigation, reinforcement learning, TinyML, embedded AI, and resource-constrained robotic systems.