<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Crazyflie |</title><link>https://omerkurkutlu.github.io/tags/crazyflie/</link><atom:link href="https://omerkurkutlu.github.io/tags/crazyflie/index.xml" rel="self" type="application/rss+xml"/><description>Crazyflie</description><generator>HugoBlox Kit (https://hugoblox.com)</generator><language>en-us</language><lastBuildDate>Thu, 02 Jul 2026 00:00:00 +0000</lastBuildDate><image><url>https://omerkurkutlu.github.io/media/icon_hu_eee4a95885829ab2.png</url><title>Crazyflie</title><link>https://omerkurkutlu.github.io/tags/crazyflie/</link></image><item><title>TinyDrone-MicroNav</title><link>https://omerkurkutlu.github.io/projects/tinydrone-micronav/</link><pubDate>Thu, 02 Jul 2026 00:00:00 +0000</pubDate><guid>https://omerkurkutlu.github.io/projects/tinydrone-micronav/</guid><description>&lt;h2 id="overview"&gt;Overview&lt;/h2&gt;
&lt;p&gt;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.&lt;/p&gt;
&lt;h2 id="motivation"&gt;Motivation&lt;/h2&gt;
&lt;p&gt;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.&lt;/p&gt;
&lt;h2 id="key-features"&gt;Key Features&lt;/h2&gt;
&lt;ul&gt;
&lt;li&gt;Vision-based autonomous navigation&lt;/li&gt;
&lt;li&gt;Dynamic obstacle avoidance&lt;/li&gt;
&lt;li&gt;Local path planning&lt;/li&gt;
&lt;li&gt;Global recovery strategy&lt;/li&gt;
&lt;li&gt;Webots simulation&lt;/li&gt;
&lt;li&gt;Crazyflie 2.1 deployment&lt;/li&gt;
&lt;li&gt;Simulation-to-real transfer&lt;/li&gt;
&lt;li&gt;Lightweight algorithms for embedded platforms&lt;/li&gt;
&lt;/ul&gt;
&lt;h2 id="hardware"&gt;Hardware&lt;/h2&gt;
&lt;ul&gt;
&lt;li&gt;Crazyflie 2.1&lt;/li&gt;
&lt;li&gt;Multi-ranger Deck&lt;/li&gt;
&lt;li&gt;Flow Deck&lt;/li&gt;
&lt;li&gt;Onboard IMU&lt;/li&gt;
&lt;/ul&gt;
&lt;h2 id="software"&gt;Software&lt;/h2&gt;
&lt;ul&gt;
&lt;li&gt;Python&lt;/li&gt;
&lt;li&gt;Webots&lt;/li&gt;
&lt;li&gt;Crazyflie Python Library&lt;/li&gt;
&lt;li&gt;OpenCV&lt;/li&gt;
&lt;li&gt;NumPy&lt;/li&gt;
&lt;/ul&gt;
&lt;h2 id="current-research"&gt;Current Research&lt;/h2&gt;
&lt;p&gt;Current work focuses on integrating:&lt;/p&gt;
&lt;ul&gt;
&lt;li&gt;Deep Reinforcement Learning&lt;/li&gt;
&lt;li&gt;Vision-Language-Action Models&lt;/li&gt;
&lt;li&gt;TinyML&lt;/li&gt;
&lt;li&gt;Embedded AI&lt;/li&gt;
&lt;li&gt;Efficient onboard perception&lt;/li&gt;
&lt;/ul&gt;
&lt;p&gt;to improve autonomous navigation under severe computational constraints.&lt;/p&gt;
&lt;h2 id="future-work"&gt;Future Work&lt;/h2&gt;
&lt;p&gt;Future directions include:&lt;/p&gt;
&lt;ul&gt;
&lt;li&gt;End-to-end visual navigation&lt;/li&gt;
&lt;li&gt;Multi-drone collaboration&lt;/li&gt;
&lt;li&gt;Real-world outdoor navigation&lt;/li&gt;
&lt;li&gt;Embedded deployment of foundation models&lt;/li&gt;
&lt;li&gt;Fully onboard autonomous flight&lt;/li&gt;
&lt;/ul&gt;
&lt;h2 id="project-status"&gt;Project Status&lt;/h2&gt;
&lt;p&gt;&lt;strong&gt;Status:&lt;/strong&gt; 🚧 Active Research Project&lt;/p&gt;
&lt;p&gt;This project is currently under active development as part of my PhD research at the University of Illinois Chicago.&lt;/p&gt;</description></item></channel></rss>