Fly a Drone Using an LLM with ROS 2 + Gazebo

Fly a Drone Using an LLM with ROS 2 + Gazebo
Published 9/2026
Created by Mouad Boumediene
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz, 2 Ch
Level: Beginner | Genre: eLearning | Language: English | Duration: 6 Lectures ( 56m ) | Size: 1.1 GB

Build an LLM powered drone with ROS 2, PX4, Gazebo, Ollama and YOLO using natural-language commands and computer vision

What you'll learn
Control a simulated drone using natural-language commands and a local LLM
Set up and run a PX4 drone simulation using ROS 2 Jazzy and Gazebo
Use YOLO and the drone camera to detect objects and react to visual observations
Translate English instructions into multi-step drone missions such as takeoff, search, and flight patterns

Requirements
No previous experience with ROS 2, Gazebo, PX4, or LLM-based robotics is required

Description
Learn how to build and control an AI-powered simulated drone usingLarge Language Models, ROS 2, PX4, Gazebo, Ollama, and YOLO.

You will be able to give instructions such as

“Take off and hover at 5 meters.”

“Fly in a heart shape.”

“Search for a person, then approach the person.”

This beginner-friendly course takes you from a cleanUbuntu 24.04 installation to a complete drone system that can understand natural-language instructions, execute autonomous flight behaviors, and react to objects detected through its camera.

You will begin by setting upROS 2 Jazzy, Gazebo, and PX4 Autopilot, then launch and test a simulated X500 drone. You will also learn how PX4 communicates with ROS 2 through theMicro XRCE-DDS Agent and how the simulated camera is bridged from Gazebo into ROS 2.

Next, you will installOllama and run a local Large Language Model that converts simple English instructions into drone actions. Instead of writing low-level commands.

You will also integrateYOLO object detection with the drone camera, allowing the system to detect objects such as people and vehicles and use those detections during autonomous missions.

Finally, you will launch the complete LLM-controlled drone system and experiment with flight patterns, object search and approach behaviors, multi-step missions, camera visualization, and drone path visualization usingRViz.

No previous experience withROS 2, PX4, Gazebo, or LLM-based robotics is required. The course is designed to give beginners a practical robotics and AI project they can extend, demonstrate in a portfolio, or discuss during robotics and AI job interviews.

Who this course is for
Beginners interested in drones, robotics, ROS 2, and AI
Students who want to build a practical LLM-powered drone project for their CV or portfolio
Robotics and AI learners who want hands-on experience with ROS 2, PX4, Gazebo, YOLO, and local LLMs

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