ROS2 Robotics Programming - Interactive Tutorial

📚 Course Overview

This comprehensive 16-week course provides hands-on training in developing intelligent robotics applications using ROS2 (Robot Operating System 2) and Python. Students will master the complete robotics development pipeline, from fundamental Linux skills and ROS2 architecture to advanced autonomous systems including computer vision, SLAM, and navigation.

📖 What You'll Learn

Master ROS2 architecture, computer vision, SLAM, navigation, and simulation for both ground robots and aerial drones

🛠️ What You'll Build

Multi-sensor fusion systems, autonomous navigation, inspection drones, warehouse robots, and more

💻 Technologies

ROS2 Jazzy, Python 3.12+, OpenCV, Gazebo Harmonic, Nav2, TF2, MAVROS

🎯 Who Should Take This Course?

This course is designed exclusively for AI Students at University of Petra

✅ Students pursuing Artificial Intelligence degree
✅ Interest in applying AI to robotics and autonomous systems
✅ Passion for intelligent mobile robots and drones
✅ No prior ROS or Linux experience required!
✅ Basic Python programming knowledge recommended

📅 16-Week Interactive Curriculum

Week 1

🐧 Linux Basics & ROS2 Introduction

Topics:

Ubuntu 24.04 installation and file system
Essential Linux commands and bash scripting
ROS2 architecture and DDS middleware
ROS2 Jazzy installation

🐢 Project: TurtleSim Mastery

Control turtles using command-line, draw geometric shapes, spawn multiple turtles, change colors, and create TurtleSim art!

# Launch TurtleSim
ros2 run turtlesim turtlesim_node

# Control with keyboard
ros2 run turtlesim turtle_teleop_key

# Spawn a new turtle
ros2 service call /spawn turtlesim/srv/Spawn \
  "{x: 2.0, y: 2.0, theta: 0.2, name: 'turtle2'}"
                            

Start Learning

Week 2

📦 Workspace & Package Management

Topics:

Creating ROS2 workspaces with colcon
Python package structure and organization
Package dependencies and manifest files

🔧 Project: Your First ROS2 Package

Build a complete Python package with proper structure, dependencies, and documentation

Start Learning

Week 3

📡 Publishers & Subscribers

Topics:

Node architecture and lifecycle
Topic communication patterns
Creating publishers and subscribers in Python
Real-time data handling

🌡️ Project: Multi-Sensor Fusion System

Simulate IMU, GPS, temperature, and barometer sensors for drone/robot applications with real-time data fusion

Start Learning

Weeks 4-6

🔄 Advanced Communication Patterns

Week 4 - Services: Request/response communication, Robot Task Management Service

Week 5 - Actions: Long-running tasks with feedback, Flight/Navigation Action Server

Week 6 - Parameters & Launch: Dynamic configuration, launch files, Configurable Robot System

Start Learning

Weeks 7-8

🔧 Custom Messages & Transformations

Week 7 - Custom Messages:

Designing custom message types for robotics
Custom services and actions
Project: Intelligent Perception Message System

Week 8 - TF2:

Coordinate frames and transformations
Static and dynamic transform broadcasting
Project: Multi-Platform Coordinate System (Drone + Ground Robot)

Start Learning

Weeks 9-10

👁️ Computer Vision Integration

Week 9 - Camera Setup:

Camera integration with ROS2
OpenCV with cv_bridge
Real-time image processing
Project: Face tracking to control TurtleSim

Week 10 - Object Detection:

Color-based detection and tracking
Contour analysis
Project: Intelligent Object Tracking Robot

Start Learning

Week 11

📊 RViz2 Visualization

RViz2 interface and displays
Visualizing sensor data and robot states
Perception visualization with markers

🖥️ Project: Intelligent Robot Perception Dashboard

Create comprehensive visualization for camera, detections, transforms, and trajectories

Start Learning

Weeks 12-13

🎮 Gazebo Simulation

Week 12 - World Building:

Gazebo Harmonic environment design
Physics and wind simulation
Project: Smart City Environment with no-fly zones

Week 13 - Robot & Drone Modeling:

URDF/SDF modeling
Sensor simulation (LIDAR, camera, IMU, GPS)
Flight controller integration (PX4/ArduPilot)
Project: Intelligent Quadcopter Drone with Vision

Start Learning

Weeks 14-15

🗺️ SLAM & Navigation

Week 14 - SLAM & Mapping:

SLAM concepts and particle filters
AMCL localization
SLAM Toolbox, Gmapping, Cartographer
Project: Intelligent Autonomous Mapper

Week 15 - Navigation:

Nav2 stack configuration
Path planning and costmaps
Behavior tree navigator
Project: Intelligent Delivery System

Start Learning

Week 16

🏆 Final Project

Choose one of six project options:

🏭 Warehouse AMR

Autonomous navigation, package detection, multi-floor operation

🔍 Inspection Drone

Infrastructure inspection, crack detection, 3D mapping

🚨 Surveillance Robot

Patrol planning, anomaly detection, face tracking

🆘 Search & Rescue Drone

Area coverage, thermal imaging, person detection

🤝 Service Robot

Human following, gesture recognition, object delivery

🌾 Agricultural Drone

Field coverage, plant health detection, NDVI analysis

Start Learning

🧠 Test Your Knowledge

1. What does ROS2 use for communication between nodes?

A) HTTP/REST API

B) DDS (Data Distribution Service)

C) WebSockets

D) MQTT

2. Which tool is used to build ROS2 packages?

A) catkin

B) colcon

C) cmake

D) make

3. What does SLAM stand for?

A) System Level Application Module

B) Simultaneous Localization and Mapping

C) Sensor Layout and Management

D) Smart Localization Algorithm Method

🛠️ Project Portfolio

Build 15 hands-on projects throughout the course plus one comprehensive final project

🐢 TurtleSim Art

Control and create artistic patterns with multiple turtles using ROS2 commands

Week 1

🌡️ Sensor Fusion

Multi-sensor data integration for drones and robots with real-time processing

Week 3

👁️ Face Tracker

Computer vision-based face tracking to control TurtleSim movements

Week 9

🚁 Quadcopter Sim

Complete drone simulation with autonomous flight capabilities in Gazebo

Week 13

🗺️ Autonomous Mapper

SLAM-based mapping with autonomous exploration strategy

Week 14

🏆 Final Project

Complete intelligent autonomous system - AMR, drone, or service robot

Week 16

📊 Assessment & Grading

Component	Weight	Description
Weekly Projects (1-15)	20%	Completion and quality of hands-on projects throughout the course
Midterm Exam	25%	Comprehensive exam covering Weeks 1-8 (Theory + Practical)
Final Project	15%	Week 16 comprehensive autonomous system project
Final Exam	40%	Comprehensive final exam covering all course material

📝 Exam Details

Midterm Exam (Week 8):

Theory questions (50%)
Practical coding tasks (50%)
Covers: Linux, ROS2 basics, communication patterns, TF2

Final Exam (Week 16):

Theory questions (40%)
Problem-solving (30%)
Practical implementation (30%)
Covers: All course material with emphasis on SLAM & Navigation

📅 Weekly Schedule

🏫 In-Campus Session: 2 hours

Theory + Live demonstrations + Hands-on practice

Interactive lectures with real-time coding

💻 Asynchronous (غير متزامن): 1 hour

Video tutorials + Reading materials

Self-paced learning and project work

📖 Self-Study: 4-6 hours per week for project completion, practice, and experimentation

Total weekly commitment: 7-9 hours (3 hours structured + 4-6 hours independent work)

🕐 Office Hours

Every Day: 10:00 AM - 12:00 PM

📍 Innovation Center - Robotics Lab / Online (Teams)

Available for: Technical questions, project guidance, debugging support, and consultation

Drop-in welcome or schedule an appointment via email

📚 Resources & Support

                    💻 Required Software
                    Ubuntu 24.04 LTS
ROS2 Jazzy
Python 3.12+
Gazebo Harmonic
OpenCV & NumPy
VS Code / PyCharm

                

🖥️ Hardware

Minimum: 8GB RAM, i5 processor, 50GB storage, webcam

Recommended: 16GB RAM, i7/Ryzen 7, dedicated GPU, 100GB SSD

📖 Documentation & References

🔗 Official Docs

ROS2 Jazzy, Gazebo, Nav2, OpenCV, PX4/ArduPilot

📚 Books

Programming Robots with ROS, Computer Vision, Probabilistic Robotics

💬 Support

Office hours, Discussion forum, GitHub repos

🚀 Ready to Start Your Robotics Journey?

Transform from beginner to robotics developer in just 16 weeks

✅ No prior ROS or Linux experience required

✅ Hands-on projects every week

✅ Build real autonomous systems

✅ Ground robots AND drones

✅ Portfolio-ready final project

📧 Contact Information

Dr. Abdulkarim Albanna

Office Hours: Every Day, 10:00 AM - 12:00 PM

📍 Innovation Center - Robotics Lab / Online via Teams

Robotics Programming with ROS2 and Python

📚 Course Overview

📖 What You'll Learn

🛠️ What You'll Build

💻 Technologies

🎯 Who Should Take This Course?

📅 16-Week Interactive Curriculum

🐧 Linux Basics & ROS2 Introduction

🐢 Project: TurtleSim Mastery

📦 Workspace & Package Management

🔧 Project: Your First ROS2 Package

📡 Publishers & Subscribers

🌡️ Project: Multi-Sensor Fusion System

🔄 Advanced Communication Patterns

🔧 Custom Messages & Transformations

👁️ Computer Vision Integration

📊 RViz2 Visualization

🖥️ Project: Intelligent Robot Perception Dashboard

🎮 Gazebo Simulation

🗺️ SLAM & Navigation

🏆 Final Project

🏭 Warehouse AMR

🔍 Inspection Drone

🚨 Surveillance Robot

🆘 Search & Rescue Drone

🤝 Service Robot

🌾 Agricultural Drone

🧠 Test Your Knowledge

🛠️ Project Portfolio

🐢 TurtleSim Art

🌡️ Sensor Fusion

👁️ Face Tracker

🚁 Quadcopter Sim

🗺️ Autonomous Mapper

🏆 Final Project

📊 Assessment & Grading

📝 Exam Details

📅 Weekly Schedule

🕐 Office Hours

📚 Resources & Support

💻 Required Software

🖥️ Hardware

📖 Documentation & References

🚀 Ready to Start Your Robotics Journey?

📧 Contact Information

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