ROS2 Robotics - From Basics to Autonomous Systems

Course Overview

Welcome to the comprehensive ROS2 (Robot Operating System 2) course! Learn to build intelligent, autonomous robots using the industry-standard robotics middleware framework.

What You'll Learn

By completing this course, you will be able to:

• ✅ Understand ROS2 architecture and core concepts
• ✅ Create and manage ROS2 nodes, topics, and services
• ✅ Build robot navigation and mapping systems
• ✅ Implement computer vision and perception
• ✅ Develop autonomous robot behaviors
• ✅ Simulate robots in Gazebo
• ✅ Deploy real-world robotic applications

Why ROS2?

ROS2 is the next generation of ROS, offering:

• Real-time performance for safety-critical systems
• Multi-robot support out of the box
• Security with DDS (Data Distribution Service)
• Cross-platform (Linux, Windows, macOS)
• Industry adoption by companies like BMW, NASA, Amazon

Course Structure

8 Comprehensive Chapters

1. Introduction to ROS2 - Setup, architecture, first nodes
2. ROS2 Communication - Topics, services, actions, parameters
3. Robot Simulation - Gazebo, URDF, robot modeling
4. Navigation & SLAM - Autonomous navigation, mapping
5. Computer Vision - Camera integration, object detection
6. Sensor Fusion - LiDAR, IMU, sensor integration
7. Autonomous Behaviors - State machines, decision making
8. Real Robot Deployment - Hardware integration, debugging

Hands-On Labs

Each chapter includes practical labs with:

• Real robot simulations in Gazebo
• Code examples in Python and C++
• Step-by-step tutorials
• Challenge projects

Prerequisites

Required Knowledge:

• Programming: Python (intermediate) or C++ (basic)
• Linux: Basic command line skills
• Math: Basic linear algebra, geometry

Recommended:

• Understanding of robotics concepts
• Experience with Ubuntu/Linux
• Basic control theory knowledge

Tools & Software

You'll work with:

• ROS2 Humble (LTS version)
• Ubuntu 22.04 (recommended)
• Gazebo simulator
• RViz2 visualization
• Python 3 and C++17
• OpenCV for vision
• Nav2 for navigation

Learning Path

graph LR
    A[ROS2 Basics] --> B[Communication]
    B --> C[Simulation]
    C --> D[Navigation]
    D --> E[Vision]
    E --> F[Sensors]
    F --> G[Behaviors]
    G --> H[Real Robot]
    
    style A fill:#C9B59C
    style D fill:#C9B59C
    style H fill:#C9B59C

Course Projects

Progressive Robot Development:

1. Simple Publisher/Subscriber - Hello World robot
2. Teleoperation Robot - Keyboard control
3. Obstacle Avoidance - Autonomous navigation
4. Line Following - Computer vision
5. Mapping Robot - SLAM implementation
6. Delivery Robot - Full autonomous system

Assessment

• Labs: 50% (8 practical assignments)
• Mid-term Project: 20% (Autonomous navigation)
• Final Project: 30% (Complete robot system)

Schedule

Week	Chapter	Topics	Lab
1	Introduction	ROS2 setup, nodes, packages	Lab 1: First node
2	Communication	Topics, services, actions	Lab 2: Publisher/Subscriber
3	Simulation	Gazebo, URDF, robot models	Lab 3: Robot in Gazebo
4	Navigation	Nav2, costmaps, path planning	Lab 4: Autonomous nav
5	Vision	Camera, OpenCV, detection	Lab 5: Object tracking
6	Sensors	LiDAR, IMU, sensor fusion	Lab 6: Multi-sensor
7	Behaviors	State machines, BT	Lab 7: Behavior tree
8	Deployment	Hardware, debugging	Lab 8: Real robot

Hardware (Optional)

For real robot deployment:

• TurtleBot3 (recommended for beginners)
• Raspberry Pi 4 or similar SBC
• LiDAR (e.g., RPLiDAR A1)
• Camera (e.g., Raspberry Pi Camera)
• IMU sensor

Note: All labs can be completed in simulation without hardware

Resources

• ROS2 Documentation: docs.ros.org
• Gazebo Tutorials: gazebosim.org
• Nav2 Documentation: navigation.ros.org
• Community: ROS Discourse, Stack Overflow

Getting Started

Installation Steps:

1. Install Ubuntu 22.04 (or use Docker)
2. Install ROS2 Humble
3. Set up workspace
4. Install Gazebo and RViz2
5. Clone course repository

Detailed instructions in Chapter 1!

Support

• Office Hours: Tuesday & Thursday, 3-5 PM
• Discussion Forum: Ask questions, share projects
• Lab Sessions: Hands-on help with TAs

---

Ready to build autonomous robots? Let's start with Chapter 1! 🤖🚀