ROBOTICS.AJ1
Hands-On ROS for Robotics Programming
Transform your technical career by mastering the Robot Operating System training, the definitive skill set for the modern robotics engineer.
- Practice in 39 Praktische Übungen — nothing to install
- 13 Interaktive Lektionen Und 66 topics mapped to the official exam objectives
Expert Selbstgesteuert · 1 Jahr Zugang
39 Praktische LiveLabs
Practice real IT tasks in guided environments.
- Reale Umgebungen
- Automatisch bewertet
- Keine Installation
01 / Fähigkeiten, die Sie erwerben
What you will be able to do
Are you ready to move beyond basic hobbyist scripts and truly leverage the power of industrial-grade middleware to revolutionize your robotics projects? The role of the developer in this field is fundamentally changing, demanding specialized knowledge in how to strategically integrate complex algorithms into physical systems. This hands-on ROS tutorial moves you past simple "Hello World" examples and dives deep into architecting, securing, and operationalizing robot intelligence across the entire development lifecycle.
You will master ROS2 fundamentals, learn professional URDF modeling for accurate robot representation, and explore Gazebo simulation to test your designs in physics-based virtual environments. Whether you are aiming for precise autonomous navigation, designing multi-robot systems, or implementing advanced SLAM navigation, this program provides the practical, hands-on knowledge to design and launch sophisticated robotics solutions from prototype to production. By focusing on Raspberry Pi robotics and the GoPiGo3 platform, we bridge the gap between simulation and the real world.
- Hardware Foundations & ROS Core: Master the assembly of GoPiGo3 components and Raspberry Pi robotics integration while gaining a deep understanding of ROS architecture, nodes, topics, and services.
- Modeling & Physics Simulation: Design complex robot structures using URDF modeling and validate their real-world behavior within a high-fidelity Gazebo simulation environment.
- Autonomous Navigation & SLAM: Implement SLAM navigation and AMCL for precise localization, enabling your robot to perform autonomous navigation and path planning in dynamic environments.
- Intelligent Control & Reinforcement Learning: Enhance robot capabilities by integrating computer vision and Reinforcement Learning for robotics, training agents to solve goal-driven tasks with OpenAI.
Course Highlights
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13 Strukturierte Lektionen Umfassende Abdeckung der zentralen Kursziele
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39 Praktische LiveLabs Interaktive, geführte Szenarien mit sofortiger Auswertung
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1 Jahr voller Zugang Selbstgesteuertes Lernen, jederzeit auf allen Geräten zugänglich
02 / Lektionen & Labore
See exactly what you will learn and practice
Unterrichtsplan
13 Interaktive Lektionen · 66 topics01 Preface 2 topics +
- Who this course is for
- What this course covers
02 Assembling the Robot 6 topics · 3 LiveLab +
- Understanding the GoPiGo3 robot
- Getting familiar with the embedded hardware
- Deep diving into the electromechanics
- Putting it all together
- Quick hardware test
- Summary
3 LiveLab in this lesson — see the labs panel →
03 Unit Testing of GoPiGo3 3 topics · 6 LiveLab +
- Getting started with Python and JupyterLab
- Unit testing of sensors and drives
- Summary
6 LiveLab in this lesson — see the labs panel →
04 Getting Started with ROS 5 topics · 7 LiveLab +
- ROS basic concepts
- Configuring your ROS development environment
- Communication between ROS nodes – messages and topics
- Using publicly available packages for ROS
- Summary
7 LiveLab in this lesson — see the labs panel →
05 Creating the Virtual Two-Wheeled ROS Robot 6 topics · 5 LiveLab +
- Getting started with RViz for robot visualization
- Building a differential drive robot with URDF
- Inspecting the GoPiGo3 model in ROS with RViz
- Robot frames of reference in the URDF model
- Using RViz to check the model while building
- Summary
5 LiveLab in this lesson — see the labs panel →
06 Simulating Robot Behavior with Gazebo 5 topics · 3 LiveLab +
- Getting started with the Gazebo simulator
- Making modifications to the robot URDF
- Verifying a Gazebo model and viewing the URDF
- Moving your model around
- Summary
3 LiveLab in this lesson — see the labs panel →
07 Programming in ROS - Commands and Tools 9 topics · 2 LiveLab +
- Setting up a physical robot
- A quick introduction to ROS programming
- Case study 1 – writing a ROS distance-sensor package
- Working with ROS commands
- Creating and running publisher and subscriber nodes
- Automating the execution of nodes using roslaunch
- Case study 2 – ROS GUI development tools – the Pi Camera
- Customizing robot features using ROS parameters
- Summary
2 LiveLab in this lesson — see the labs panel →
08 Robot Control and Simulation 5 topics · 5 LiveLab +
- Setting up the GoPiGo3 development environment
- Case study 3 – remote control using the keyboard
- Remote control using ROS topics
- Remotely controlling both physical and virtual robots
- Summary
5 LiveLab in this lesson — see the labs panel →
09 Virtual SLAM and Navigation Using Gazebo 5 topics · 2 LiveLab +
- Dynamic simulation using Gazebo
- Components in navigation
- Robot perception and SLAM
- Practising SLAM and navigation with the GoPiGo3
- Summary
2 LiveLab in this lesson — see the labs panel →
10 SLAM for Robot Navigation 4 topics · 6 LiveLab +
- Preparing an LDS for your robot
- Creating a navigation application in ROS
- Practicing navigation with GoPiGo3
- Summary
6 LiveLab in this lesson — see the labs panel →
11 Applying Machine Learning in Robotics 6 topics · 5 LiveLab +
- Setting up the system for TensorFlow
- ML comes to robotics
- From ML to deep learning
- A methodology to programmatically apply ML in robotics
- Deep learning applied to robotics – computer vision
- Summary
5 LiveLab in this lesson — see the labs panel →
12 Machine Learning with OpenAI Gym 5 topics · 2 LiveLab +
- An introduction to OpenAI Gym
- Running an environment
- Configuring the environment file
- Running the simulation and plotting the results
- Summary
2 LiveLab in this lesson — see the labs panel →
13 Achieve a Goal through Reinforcement Learning 5 topics · 3 LiveLab +
- Preparing the environment with TensorFlow, Keras, and Anaconda
- Understanding the ROS Machine Learning packages
- Setting the training task parameters
- Training GoPiGo3 to reach a target location while avoiding obstacles
- Summary
3 LiveLab in this lesson — see the labs panel →
Praktische Übungen Our edge
39 LiveLabs- Configuring GoPiGo3 Hardware Interfaces for ROS Operation
- Setting Up the Raspberry Pi 3B+ for ROS Operation
- Assembling a Raspberry Pi 3B+ with the GoPiGo3
- Configuring a ROS 2 Workspace and Environment
- Controlling the GoPiGo3 Robot
- Processing Sensor Data Using ROS Nodes
- Training a Model to Classify Sensor Data
- Building a Rule-Based Decision System
- Using AI Prompts to Explore ROS Messages and Topics
- Mastering ROS Node Communication
- Exploring ROS 2 Topics, Messages, and Node Communication
- Monitoring Continuous Sensor Data Using ROS Nodes
- Debugging ROS Nodes and Visualizing Topics
- Simulating Collaborative Tasks
- Exploring the Robot Configuration Using a Differential Drive Robot
- Prompting AI to Inspect and Understand a Robot Model in RViz
- Visualizing Robot Position in an Environment
- Visualizing GoPiGo3 TF Frames in RViz
- Programming Multi-Step Robot Tasks
- Simulating and Evaluating Robot Motion in Gazebo
- Simulating Robot Behavior with Gazebo
- Exploring ROS Subscriber Behavior and Topic Data Flow
- Implementing Keyboard Teleoperation
- Recording and Replaying Human Actions
- Controlling Remote Robots Using ROS Topics
- Controlling a Robot Using /cmd_vel
- Implementing Robot Perception and SLAM Using a Simulated Laser Distance Sensor
- Implementing Wandering and Avoidance Behaviors
- Implementing Goal-Based Navigation
- Creating an Autonomous Delivery/Task Robot
- Implementing an Autonomous Intelligent Robot System
- Practicing Autonomous Navigation with GoPiGo3
- Detecting Objects Using OpenCV
- Recognizing Objects from a Camera Feed
- Building a Perception-Driven Robot
- Applying a Methodology for Machine Learning in Robotics
- Exploring OpenAI Gym for Reinforcement Learning
- Training an Agent in CartPole Using Reinforcement Learning
- Training GoPiGo3 to Reach a Target Location While Avoiding Obstacles
03 / FAQS
Fragen, bevor Sie beginnen
Who should take this ROS programming course?+
Do I need physical hardware to learn?+
Does the training cover the latest industry standards like ROS2?+
How is AI integrated into this robotics program? +
Beyond standard control, we explore Reinforcement Learning for robotics. You will learn to use OpenAI Gym with ROS to train robots for complex tasks, merging traditional SLAM navigation with modern machine learning.
covery.
Keep going
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Ready to Build Certified Robotics Applications?
The future of industry is automated. Start your journey to becoming a lead robotics engineer and transform your technical capabilities with this essential ROS programming course. Enroll in our Robot Operating System training today and lead the next wave of robotics software development!
- 1 Jahr voller Zugang
- 39 LiveLab enthalten
- Abschlusszertifikat
Keine Kreditkarte benötigt