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ROS 2 (ROBOT OPERATING SYSTEM 2)
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4.5 (2343 Ratings)
ROS 2 (Robot Operating System 2) training helps learners understand how to develop software for robots, autonomous systems, and industrial robotics applications. The course covers ROS 2 fundamentals, Python/C++ programming, robot simulation, sensor integration, navigation, and motion control. Through hands-on labs, practical projects, and real-world troubleshooting, learners build skills to develop and test robotic applications and prepare for careers in robotics software engineering and autonomous systems.
Duration of Training: 40 Hours
Batch Type: Weekdays/Weekends
Mode of Training: Classroom/Online/Corporate Training
Detailed Syllabus • Hands-on Labs • Assignments • Simulation • Practical Projects • Job-Oriented Training
Overview
The Syllabus
Curriculum Designed by Experts
1. Course Overview
ROS 2 is an open-source robotics middleware framework used to build modular robot applications. It provides communication infrastructure, tools, libraries, and development patterns for robots that need to sense their environment, process data, make decisions, and execute actions.
This course takes learners from ROS 2 fundamentals to robot simulation, sensor integration, navigation, motion control, and introductory autonomous robotics development.
Key learning outcomes
• Understand ROS 2 architecture, nodes, topics, services, and actions.
• Develop robot applications using Python (rclpy) and introductory C++ (rclcpp).
• Build custom messages, services, and actions.
• Configure launch files, parameters, and ROS 2 communication.
• Integrate simulated sensors and robot models using URDF and Xacro.
• Simulate robots using Gazebo and visualize them in RViz.
• Implement robot control, TF2 transforms, and sensor data processing.
• Develop an introductory autonomous navigation workflow using Nav2.
• Debug communication, simulation, sensor, and navigation issues.
• Complete portfolio projects relevant to robotics software engineering.
Target job roles
• ROS 2 Developer
• Robotics Software Engineer
• Autonomous Robotics Engineer
• Robot Simulation Engineer
• Robotics Integration Engineer
• Mobile Robot Developer
• Junior Navigation and Perception Engineer
• Industrial Robotics Software Engineer
2. Prerequisites
• Basic programming knowledge in Python; C++ is beneficial.
• Basic Linux command-line skills.
• Familiarity with variables, functions, classes, and debugging.
• Basic mathematics, including coordinate systems and vectors.
• Basic understanding of mechanics or electronics is helpful but not mandatory.
3. Fundamentals
• Introduction to robotics, automation, and autonomous systems.
• Robot hardware: sensors, actuators, motors, and controllers.
• Robot software architecture and middleware concepts.
• ROS 2 architecture, distributions, and workspace structure.
• Linux filesystem, terminal, packages, and environment variables.
• Python programming for robotics.
• Basic C++ programming concepts for robotics.
• Coordinate frames, position, orientation, and velocity.
• Introduction to kinematics, feedback control, and sensor data.
• Differences between ROS 1 and ROS 2.
• DDS (Data Distribution Service) and the ROS 2 communication model.
• Real-time robotics concepts, reliability, and system integration.
4. Core Technical Topics — Main Syllabus
Core Technical Topics — Main Syllabus
Module 1: ROS 2 Architecture and Development Environment
• ROS 2 architecture, distributions, installation, and environment setup.
• Workspace creation, packages, build systems, and overlays.
• colcon build tool and dependency management.
• ROS 2 CLI: nodes, topics, services, actions, and interfaces.
• Node lifecycle and process management.
• DDS middleware, discovery, and Quality of Service (QoS) concepts.
• ROS 2 domain IDs and multi-node communication.
Module 2: ROS 2 Programming with Python and C++
• Creating ROS 2 packages using Python and C++.
• Nodes, executors, timers, callbacks, and spinning.
• Publishers and subscribers.
• Services and clients.
• Actions, goals, feedback, and results.
• Standard messages: std_msgs, geometry_msgs, sensor_msgs, and nav_msgs.
• Custom messages, services, and actions.
• Parameters, logging, launch files, and node composition concepts.
• Error handling, code organization, and package testing.
Module 3: Robot Description, TF2, and Visualization
• URDF robot description and Xacro macros.
• Links, joints, inertial properties, and visual/collision geometry.
• Robot state publisher and joint state publisher.
• TF2 coordinate frames and transform trees.
• Fixed, revolute, and continuous joints.
• RViz visualization of robot models, sensor data, and transforms.
• Basic differential-drive robot modeling.
• Frame alignment, coordinate conventions, and robot model validation.
Module 4: Robot Simulation and Control
• Gazebo simulation environment and robot world setup.
• Spawn and control a robot in a simulated environment.
• Simulated camera, LiDAR, IMU, and odometry sensors.
• ROS 2 control concepts and controller-manager architecture.
• Differential-drive controller and joint interfaces.
• Velocity commands using geometry_msgs/Twist or the supported command interface.
• Basic proportional and PID control concepts.
• Sensor topics, odometry, and feedback control.
• Simulation clock, time synchronization, and debugging.
Module 5: Sensors, Perception, and Data Processing
• Sensor message types and timestamp handling.
• LiDAR scans and point-cloud fundamentals.
• Camera image topics and image processing with OpenCV.
• IMU and odometry data interpretation.
• Sensor-data filtering and basic noise handling.
• Message synchronization and sensor fusion concepts.
• Coordinate transformation of sensor measurements using TF2.
• Introduction to visual perception and obstacle detection.
• Bag recording, playback, and repeatable data analysis.
Module 6: Navigation and Autonomous Mobile Robots
• Mobile robot kinematics and odometry.
• Mapping concepts and occupancy-grid maps.
• Introduction to SLAM Toolbox.
• Localization and the robot’s pose estimate.
• Nav2 architecture and navigation servers.
• Costmaps, planners, controllers, and behavior trees.
• Goal-based navigation and waypoint following.
• Obstacle avoidance and recovery behaviors.
• Map, TF, localization, and costmap troubleshooting.
• Autonomous navigation testing in simulation.
Module 7: Integration, Testing, and Deployment
• Launch-file orchestration for complete robot applications.
• Parameter files and configuration management.
• ROS 2 QoS tuning for reliable sensor communication.
• Unit and integration testing using ament and launch_testing.
• Logging, diagnostics, rosbag capture, and replay.
• Git-based development and team collaboration.
• Introduction to lifecycle nodes and managed startup.
• Linux process management and deployment fundamentals.
• Real-time considerations, safety stops, and failure handling.
• Debugging multi-node applications and preparing project documentation.
5. Hands-On Labs
1 Set up a ROS 2 environment and create a workspace
2 Build and run a basic ROS 2 node
3 Create a Python publisher and subscriber
4 Implement a service server and client
5 Build an action server and action client
6 Create a custom message and interface package
7 Configure parameters and launch multiple nodes
8 Explore QoS settings and communication behavior
9 Build a URDF robot with links and joints
10 Visualize the robot model and TF tree in RViz
11 Spawn a mobile robot in Gazebo
12 Control robot movement using velocity commands
13 Read and process simulated LiDAR and IMU data
14 Record and replay robot data using rosbag
15 Implement basic sensor processing using OpenCV
16 Configure a differential-drive controller
17 Generate and inspect an occupancy-grid map
18 Configure basic SLAM and localization
19 Run Nav2 navigation to a target pose
20 Debug navigation failures and validate the final robot workflow
6. Assignments
1. Create a ROS 2 workspace and build a package using colcon.
2. Develop a publisher/subscriber application for robot status data.
3. Implement a service to reset a simulated robot’s state.
4. Create an action client that sends a movement goal and processes feedback.
5. Design custom messages for robot telemetry.
6. Configure a launch file with parameters for multiple nodes.
7. Investigate a communication failure caused by incompatible QoS settings.
8. Create a URDF model of a two-wheel mobile robot.
9. Visualize coordinate frames and correct a TF2 transformation error.
10. Implement a velocity controller for a simulated differential-drive robot.
11. Process LiDAR scans to identify nearby obstacles.
12. Record sensor topics and replay them for repeatable testing.
13. Generate a map using a simulated mobile robot.
14. Configure a Nav2 goal-navigation task.
15. Diagnose a navigation failure caused by incorrect frames, localization, or costmap configuration.
7. Mini Projects
Project 1: Differential-Drive Robot Controller
Develop a ROS 2 application that moves a two-wheel robot, accepts velocity commands, publishes odometry, and stops safely when required.
Technologies: ROS 2, Python/C++, Gazebo, RViz
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Project 2: LiDAR-Based Obstacle Detection
Process laser scans to detect nearby obstacles, publish obstacle status, and implement a basic stop-or-turn behavior.
Technologies: ROS 2, sensor_msgs, TF2, simulation
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Project 3: Autonomous Mapping and Navigation
Generate a map in simulation, configure localization, and use Nav2 to navigate between target locations while avoiding obstacles.
Technologies: Gazebo, SLAM Toolbox, Nav2, RViz
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Project 4: Camera-Based Object Detection
Subscribe to a camera stream, process images using OpenCV, and publish detection results or object-position estimates.
Technologies: ROS 2, OpenCV, Python, image_transport
8. Capstone Projects
Capstone 1: Autonomous Warehouse Mobile Robot
Objective: Build a simulated robot that navigates a warehouse environment and reaches assigned pickup and delivery points.
Scope:
• Develop a robot model using URDF/Xacro.
• Configure simulation, sensors, and differential-drive control.
• Generate a map using SLAM or use a prepared map.
• Configure localization and Nav2 navigation.
• Implement waypoint-based missions.
• Test obstacle avoidance and recovery behaviors.
• Record runs, analyze failures, and document performance.
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Capstone 2: ROS 2 Sensor Integration and Monitoring System
Objective: Develop a modular robotics application that collects sensor data and provides status monitoring.
Scope:
• Integrate simulated LiDAR, IMU, camera, and odometry topics.
• Build publisher/subscriber and processing nodes.
• Apply TF2 transformations and timestamp handling.
• Implement sensor-health and communication-status monitoring.
• Record and replay test data.
• Validate behavior during missing or delayed sensor messages
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Capstone 3: Vision-Assisted Mobile Robot
Objective: Combine camera processing with mobile robot control in simulation.
Scope:
• Configure a simulated camera and robot model.
• Process images using OpenCV.
• Publish object detections and related metadata.
• Integrate detection results with robot behavior.
• Add safety limits and stop conditions.
• Test different lighting, distance, and object-position scenarios in simulation.
• Document the architecture, limitations, and test results.
Expected deliverables: Source code, ROS 2 packages, launch files, robot description, configuration files, simulation world, test cases, rosbag recordings, demonstration video or screenshots, and technical documentation.
9. Real-Time Job-Oriented Scenarios
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Job Scenario | Practical Task |
|---|---|
Robot Software Development | Build modular nodes for sensing, control, and status monitoring. |
Robot Integration | Connect sensors and controllers using ROS 2 interfaces. |
Mobile Robot Navigation | Configure localization, mapping, and Nav2 goal execution. |
Sensor Integration | Process LiDAR, camera, IMU, and odometry messages. |
Simulation Engineering | Reproduce robot behavior in Gazebo before hardware testing. |
Communication Debugging | Investigate topic discovery and QoS incompatibility. |
TF Troubleshooting | Correct inconsistent coordinate frames and robot transforms. |
Autonomous Operation | Implement waypoint missions and obstacle handling. |
Testing and Validation | Build repeatable tests using recorded sensor data and automated assertions. |
Deployment Support | Configure launch files, parameters, logging, and startup behavior. |
Performance Investigation | Identify callback delays, excessive processing, and message backlogs. |
Hardware Integration | Connect ROS 2 nodes to supported motor controllers and sensors in a controlled test environment. |
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10. Troubleshooting Scenarios
1. Node cannot discover another node: Check domain ID, network configuration, middleware, and discovery settings.
2. Topic messages are not received: Inspect topic names, message types, publishers, subscribers, and QoS compatibility.
3. Package build fails: Check dependencies, package.xml, build configuration, and workspace overlays.
4. Robot model does not appear in RViz: Validate URDF/Xacro, robot description, fixed frame, and TF publishers.
5. Robot moves incorrectly: Check velocity commands, wheel configuration, controller parameters, and odometry.
6. TF extrapolation errors: Investigate timestamps, transform availability, and simulatedtime configuration.
7. LiDAR readings appear misaligned: Verify sensor frames, transform direction, and mounting configuration.
8. Gazebo plugin fails: Check plugin availability, model configuration, and ROS integration compatibility.
9. SLAM map is distorted: Inspect odometry quality, sensor alignment, movement, and transform timing.
10. Nav2 cannot reach a goal: Examine localization, map frames, costmaps, footprint, planners, and recovery behavior.
11. Camera processing is slow: Profile image processing, reduce unnecessary copies, and evaluate resolution and processing frequency.
12. ROS 2 application behaves inconsistently: Inspect callback execution, concurrency, shared state, and timing assumptions.
11. Industry Tools and Technologies
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Tool / Technology | Application in Training |
|---|---|
ROS 2 | Robotics middleware, nodes, communication, and application development |
Ubuntu Linux | Robotics development environment |
Python / rclpy | Robotics scripting and application logic |
C++ / rclcpp | Performance-oriented robotics nodes |
colcon | Workspace building and package management |
Gazebo | Robot and environment simulation |
RViz | Robot model, sensor, and TF visualization |
URDF / Xacro | Robot description and modeling |
Nav2 | Autonomous mobile robot navigation |
SLAM Toolbox | Mapping and simultaneous localization and mapping |
ros2_control | Robot hardware interfaces and controller management |
TF2 | Coordinate frame transformations |
OpenCV | Image processing and computer vision |
Git / GitHub | Version control and collaboration |
Docker | Reproducible development environments and deployment |
Visual Studio Code | Code editing, debugging, and development |
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12. Best Practices
• Use supported ROS 2 distributions and compatible dependencies.
• Follow consistent package, node, topic, and interface naming conventions.
• Separate sensing, perception, planning, and control into modular nodes.
• Use appropriate QoS settings for sensor streams and critical communication.
• Handle callback timing, concurrency, and shared state carefully.
• Validate TF2 frames, timestamps, and coordinate conventions.
• Use parameters and launch files instead of hardcoding configuration.
• Implement safety stops, command limits, and fault-handling mechanisms.
• Test robot behavior in simulation before hardware deployment.
• Use rosbag recordings for repeatable debugging and regression testing.
• Maintain unit tests, integration tests, and version control.
• Document hardware assumptions, dependencies, limitations, and deployment steps.
13. Certification Names
ROS 2 does not have one universally recognized official certification that is mandatory for robotics software engineering roles. Relevant training credentials include:
• ROS 2 training completion certificate from a recognized robotics training provider.
• The Construct ROS / ROS 2 course certificates, depending on current course availability.
• NVIDIA robotics and Isaac Sim training credentials, where available and relevant to simulation or accelerated robotics.
• Vendor-specific robotics certifications for supported robot platforms and industrial automation systems.
For a ROS 2 developer program, practical portfolio projects and demonstrated programmingability are particularly important. Verify current certification availability and credential status before advertising a specific examination as an official ROS 2 certification.
14. Mock Interviews
• ROS 2 architecture, nodes, topics, services, and actions.
• Python/C++ node development and callback debugging.
• DDS, QoS, and communication troubleshooting.
• URDF, TF2, and robot coordinate frames.
• Gazebo simulation and robot control.
• LiDAR processing, SLAM, localization, and Nav2.
• ROS 2 testing, rosbag, launch files, and deployment.
• Capstone walkthrough and real-world debugging exercises
15. Recommended 40-Hour Training Plan
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Module | Hours |
|---|---|
ROS 2 Architecture and Development Environment | 4 |
Python/C++ Nodes, Topics, Services, and Actions | 7 |
URDF, TF2, and RViz | 4 |
Gazebo Simulation and Robot Control | 5 |
Sensors, Perception, and Rosbag | 5 |
SLAM, Localization, and Nav2 | 7 |
Integration, Testing, and Troubleshooting | 3 |
Mini Project and Capstone Implementation | 4 |
Mock Interviews and Project Review | 1 |
Total | 40 Hours |
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