Course Outline
Introduction to ROS and Python for Robotics
- Overview of ROS features and architecture.
- Benefits of using ROS for mobile robotics.
Understanding ROS
- Core concepts and components.
- ROS file system, directory structure, and communication model.
Setting up the Development Environment
- Installation of ROS and Python.
- Configuration of ROS environment and workspace.
- Connecting a mobile robot platform with ROS.
Creating and Running ROS Nodes with Python
- Creating ROS nodes using Python.
- Running nodes and using command line tools.
- Writing and using ROS node launch files.
- Utilizing ROS parameters and logging.
Creating and Using ROS Topics with Python
- Creating ROS topics with Python.
- Publishing and subscribing to ROS topics.
- Utilizing ROS message types and custom messages.
- Monitoring and recording ROS topics using ROS tools.
Creating and Using ROS Services with Python
- Creating ROS services with Python.
- Requesting and providing ROS services.
- Utilizing ROS service types and custom services.
- Inspecting and calling ROS services using ROS tools.
Creating and Using ROS Actions with Python
- Creating ROS actions with Python.
- Sending and receiving ROS action goals.
- Utilizing ROS action types and custom actions.
- Managing and canceling ROS actions using ROS tools.
Using ROS Packages and Libraries for Mobile Robots
- Using ROS navigation stack for mobile robots.
- Implementing ROS SLAM packages for mobile robots.
- Employing ROS perception packages for mobile robots.
Integrating ROS with Other Frameworks and Tools
- Using ROS with OpenCV for computer vision.
- Using ROS with TensorFlow for machine learning.
- Using ROS with Gazebo for simulation.
- Using ROS with other frameworks and tools.
Troubleshooting and Debugging ROS Applications
- Addressing common issues and errors in ROS applications.
- Applying effective debugging techniques and tools.
- Tips and best practices for improving ROS performance.
Summary and Next Steps
Requirements
- A solid grasp of fundamental robotics concepts and terminology.
- Proficiency in Python programming and data analysis.
- Familiarity with the Linux operating system and command-line utilities.
Target Audience
- Robotics developers.
- Robotics enthusiasts.
Testimonials (3)
Hands-on exercises related to content really helps to understand more about each topic. Also, style of start class with lecture and continue with hands-on exercise is good and helpful to relate with the lecture that presented earlier.
Nazeera Mohamad - Ministry of Science, Technology and Innovation
Course - Introduction to Data Science and AI using Python
Individual support
Simon the 2nd - Cboost
Course - ROS: Programming for Robotics
Examples/exercices perfectly adapted to our domain
