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Duration 21 hours
Course Outline
Foundations of ROS 2 and Autonomous Navigation
- Examining the architecture and capabilities of ROS 2
- Comprehending navigation systems within robotics
- Establishing the ROS 2 development environment
Managing Sensors and Data Collection
- Integrating LiDAR and camera technologies
- Gathering and processing incoming sensor data
- Displaying sensor outputs through Rviz
Core Concepts in Mapping and Localization
- Understanding the underlying principles of SLAM
- Executing 2D and 3D mapping procedures
- Achieving localization via AMCL and alternative methods
Path Planning and Obstacle Avoidance
- Investigating various path planning algorithms
- Detecting and avoiding dynamic obstacles
- Validating navigation strategies in simulated settings
Leveraging Gazebo for Simulation
- Configuring Gazebo simulations alongside ROS 2
- Evaluating robot models and navigation stacks
- Assessing performance metrics within virtual environments
Implementing SLAM and Navigation on Physical Robots
- Linking ROS 2 to physical hardware components
- Calibrating sensors and actuator systems
- Conducting real-time navigation experiments
Debugging and Performance Enhancement
- Resolving navigation issues within the ROS 2 framework
- Optimizing SLAM algorithms for improved efficiency
- Refining navigation parameters for optimal results
Conclusion and Future Directions
Requirements
- A solid grasp of core robotics principles.
- Proficiency with Linux-based operating systems.
- Fundamental programming skills in either Python or C++.
Target Audience
- Robotics engineers
- Automation developers
- Professionals focused on research and development of autonomous systems
Testimonials (1)
Supply of the materials (virtual machine) to get straight into the excersises, and the explanation of the Ros2 core. Why things work a certain way.