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Duration 21 hours
Course Outline
Foundations of ROS 2 and Autonomous Navigation
- Examining the architecture and core capabilities of ROS 2
- Gaining insight into navigation systems within robotics
- Preparing the ROS 2 development environment
Managing Sensors and Data Acquisition
- Connecting LiDAR and camera hardware
- Acquiring and refining sensor data streams
- Using Rviz to visualize sensor output
Core Concepts in Mapping and Localization
- Exploring the principles behind SLAM
- Building 2D and 3D mapping solutions
- Achieving localization via AMCL and alternative methods
Path Planning and Safety Protocols
- Reviewing advanced path planning algorithms
- Implementing dynamic obstacle detection and avoidance
- Validating navigation logic in simulated settings
Leveraging Gazebo for Simulation
- Establishing Gazebo simulations integrated with ROS 2
- Testing robot models and navigation stacks
- Evaluating performance metrics in virtual environments
Deploying SLAM and Navigation on Physical Robots
- Bridging ROS 2 with physical hardware components
- Calibrating sensors and actuators for precision
- Conducting real-time navigation experiments
Debugging and Performance Tuning
- Resolving common navigation issues in ROS 2
- Enhancing SLAM algorithm efficiency
- Refining navigation parameters for optimal performance
Conclusion and Future Directions
Requirements
- A solid grasp of fundamental robotics concepts.
- Hands-on experience working with Linux-based operating systems.
- Proficiency in programming with Python or C++.
Target Audience
- Robotics engineers.
- Automation developers.
- R&D specialists focused on 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.