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Duration 21 hours
Course Outline
Fundamentals of ROS 2 and Autonomous Navigation
- Examining the architecture and core capabilities of ROS 2
- Comprehending navigation systems within robotics
- Preparing the ROS 2 development environment
Sensor Integration and Data Handling
- Connecting LiDAR and camera sensors
- Gathering and processing sensor inputs
- Displaying sensor data streams using Rviz
Basics of Mapping and Localization
- Core principles of SLAM
- Creating 2D and 3D maps
- Achieving localization via AMCL and alternative methods
Path Planning and Obstacle Evasion
- Reviewing various path planning algorithms
- Implementing dynamic obstacle detection and avoidance strategies
- Validating navigation behaviors in simulated settings
Simulating with Gazebo
- Configuring Gazebo simulations alongside ROS 2
- Evaluating robot models and navigation stacks
- Assessing performance metrics in virtual scenarios
Implementing SLAM and Navigation on Physical Robots
- Linking ROS 2 to physical hardware components
- Calibrating sensors and actuators for precision
- Conducting real-time navigation trials
Debugging and Performance Tuning
- Resolving navigation issues within ROS 2
- Enhancing the efficiency of SLAM algorithms
- Refining navigation parameters for optimal performance
Conclusion and Future Directions
Requirements
- Familiarity with fundamental robotics concepts
- Proficiency in Linux-based operating systems
- Foundational programming knowledge in Python or C++
Target Audience
- Robotics engineers
- Automation developers
- R&D specialists working 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.