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Duration 21 hours
Course Outline
Introduction to ROS 2 and Autonomous Navigation
- Examination of ROS 2 architectural components and core capabilities
- An analysis of navigation system dynamics within robotics
- Establishing the foundational ROS 2 development environment
Sensor Integration and Data Processing
- Incorporation of LiDAR and camera sensor systems
- Strategies for efficient data collection and signal processing
- Visualization of sensor data streams using Rviz
Foundations of Mapping and Localization
- Core principles underlying SLAM technology
- Execution of 2D and 3D mapping techniques
- Localization methods utilizing AMCL and complementary approaches
Path Planning and Obstacle Management
- Review of advanced path planning algorithms
- Techniques for dynamic obstacle detection and evasion
- Validation of navigation logic within simulated contexts
Leveraging Gazebo for Simulation
- Initialization of Gazebo simulations linked with ROS 2
- Verification of robot models and navigation stacks
- Performance analysis within virtual testing environments
Implementation of SLAM and Navigation on Physical Robots
- Establishing communication between ROS 2 and physical hardware
- Calibration processes for sensors and actuators
- Execution of real-time navigation trials
Diagnosis and Performance Tuning
- Debugging navigation anomalies within the ROS 2 framework
- Refining SLAM algorithms for optimal efficiency
- Adjusting navigation parameters for precision
Conclusion and Future Directions
Requirements
- A solid grasp of fundamental robotics concepts
- Proficiency with Linux-based operating systems
- Familiarity with Python or C++ programming fundamentals
Target Audience
- Robotics engineers
- Automation developers
- Professionals in R&D specializing in 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.