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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
- Initializing the ROS 2 development environment
Sensor Integration and Data Acquisition
- Incorporating LiDAR and camera sensor data
- Techniques for collecting and processing sensor inputs
- Visualizing sensor outputs through Rviz
Core Concepts in Mapping and Localization
- Understanding the underlying principles of SLAM
- Executing 2D and 3D mapping strategies
- Achieving localization via AMCL and alternative methods
Path Planning and Obstacle Management
- Evaluating various path planning algorithms
- Implementing dynamic obstacle detection and avoidance strategies
- Validating navigation capabilities in simulated settings
Simulating with Gazebo
- Configuring Gazebo simulations integrated with ROS 2
- Assessing robot models and navigation stacks
- Reviewing performance metrics in virtual environments
Real-World Deployment of SLAM and Navigation
- Linking ROS 2 with physical hardware components
- Calibrating sensors and actuators for precision
- Executing real-time navigation experiments
Debugging and Performance Enhancement
- Resolving navigation issues within ROS 2
- Optimizing SLAM algorithms for improved efficiency
- Refining navigation parameters for optimal performance
Conclusion and Future Directions
Requirements
- A solid grasp of fundamental robotics principles
- Proficiency with Linux-based operating systems
- Foundational programming skills in either Python or C++
Target Audience
- Robotics engineers
- Automation developers
- Professionals in R&D roles 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.