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Duration 21 hours
Course Outline
Introduction to ROS 2 and Autonomous Navigation
- Exploring the architecture and core capabilities of ROS 2.
- Gaining insight into navigation systems within robotics.
- Establishing a functional ROS 2 development environment.
Managing Sensors and Data Acquisition
- Connecting LiDAR and camera sensors to the system.
- Methods for collecting and processing sensor data streams.
- Utilizing Rviz to visualize sensor outputs.
Foundations of Mapping and Localization
- Understanding the underlying principles of SLAM.
- Developing 2D and 3D mapping capabilities.
- Applying AMCL and other localization techniques.
Path Planning and Obstacle Avoidance Strategies
- Evaluating various path planning algorithms.
- Implementing dynamic obstacle detection and avoidance mechanisms.
- Validating navigation capabilities in simulated scenarios.
Leveraging Gazebo for Simulation
- Configuring Gazebo simulations compatible with ROS 2.
- Testing robot models and integrated navigation stacks.
- Assessing performance metrics in virtual settings.
Implementing SLAM and Navigation on Physical Robots
- Establishing connections between ROS 2 and physical hardware.
- Performing calibration of sensors and actuators.
- Executing real-time navigation experiments.
Optimizing Performance and Troubleshooting
- Resolving common navigation issues within the ROS 2 stack.
- Enhancing the efficiency of SLAM algorithms.
- Refining navigation parameters for optimal results.
Conclusion and Future Directions
Requirements
- A solid grasp of fundamental robotics principles.
- Hands-on experience with Linux-based operating systems.
- Familiarity with programming in Python or C++.
Target Audience
- Robotics engineers.
- Automation developers.
- Professionals in research and development focusing 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.