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Duration 21 hours
Course Outline
Foundations of ROS 2 and Autonomous Navigation
- An examination of ROS 2 architecture and its core capabilities
- Exploring the role of navigation systems in robotics
- Configuring the initial ROS 2 development environment
Handling Sensors and Data Acquisition
- Incorporating LiDAR and camera sensor data streams
- Techniques for collecting and processing raw sensor inputs
- Visualizing sensor outputs effectively using Rviz
Core Concepts in Mapping and Localization
- Key principles underlying SLAM technology
- Methods for implementing both 2D and 3D mapping
- Achieving localization through AMCL and alternative techniques
Strategies for Path Planning and Obstacle Avoidance
- Review of advanced path planning algorithms
- Mechanisms for dynamic obstacle detection and avoidance
- Validating navigation logic within simulated scenarios
Leveraging Gazebo for Simulation
- Establishing Gazebo simulation environments integrated with ROS 2
- Testing robot models and navigation stacks for reliability
- Evaluating system performance in virtual settings
Real-World Deployment of SLAM and Navigation
- Establishing connections between ROS 2 and physical hardware
- Calibrating sensors and actuators for accuracy
- Executing real-time navigation experiments
Diagnosing Issues and Optimizing Performance
- Debugging common navigation challenges in ROS 2
- Refining SLAM algorithms for improved efficiency
- Adjusting navigation parameters for optimal results
Conclusion and Future Directions
Requirements
- Familiarity with fundamental robotics principles
- Proficiency in working with Linux-based operating systems
- Foundational knowledge of programming in Python or C++
Intended Audience
- Robotics engineers
- Automation software developers
- Professionals engaged in research and development of 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.