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Duration 21 hours
Course Outline
Introduction to ROS 2 and the Principles of Autonomous Navigation
- An overview of ROS 2 architecture and its functional capabilities
- An analysis of navigation systems within the field of robotics
- Steps for establishing the ROS 2 development environment
Managing Sensors and Data Acquisition
- The process of integrating LiDAR and camera sensors
- Methods for collecting and processing incoming sensor data
- Techniques for visualizing sensor outputs via Rviz
Core Concepts of Mapping and Localization
- Theoretical principles underlying SLAM
- Implementation strategies for 2D and 3D mapping
- Localization methods utilizing AMCL and alternative techniques
Path Planning Strategies and Obstacle Avoidance
- Investigation of various path planning algorithms
- Strategies for dynamic obstacle detection and evasion
- Evaluating navigation capabilities in simulated environments
Leveraging Gazebo for Simulation
- Configuration of Gazebo simulations in conjunction with ROS 2
- Validation of robot models and navigation stacks
- Performance analysis within virtual settings
Implementation of SLAM and Navigation on Physical Robots
- Establishing connections between ROS 2 and physical hardware
- Calibration of sensors and actuators
- Conducting real-time navigation experiments
Debugging and Performance Enhancement
- Identifying and resolving navigation issues within ROS 2
- Optimizing SLAM algorithms for improved efficiency
- Refining navigation parameters for optimal results
Conclusion and Recommended Next Steps
Requirements
- Familiarity with core robotics principles
- Proficiency in operating Linux-based systems
- Foundational programming knowledge in either Python or C++
Target Audience
- Robotics engineers
- Automation 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.