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Duration 21 hours
Course Outline
Fundamentals of ROS 2 and Autonomous Navigation
- Examining the ROS 2 architecture and its core capabilities
- Gaining insight into navigation systems within robotics
- Establishing a functional ROS 2 development environment
Managing Sensors and Data Acquisition
- Incorporating LiDAR and camera sensor inputs
- Techniques for collecting and processing sensor data streams
- Visualizing sensor output effectively using Rviz
Core Concepts of Mapping and Localization
- Exploring the foundational principles of SLAM
- Executing 2D and 3D mapping strategies
- Achieving localization through AMCL and alternative methods
Path Planning and Obstacle Avoidance Strategies
- Evaluating various path planning algorithms
- Implementing dynamic obstacle detection and evasion
- Validating navigation logic within simulated settings
Leveraging Gazebo for Simulation
- Configuring Gazebo simulations linked to ROS 2
- Evaluating robot models and navigation stack performance
- Analyzing operational metrics in virtual environments
Implementing SLAM and Navigation on Physical Robots
- Connecting ROS 2 interfaces to physical hardware
- Calibrating sensors and actuators for accuracy
- Conducting real-time navigation experiments
Diagnostic and Performance Enhancement
- Resolving navigation issues within the ROS 2 framework
- Refining SLAM algorithms to improve efficiency
- Adjusting navigation parameters for optimal results
Conclusion and Future Directions
Requirements
- A solid grasp of fundamental robotics principles
- Proficiency with Linux-based operating systems
- Familiarity with programming in Python or C++
Target Audience
- Robotics engineers
- Automation software developers
- Professionals in research and development 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.