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Duration 21 hours
Course Outline
Introduction to ROS 2 and Autonomous Navigation
- Examining the architecture and core capabilities of ROS 2
- Understanding the structure of navigation systems in robotics
- Establishing the foundational ROS 2 development environment
Managing Sensors and Data Acquisition
- Incorporating LiDAR and camera sensor data
- Acquiring and processing raw sensor information
- Utilizing Rviz to visualize sensor outputs
Core Concepts of Mapping and Localization
- Exploring the underlying principles of SLAM
- Implementing both 2D and 3D mapping techniques
- Achieving localization via AMCL and alternative methods
Path Planning and Obstacle Avoidance
- Reviewing various path planning algorithms
- Detecting and avoiding dynamic obstacles
- Evaluating navigation performance in simulated settings
Leveraging Gazebo for Simulation
- Configuring Gazebo simulations integrated with ROS 2
- Validating robot models and navigation stacks
- Analyzing system performance within virtual environments
Implementing SLAM and Navigation on Physical Robots
- Establishing the connection between ROS 2 and physical hardware
- Calibrating sensors and actuators for accuracy
- Executing real-time navigation experiments
Debugging and Performance Enhancement
- Resolving navigation challenges within the ROS 2 stack
- Optimizing SLAM algorithms for improved efficiency
- Refining navigation parameters for optimal results
Summary and Future Directions
Requirements
- A solid grasp of fundamental robotics principles
- Practical experience with Linux-based operating systems
- Familiarity with programming in Python or C++
Intended Audience
- Robotics engineers
- Automation developers
- Professionals in R&D focused 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.