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Duration 21 hours
Course Outline
Introduction to ROS 2 and Autonomous Navigation
- Overview of the ROS 2 architecture and its core capabilities
- Comprehending navigation systems within robotics
- Establishing the ROS 2 development environment
Working with Sensors and Data Acquisition
- Integration of LiDAR and camera sensors
- Methods for collecting and processing sensor data
- Visualization of sensor outputs using Rviz
Mapping and Localization Fundamentals
- Core principles of SLAM
- Implementation of 2D and 3D mapping techniques
- Localization methods using AMCL and alternative approaches
Path Planning and Obstacle Avoidance
- Exploration of various path planning algorithms
- Techniques for dynamic obstacle detection and avoidance
- Testing navigation strategies in simulated settings
Using Gazebo for Simulation
- Configuring Gazebo simulations compatible with ROS 2
- Testing robot models and navigation stacks
- Performance analysis within virtual environments
Deploying SLAM and Navigation on Real Robots
- Establishing connections between ROS 2 and physical hardware
- Calibration of sensors and actuators
- Execution of real-time navigation experiments
Troubleshooting and Performance Optimization
- Debugging common navigation issues in ROS 2
- Optimizing SLAM algorithms for improved efficiency
- Fine-tuning navigation parameters for optimal performance
Summary and Next Steps
Requirements
- A solid grasp of fundamental robotics principles
- Working experience with Linux-based operating systems
- Familiarity with Python or C++ programming
Target Audience
- Robotics engineers
- Automation developers
- R&D professionals 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.