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Duration 21 hours
Course Outline
Introduction to ROS 2 and Autonomous Navigation
- Overview of ROS 2 architecture and core capabilities
- Understanding navigation systems within robotics
- Setting up the ROS 2 development environment
Working with Sensors and Data Acquisition
- Integrating LiDAR and camera sensors
- Collecting and processing sensor data streams
- Visualizing sensor outputs using Rviz
Mapping and Localization Fundamentals
- Core principles of SLAM
- Implementing 2D and 3D mapping strategies
- Localization techniques using AMCL and other methods
Path Planning and Obstacle Avoidance
- Exploring various path planning algorithms
- Dynamic obstacle detection and avoidance mechanisms
- Testing navigation capabilities in simulated environments
Using Gazebo for Simulation
- Configuring Gazebo simulations with ROS 2
- Testing robot models and navigation stacks
- Analyzing performance metrics in virtual environments
Deploying SLAM and Navigation on Real Robots
- Connecting ROS 2 to physical hardware interfaces
- Calibrating sensors and actuators
- Executing real-time navigation experiments
Troubleshooting and Performance Optimization
- Debugging common navigation issues in ROS 2
- Optimizing SLAM algorithms for enhanced efficiency
- Fine-tuning navigation parameters
Summary and Next Steps
Requirements
- A solid grasp of fundamental robotics principles
- Familiarity with Linux-based operating systems
- Foundational programming skills in Python or C++
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.