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Course Outline
Introduction to ROS 2 and Autonomous Navigation
- Overview of ROS 2 architecture and capabilities.
- Understanding navigation systems in robotics.
- Setting up the ROS 2 environment.
Working with Sensors and Data Acquisition
- Integrating LiDAR and camera sensors.
- Collecting and processing sensor data.
- Visualizing sensor outputs using Rviz.
Mapping and Localization Fundamentals
- Principles of SLAM.
- Implementing 2D and 3D mapping.
- Localization using AMCL and other techniques.
Path Planning and Obstacle Avoidance
- Exploring path planning algorithms.
- Dynamic obstacle detection and avoidance.
- Testing navigation in simulated environments.
Using Gazebo for Simulation
- Setting up Gazebo simulations with ROS 2.
- Testing robot models and navigation stacks.
- Analyzing performance in virtual environments.
Deploying SLAM and Navigation on Real Robots
- Connecting ROS 2 to physical hardware.
- Calibrating sensors and actuators.
- Running real-time navigation experiments.
Troubleshooting and Performance Optimization
- Debugging navigation issues in ROS 2.
- Optimizing SLAM algorithms for efficiency.
- Fine-tuning navigation parameters.
Summary and Next Steps
Requirements
- A solid understanding of robotics principles.
- Experience working with Linux-based systems.
- Basic proficiency in programming with Python or C++.
Target Audience
- Robotics engineers.
- Automation developers.
- Research and development professionals in autonomous systems.
21 Hours
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.
