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Duration 21 hours
Course Outline
ROS 2 Foundations and Autonomous Navigation
- Examining the architectural components and capabilities of ROS 2
- Analyzing navigation systems within the context of robotics
- Establishing a functional ROS 2 development environment
Sensor Integration and Data Handling
- Incorporating LiDAR and camera sensor data
- Strategies for acquiring and processing sensor inputs
- Visualizing sensor data streams using Rviz
Core Concepts of Mapping and Localization
- Understanding the underlying principles of SLAM
- Executing 2D and 3D mapping techniques
- Achieving localization through AMCL and alternative methods
Path Planning and Obstacle Management
- Evaluating various path planning algorithms
- Implementing dynamic obstacle detection and avoidance strategies
- Validating navigation logic in simulated scenarios
Leveraging Gazebo for Simulation
- Configuring Gazebo simulations linked with ROS 2
- Assessing robot models and navigation stacks in a virtual setting
- Evaluating system performance within virtual environments
Real-World Deployment of SLAM and Navigation
- Establishing connectivity between ROS 2 and physical hardware
- Performing calibration of sensors and actuators
- Conducting real-time navigation experiments
Optimization and Troubleshooting
- Diagnosing and resolving navigation challenges in ROS 2
- Enhancing the efficiency of SLAM algorithms
- Adjusting navigation parameters for optimal performance
Conclusion and Future Directions
Requirements
- A solid grasp of fundamental robotics principles
- Practical experience working with Linux-based operating systems
- Foundational proficiency in Python or C++ programming
Target Audience
- Robotics engineers
- Automation developers
- R&D specialists 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.