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Duration 21 hours
Course Outline
Fundamentals of ROS 2 and Autonomous Navigation
- Review of ROS 2 architecture and its core capabilities
- Understanding the mechanics of robotic navigation systems
- Installation and setup of the ROS 2 development environment
Managing Sensors and Data Acquisition
- Integration of LiDAR and camera sensors into the system
- Techniques for collecting and processing sensor data streams
- Visualization of sensor outputs using Rviz
Core Concepts in Mapping and Localization
- Theoretical foundations of SLAM
- Implementation strategies for 2D and 3D mapping
- Localization techniques utilizing AMCL and alternative methods
Path Planning and Obstacle Mitigation
- In-depth exploration of path planning algorithms
- Strategies for dynamic obstacle detection and avoidance
- Validation of navigation logic in simulated environments
Leveraging Gazebo for Simulation
- Configuration of Gazebo simulations integrated with ROS 2
- Testing robot models and associated navigation stacks
- Performance analysis within virtual testbeds
Deploying SLAM and Navigation on Physical Robots
- Establishing connections between ROS 2 and physical hardware
- Calibration of sensors and actuators for precision
- Execution of real-time navigation experiments
Debugging and Performance Enhancement
- Resolving navigation-related issues within the ROS 2 framework
- Optimizing SLAM algorithms for computational efficiency
- Precision tuning of navigation parameters
Conclusion and Future Directions
Requirements
- A solid grasp of fundamental robotics principles
- Proficiency with Linux-based operating systems
- Foundational programming skills in either 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.