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Duration 21 hours
Course Outline
Fundamentals of ROS 2 and Autonomous Navigation
- Examining the architecture and capabilities of ROS 2.
- Gaining insight into navigation systems within robotics.
- Configuring the initial ROS 2 development environment.
Sensor Integration and Data Acquisition
- Connecting LiDAR and camera sensors to the system.
- Techniques for collecting and processing incoming sensor data.
- Visualizing sensor outputs through the use of Rviz.
Core Concepts of Mapping and Localization
- Understanding the underlying principles of SLAM.
- Implementing both 2D and 3D mapping strategies.
- Achieving localization through AMCL and alternative techniques.
Strategic Path Planning and Obstacle Avoidance
- Reviewing various path planning algorithms.
- Managing dynamic obstacle detection and avoidance mechanisms.
- Evaluating navigation performance within simulated environments.
Leveraging Gazebo for Simulation
- Configuring Gazebo simulations in conjunction with ROS 2.
- Testing robot models and associated navigation stacks.
- Analyzing system performance in virtual settings.
Real-World Deployment of SLAM and Navigation
- Linking ROS 2 interfaces to physical hardware components.
- Calibrating sensors and actuators for optimal performance.
- Conducting real-time navigation experiments.
Debugging and Performance Enhancement
- Identifying and resolving navigation issues within ROS 2.
- Refining SLAM algorithms for greater efficiency.
- Adjusting navigation parameters for precision.
Recap and Future Directions
Requirements
- A solid grasp of fundamental robotics principles.
- Proficiency with Linux-based operating systems.
- Familiarity with basic programming concepts in Python or C++.
Target Audience
- Robotics engineers.
- Automation developers.
- R&D specialists working 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.