Thank you for sending your enquiry! One of our team members will contact you shortly.
Thank you for sending your booking! One of our team members will contact you shortly.
Duration 21 hours
Course Outline
Introduction to ROS 2 and Autonomous Navigation
- Overview of ROS 2 architecture and capabilities.
- Understanding navigation systems within robotics.
- Setting up the ROS 2 development environment.
Working with Sensors and Data Acquisition
- Integration of LiDAR and camera sensors.
- Collection and processing of sensor data.
- Visualization of sensor outputs using Rviz.
Mapping and Localization Fundamentals
- Core principles of SLAM.
- Implementation of 2D and 3D mapping techniques.
- Localization methods utilizing AMCL and other approaches.
Path Planning and Obstacle Avoidance
- Exploration of path planning algorithms.
- Dynamic obstacle detection and avoidance strategies.
- Testing navigation capabilities in simulated environments.
Utilizing Gazebo for Simulation
- Configuration of Gazebo simulations with ROS 2.
- Testing robot models and navigation stacks.
- Performance analysis in virtual settings.
Deploying SLAM and Navigation on Real Robots
- Connecting ROS 2 to physical hardware.
- Calibration of sensors and actuators.
- Execution of real-time navigation experiments.
Troubleshooting and Performance Optimization
- Debugging navigation issues within ROS 2.
- Optimizing SLAM algorithms for enhanced efficiency.
- Fine-tuning navigation parameters.
Summary and Next Steps
Requirements
- A solid grasp of robotics principles.
- Proficiency in Linux-based operating systems.
- Foundational knowledge of Python or C++ programming.
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.