Get in Touch
 Duration 21 hours

Course Outline

Introduction to ROS 2 and Autonomous Navigation

  • Exploring the architecture and core capabilities of ROS 2.
  • Gaining insight into navigation systems within robotics.
  • Establishing a functional ROS 2 development environment.

Managing Sensors and Data Acquisition

  • Connecting LiDAR and camera sensors to the system.
  • Methods for collecting and processing sensor data streams.
  • Utilizing Rviz to visualize sensor outputs.

Foundations of Mapping and Localization

  • Understanding the underlying principles of SLAM.
  • Developing 2D and 3D mapping capabilities.
  • Applying AMCL and other localization techniques.

Path Planning and Obstacle Avoidance Strategies

  • Evaluating various path planning algorithms.
  • Implementing dynamic obstacle detection and avoidance mechanisms.
  • Validating navigation capabilities in simulated scenarios.

Leveraging Gazebo for Simulation

  • Configuring Gazebo simulations compatible with ROS 2.
  • Testing robot models and integrated navigation stacks.
  • Assessing performance metrics in virtual settings.

Implementing SLAM and Navigation on Physical Robots

  • Establishing connections between ROS 2 and physical hardware.
  • Performing calibration of sensors and actuators.
  • Executing real-time navigation experiments.

Optimizing Performance and Troubleshooting

  • Resolving common navigation issues within the ROS 2 stack.
  • Enhancing the efficiency of SLAM algorithms.
  • Refining navigation parameters for optimal results.

Conclusion and Future Directions

Requirements

  • A solid grasp of fundamental robotics principles.
  • Hands-on experience with Linux-based operating systems.
  • Familiarity with programming in Python or C++.

Target Audience

  • Robotics engineers.
  • Automation developers.
  • Professionals in research and development focusing on autonomous systems.

Number of participants


Price per participant

Testimonials (1)

Upcoming Courses

Related Categories