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 Duration 21 hours

Course Outline

Introduction to ROS 2 and Autonomous Navigation

  • Examination of ROS 2 architectural components and core capabilities
  • An analysis of navigation system dynamics within robotics
  • Establishing the foundational ROS 2 development environment

Sensor Integration and Data Processing

  • Incorporation of LiDAR and camera sensor systems
  • Strategies for efficient data collection and signal processing
  • Visualization of sensor data streams using Rviz

Foundations of Mapping and Localization

  • Core principles underlying SLAM technology
  • Execution of 2D and 3D mapping techniques
  • Localization methods utilizing AMCL and complementary approaches

Path Planning and Obstacle Management

  • Review of advanced path planning algorithms
  • Techniques for dynamic obstacle detection and evasion
  • Validation of navigation logic within simulated contexts

Leveraging Gazebo for Simulation

  • Initialization of Gazebo simulations linked with ROS 2
  • Verification of robot models and navigation stacks
  • Performance analysis within virtual testing environments

Implementation of SLAM and Navigation on Physical Robots

  • Establishing communication between ROS 2 and physical hardware
  • Calibration processes for sensors and actuators
  • Execution of real-time navigation trials

Diagnosis and Performance Tuning

  • Debugging navigation anomalies within the ROS 2 framework
  • Refining SLAM algorithms for optimal efficiency
  • Adjusting navigation parameters for precision

Conclusion and Future Directions

Requirements

  • A solid grasp of fundamental robotics concepts
  • Proficiency with Linux-based operating systems
  • Familiarity with Python or C++ programming fundamentals

Target Audience

  • Robotics engineers
  • Automation developers
  • Professionals in R&D specializing in autonomous systems

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