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 Duration 21 hours

Course Outline

Fundamentals of ROS 2 and Autonomous Navigation

  • Examining the architecture and core capabilities of ROS 2
  • Comprehending navigation systems within robotics
  • Preparing the ROS 2 development environment

Sensor Integration and Data Handling

  • Connecting LiDAR and camera sensors
  • Gathering and processing sensor inputs
  • Displaying sensor data streams using Rviz

Basics of Mapping and Localization

  • Core principles of SLAM
  • Creating 2D and 3D maps
  • Achieving localization via AMCL and alternative methods

Path Planning and Obstacle Evasion

  • Reviewing various path planning algorithms
  • Implementing dynamic obstacle detection and avoidance strategies
  • Validating navigation behaviors in simulated settings

Simulating with Gazebo

  • Configuring Gazebo simulations alongside ROS 2
  • Evaluating robot models and navigation stacks
  • Assessing performance metrics in virtual scenarios

Implementing SLAM and Navigation on Physical Robots

  • Linking ROS 2 to physical hardware components
  • Calibrating sensors and actuators for precision
  • Conducting real-time navigation trials

Debugging and Performance Tuning

  • Resolving navigation issues within ROS 2
  • Enhancing the efficiency of SLAM algorithms
  • Refining navigation parameters for optimal performance

Conclusion and Future Directions

Requirements

  • Familiarity with fundamental robotics concepts
  • Proficiency in Linux-based operating systems
  • Foundational programming knowledge in Python or C++

Target Audience

  • Robotics engineers
  • Automation developers
  • R&D specialists working on autonomous systems

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