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 Duration 21 hours

Course Outline

Foundations of ROS 2 and Autonomous Navigation

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

Managing Sensors and Data Collection

  • Integrating LiDAR and camera technologies
  • Gathering and processing incoming sensor data
  • Displaying sensor outputs through Rviz

Core Concepts in Mapping and Localization

  • Understanding the underlying principles of SLAM
  • Executing 2D and 3D mapping procedures
  • Achieving localization via AMCL and alternative methods

Path Planning and Obstacle Avoidance

  • Investigating various path planning algorithms
  • Detecting and avoiding dynamic obstacles
  • Validating navigation strategies in simulated settings

Leveraging Gazebo for Simulation

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

Implementing SLAM and Navigation on Physical Robots

  • Linking ROS 2 to physical hardware components
  • Calibrating sensors and actuator systems
  • Conducting real-time navigation experiments

Debugging and Performance Enhancement

  • Resolving navigation issues within the ROS 2 framework
  • Optimizing SLAM algorithms for improved efficiency
  • Refining navigation parameters for optimal results

Conclusion and Future Directions

Requirements

  • A solid grasp of core robotics principles.
  • Proficiency with Linux-based operating systems.
  • Fundamental programming skills in either Python or C++.

Target Audience

  • Robotics engineers
  • Automation developers
  • Professionals focused on research and development of autonomous systems

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