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 Duration 21 hours

Course Outline

Foundations of ROS 2 and Autonomous Navigation

  • Examining the architecture and core capabilities of ROS 2
  • Gaining insight into navigation systems within robotics
  • Initializing the ROS 2 development environment

Sensor Integration and Data Acquisition

  • Incorporating LiDAR and camera sensor data
  • Techniques for collecting and processing sensor inputs
  • Visualizing sensor outputs through Rviz

Core Concepts in Mapping and Localization

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

Path Planning and Obstacle Management

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

Simulating with Gazebo

  • Configuring Gazebo simulations integrated with ROS 2
  • Assessing robot models and navigation stacks
  • Reviewing performance metrics in virtual environments

Real-World Deployment of SLAM and Navigation

  • Linking ROS 2 with physical hardware components
  • Calibrating sensors and actuators for precision
  • Executing real-time navigation experiments

Debugging and Performance Enhancement

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

Conclusion and Future Directions

Requirements

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

Target Audience

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

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