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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
  • Preparing the ROS 2 development environment

Managing Sensors and Data Acquisition

  • Connecting LiDAR and camera hardware
  • Acquiring and refining sensor data streams
  • Using Rviz to visualize sensor output

Core Concepts in Mapping and Localization

  • Exploring the principles behind SLAM
  • Building 2D and 3D mapping solutions
  • Achieving localization via AMCL and alternative methods

Path Planning and Safety Protocols

  • Reviewing advanced path planning algorithms
  • Implementing dynamic obstacle detection and avoidance
  • Validating navigation logic in simulated settings

Leveraging Gazebo for Simulation

  • Establishing Gazebo simulations integrated with ROS 2
  • Testing robot models and navigation stacks
  • Evaluating performance metrics in virtual environments

Deploying SLAM and Navigation on Physical Robots

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

Debugging and Performance Tuning

  • Resolving common navigation issues in ROS 2
  • Enhancing SLAM algorithm efficiency
  • Refining navigation parameters for optimal performance

Conclusion and Future Directions

Requirements

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

Target Audience

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

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