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 Duration 21 hours

Course Outline

Introduction to ROS 2 and Autonomous Navigation

  • Examining the architecture and core capabilities of ROS 2
  • Understanding the structure of navigation systems in robotics
  • Establishing the foundational ROS 2 development environment

Managing Sensors and Data Acquisition

  • Incorporating LiDAR and camera sensor data
  • Acquiring and processing raw sensor information
  • Utilizing Rviz to visualize sensor outputs

Core Concepts of Mapping and Localization

  • Exploring the underlying principles of SLAM
  • Implementing both 2D and 3D mapping techniques
  • Achieving localization via AMCL and alternative methods

Path Planning and Obstacle Avoidance

  • Reviewing various path planning algorithms
  • Detecting and avoiding dynamic obstacles
  • Evaluating navigation performance in simulated settings

Leveraging Gazebo for Simulation

  • Configuring Gazebo simulations integrated with ROS 2
  • Validating robot models and navigation stacks
  • Analyzing system performance within virtual environments

Implementing SLAM and Navigation on Physical Robots

  • Establishing the connection between ROS 2 and physical hardware
  • Calibrating sensors and actuators for accuracy
  • Executing real-time navigation experiments

Debugging and Performance Enhancement

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

Summary and Future Directions

Requirements

  • A solid grasp of fundamental robotics principles
  • Practical experience with Linux-based operating systems
  • Familiarity with programming in Python or C++

Intended Audience

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

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