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 Duration 21 hours

Course Outline

Fundamentals of ROS 2 and Autonomous Navigation

  • Examining the ROS 2 architecture and its core capabilities
  • Gaining insight into navigation systems within robotics
  • Establishing a functional ROS 2 development environment

Managing Sensors and Data Acquisition

  • Incorporating LiDAR and camera sensor inputs
  • Techniques for collecting and processing sensor data streams
  • Visualizing sensor output effectively using Rviz

Core Concepts of Mapping and Localization

  • Exploring the foundational principles of SLAM
  • Executing 2D and 3D mapping strategies
  • Achieving localization through AMCL and alternative methods

Path Planning and Obstacle Avoidance Strategies

  • Evaluating various path planning algorithms
  • Implementing dynamic obstacle detection and evasion
  • Validating navigation logic within simulated settings

Leveraging Gazebo for Simulation

  • Configuring Gazebo simulations linked to ROS 2
  • Evaluating robot models and navigation stack performance
  • Analyzing operational metrics in virtual environments

Implementing SLAM and Navigation on Physical Robots

  • Connecting ROS 2 interfaces to physical hardware
  • Calibrating sensors and actuators for accuracy
  • Conducting real-time navigation experiments

Diagnostic and Performance Enhancement

  • Resolving navigation issues within the ROS 2 framework
  • Refining SLAM algorithms to improve efficiency
  • Adjusting navigation parameters for optimal results

Conclusion and Future Directions

Requirements

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

Target Audience

  • Robotics engineers
  • Automation software developers
  • Professionals in research and development specializing in autonomous systems

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