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 Duration 21 hours

Course Outline

Fundamentals of ROS 2 and Autonomous Navigation

  • Review of ROS 2 architecture and its core capabilities
  • Understanding the mechanics of robotic navigation systems
  • Installation and setup of the ROS 2 development environment

Managing Sensors and Data Acquisition

  • Integration of LiDAR and camera sensors into the system
  • Techniques for collecting and processing sensor data streams
  • Visualization of sensor outputs using Rviz

Core Concepts in Mapping and Localization

  • Theoretical foundations of SLAM
  • Implementation strategies for 2D and 3D mapping
  • Localization techniques utilizing AMCL and alternative methods

Path Planning and Obstacle Mitigation

  • In-depth exploration of path planning algorithms
  • Strategies for dynamic obstacle detection and avoidance
  • Validation of navigation logic in simulated environments

Leveraging Gazebo for Simulation

  • Configuration of Gazebo simulations integrated with ROS 2
  • Testing robot models and associated navigation stacks
  • Performance analysis within virtual testbeds

Deploying SLAM and Navigation on Physical Robots

  • Establishing connections between ROS 2 and physical hardware
  • Calibration of sensors and actuators for precision
  • Execution of real-time navigation experiments

Debugging and Performance Enhancement

  • Resolving navigation-related issues within the ROS 2 framework
  • Optimizing SLAM algorithms for computational efficiency
  • Precision tuning of navigation parameters

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
  • R&D professionals specializing in autonomous systems

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