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 Duration 21 hours

Course Outline

Introduction to ROS 2 and Autonomous Navigation

  • Overview of ROS 2 architecture and capabilities.
  • Understanding navigation systems within robotics.
  • Setting up the ROS 2 development environment.

Working with Sensors and Data Acquisition

  • Integration of LiDAR and camera sensors.
  • Collection and processing of sensor data.
  • Visualization of sensor outputs using Rviz.

Mapping and Localization Fundamentals

  • Core principles of SLAM.
  • Implementation of 2D and 3D mapping techniques.
  • Localization methods utilizing AMCL and other approaches.

Path Planning and Obstacle Avoidance

  • Exploration of path planning algorithms.
  • Dynamic obstacle detection and avoidance strategies.
  • Testing navigation capabilities in simulated environments.

Utilizing Gazebo for Simulation

  • Configuration of Gazebo simulations with ROS 2.
  • Testing robot models and navigation stacks.
  • Performance analysis in virtual settings.

Deploying SLAM and Navigation on Real Robots

  • Connecting ROS 2 to physical hardware.
  • Calibration of sensors and actuators.
  • Execution of real-time navigation experiments.

Troubleshooting and Performance Optimization

  • Debugging navigation issues within ROS 2.
  • Optimizing SLAM algorithms for enhanced efficiency.
  • Fine-tuning navigation parameters.

Summary and Next Steps

Requirements

  • A solid grasp of robotics principles.
  • Proficiency in Linux-based operating systems.
  • Foundational knowledge of Python or C++ programming.

Target Audience

  • Robotics engineers.
  • Automation developers.
  • R&D professionals specializing in autonomous systems.

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