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 Duration 21 hours

Course Outline

Fundamentals of ROS 2 and Autonomous Navigation

  • Examining the architecture and capabilities of ROS 2.
  • Gaining insight into navigation systems within robotics.
  • Configuring the initial ROS 2 development environment.

Sensor Integration and Data Acquisition

  • Connecting LiDAR and camera sensors to the system.
  • Techniques for collecting and processing incoming sensor data.
  • Visualizing sensor outputs through the use of Rviz.

Core Concepts of Mapping and Localization

  • Understanding the underlying principles of SLAM.
  • Implementing both 2D and 3D mapping strategies.
  • Achieving localization through AMCL and alternative techniques.

Strategic Path Planning and Obstacle Avoidance

  • Reviewing various path planning algorithms.
  • Managing dynamic obstacle detection and avoidance mechanisms.
  • Evaluating navigation performance within simulated environments.

Leveraging Gazebo for Simulation

  • Configuring Gazebo simulations in conjunction with ROS 2.
  • Testing robot models and associated navigation stacks.
  • Analyzing system performance in virtual settings.

Real-World Deployment of SLAM and Navigation

  • Linking ROS 2 interfaces to physical hardware components.
  • Calibrating sensors and actuators for optimal performance.
  • Conducting real-time navigation experiments.

Debugging and Performance Enhancement

  • Identifying and resolving navigation issues within ROS 2.
  • Refining SLAM algorithms for greater efficiency.
  • Adjusting navigation parameters for precision.

Recap and Future Directions

Requirements

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

Target Audience

  • Robotics engineers.
  • Automation developers.
  • R&D specialists working in autonomous systems.

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