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 Duration 21 hours

Course Outline

Introduction to ROS 2 and Autonomous Navigation

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

Working with Sensors and Data Acquisition

  • Integrating LiDAR and camera sensors
  • Collecting and processing sensor data streams
  • Visualizing sensor outputs using Rviz

Mapping and Localization Fundamentals

  • Core principles of SLAM
  • Implementing 2D and 3D mapping strategies
  • Localization techniques using AMCL and other methods

Path Planning and Obstacle Avoidance

  • Exploring various path planning algorithms
  • Dynamic obstacle detection and avoidance mechanisms
  • Testing navigation capabilities in simulated environments

Using Gazebo for Simulation

  • Configuring Gazebo simulations with ROS 2
  • Testing robot models and navigation stacks
  • Analyzing performance metrics in virtual environments

Deploying SLAM and Navigation on Real Robots

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

Troubleshooting and Performance Optimization

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

Summary and Next Steps

Requirements

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

Target Audience

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

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