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 Duration 21 hours

Course Outline

ROS 2 Foundations and Autonomous Navigation

  • Examining the architectural components and capabilities of ROS 2
  • Analyzing navigation systems within the context of robotics
  • Establishing a functional ROS 2 development environment

Sensor Integration and Data Handling

  • Incorporating LiDAR and camera sensor data
  • Strategies for acquiring and processing sensor inputs
  • Visualizing sensor data streams using Rviz

Core Concepts of Mapping and Localization

  • Understanding the underlying principles of SLAM
  • Executing 2D and 3D mapping techniques
  • Achieving localization through AMCL and alternative methods

Path Planning and Obstacle Management

  • Evaluating various path planning algorithms
  • Implementing dynamic obstacle detection and avoidance strategies
  • Validating navigation logic in simulated scenarios

Leveraging Gazebo for Simulation

  • Configuring Gazebo simulations linked with ROS 2
  • Assessing robot models and navigation stacks in a virtual setting
  • Evaluating system performance within virtual environments

Real-World Deployment of SLAM and Navigation

  • Establishing connectivity between ROS 2 and physical hardware
  • Performing calibration of sensors and actuators
  • Conducting real-time navigation experiments

Optimization and Troubleshooting

  • Diagnosing and resolving navigation challenges in ROS 2
  • Enhancing the efficiency of SLAM algorithms
  • Adjusting navigation parameters for optimal performance

Conclusion and Future Directions

Requirements

  • A solid grasp of fundamental robotics principles
  • Practical experience working with Linux-based operating systems
  • Foundational proficiency in Python or C++ programming

Target Audience

  • Robotics engineers
  • Automation developers
  • R&D specialists focused on autonomous systems

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