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Course Outline

Foundations of ROS 2 and Autonomous Navigation

  • An overview of ROS 2 architecture and its core capabilities.
  • A deeper understanding of navigation systems within robotics.
  • Guidance on establishing a functional ROS 2 development environment.

Sensor Integration and Data Processing

  • Techniques for integrating LiDAR and camera sensors.
  • Methods for effective data collection and sensor processing.
  • Visualizing sensor data outputs using Rviz.

Core Concepts in Mapping and Localization

  • Theoretical principles underlying SLAM technology.
  • Implementation strategies for 2D and 3D mapping.
  • Localization methods utilizing AMCL and alternative techniques.

Path Planning and Obstacle Management

  • Exploration of various path planning algorithms.
  • Strategies for dynamic obstacle detection and avoidance.
  • Evaluation of navigation performance in simulated scenarios.

Leveraging Gazebo for Simulation

  • Configuration of Gazebo simulations integrated with ROS 2.
  • Validation of robot models and navigation stack functionality.
  • Performance analysis within virtual testing environments.

Implementation on Physical Robotic Platforms

  • Procedures for connecting ROS 2 to physical hardware.
  • Calibration processes for sensors and actuators.
  • Execution of real-time navigation experiments.

Optimization and Troubleshooting

  • Diagnostic approaches for debugging navigation issues in ROS 2.
  • Strategies for optimizing SLAM algorithms to enhance efficiency.
  • Refining navigation parameters for peak performance.

Course Summary and Future Directions

Requirements

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

Target Audience

  • Robotics engineers seeking to expand their skill set.
  • Developers specializing in automation technologies.
  • Professionals in R&D roles focused on autonomous systems.
 21 Hours

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