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 Duration 21 hours

Course Outline

Introduction to ROS 2 and the Principles of Autonomous Navigation

  • An overview of ROS 2 architecture and its functional capabilities
  • An analysis of navigation systems within the field of robotics
  • Steps for establishing the ROS 2 development environment

Managing Sensors and Data Acquisition

  • The process of integrating LiDAR and camera sensors
  • Methods for collecting and processing incoming sensor data
  • Techniques for visualizing sensor outputs via Rviz

Core Concepts of Mapping and Localization

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

Path Planning Strategies and Obstacle Avoidance

  • Investigation of various path planning algorithms
  • Strategies for dynamic obstacle detection and evasion
  • Evaluating navigation capabilities in simulated environments

Leveraging Gazebo for Simulation

  • Configuration of Gazebo simulations in conjunction with ROS 2
  • Validation of robot models and navigation stacks
  • Performance analysis within virtual settings

Implementation of SLAM and Navigation on Physical Robots

  • Establishing connections between ROS 2 and physical hardware
  • Calibration of sensors and actuators
  • Conducting real-time navigation experiments

Debugging and Performance Enhancement

  • Identifying and resolving navigation issues within ROS 2
  • Optimizing SLAM algorithms for improved efficiency
  • Refining navigation parameters for optimal results

Conclusion and Recommended Next Steps

Requirements

  • Familiarity with core robotics principles
  • Proficiency in operating Linux-based systems
  • Foundational programming knowledge in either Python or C++

Target Audience

  • Robotics engineers
  • Automation developers
  • Professionals engaged in research and development of autonomous systems

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