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 Duration 21 hours

Course Outline

Introduction to ROS 2 and Autonomous Navigation

  • Overview of the ROS 2 architecture and its core capabilities
  • Comprehending navigation systems within robotics
  • Establishing the ROS 2 development environment

Working with Sensors and Data Acquisition

  • Integration of LiDAR and camera sensors
  • Methods for collecting and processing sensor data
  • Visualization of sensor outputs using Rviz

Mapping and Localization Fundamentals

  • Core principles of SLAM
  • Implementation of 2D and 3D mapping techniques
  • Localization methods using AMCL and alternative approaches

Path Planning and Obstacle Avoidance

  • Exploration of various path planning algorithms
  • Techniques for dynamic obstacle detection and avoidance
  • Testing navigation strategies in simulated settings

Using Gazebo for Simulation

  • Configuring Gazebo simulations compatible with ROS 2
  • Testing robot models and navigation stacks
  • Performance analysis within virtual environments

Deploying SLAM and Navigation on Real Robots

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

Troubleshooting and Performance Optimization

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

Summary and Next Steps

Requirements

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

Target Audience

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

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Provisional Upcoming Courses (Require 5+ participants)

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