Get in Touch
 Duration 21 hours

Course Outline

Foundations of ROS 2 and Autonomous Navigation

  • An examination of ROS 2 architecture and its core capabilities
  • Exploring the role of navigation systems in robotics
  • Configuring the initial ROS 2 development environment

Handling Sensors and Data Acquisition

  • Incorporating LiDAR and camera sensor data streams
  • Techniques for collecting and processing raw sensor inputs
  • Visualizing sensor outputs effectively using Rviz

Core Concepts in Mapping and Localization

  • Key principles underlying SLAM technology
  • Methods for implementing both 2D and 3D mapping
  • Achieving localization through AMCL and alternative techniques

Strategies for Path Planning and Obstacle Avoidance

  • Review of advanced path planning algorithms
  • Mechanisms for dynamic obstacle detection and avoidance
  • Validating navigation logic within simulated scenarios

Leveraging Gazebo for Simulation

  • Establishing Gazebo simulation environments integrated with ROS 2
  • Testing robot models and navigation stacks for reliability
  • Evaluating system performance in virtual settings

Real-World Deployment of SLAM and Navigation

  • Establishing connections between ROS 2 and physical hardware
  • Calibrating sensors and actuators for accuracy
  • Executing real-time navigation experiments

Diagnosing Issues and Optimizing Performance

  • Debugging common navigation challenges in ROS 2
  • Refining SLAM algorithms for improved efficiency
  • Adjusting navigation parameters for optimal results

Conclusion and Future Directions

Requirements

  • Familiarity with fundamental robotics principles
  • Proficiency in working with Linux-based operating systems
  • Foundational knowledge of programming in Python or C++

Intended Audience

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

Number of participants


Price per participant

Testimonials (1)

Upcoming Courses

Related Categories