This module introduces learners to simulating robots using advanced tools such as Gazebo and Webots. You will explore the process of creating 3D robot models, configuring simulation files, and integrating simulations with ROS 2. By the end, you'll be able to compare simulator features and run simulations for both custom and existing robots.

This module introduces the ros2_control framework, guiding learners through its architecture, the process of adding and managing controllers, and hands-on interaction using command-line tools. Learners will also gain experience compiling and integrating custom controller plugins into a ROS 2 system.

This module introduces the fundamentals of Behavior Trees (BTs) and demonstrates how to implement them in ROS 2 applications using the BehaviorTree.CPP library. Learners will explore BT node structures, integrate BTs with ROS 2 nodes and actions, and utilize the blackboard for data sharing. By the end, participants will be able to coordinate complex robotic behaviors through modular and maintainable code.

This module introduces the ROS 2 Navigation Stack (Nav2), exploring its modular architecture, core components, and practical applications in autonomous robot navigation and mapping. Learners will gain hands-on experience with simulation demos, launch file configuration, and real-world case studies, including hospital delivery robots. By the end, you'll understand how to set up, customize, and deploy Nav2 for various robotics scenarios.

This module guides learners through configuring and using MoveIt 2 for advanced robotic arm manipulation. You will prepare robot models, set up motion planning in simulated environments, and implement obstacle detection using sensors. By the end, you'll be able to plan and execute complex robot motions while accounting for real-world constraints.

This module introduces the integration of ROS 2 with perception tools such as OpenCV and NVIDIA Isaac ROS, enabling robots to process and interpret sensory data. Learners will explore camera calibration, depth sensing, and GPU-accelerated image processing to enhance robotic perception capabilities. Practical exercises include working with fiducial markers for pose estimation and optimizing performance for real-world robotics applications.