Teacher: System AdministratorTeacher: SYAMSUL NIZAM BIN AZMEE

This course offers an in-depth introduction to the Robot Operating System (ROS), covering its architecture, communication methods, and key tools for simulation and visualisation. Through interactive lectures, hands-on labs, and project-based learning, participants will gain practical experience in developing, configuring, and managing ROS environments. By the end of the course, learners will be able to build modular and scalable robotic applications using both ROS 1 and ROS 2.

Teacher: System AdministratorTeacher: SYAMSUL NIZAM BIN AZMEE

This course offers an in-depth exploration of the Robot Operating System (ROS) and its role in developing autonomous systems. Participants will learn about autonomous vehicle architecture, sensor fusion, path planning, and SLAM, gaining hands-on experience with tools like ROS 2, Gazebo, and CARLA. Through practical simulations, case studies, and collaborative projects, learners will develop the skills to design, implement, and optimise autonomous navigation systems for real-world industrial and research applications.

Teacher: System AdministratorTeacher: SYAMSUL NIZAM BIN AZMEE

This course focuses on robot modelling and spatial awareness using ROS tools such as URDF and the TF Transformation System. Participants will learn to build, simulate, and visualise robot structures, manage coordinate frames, and integrate sensor data for real-time navigation and control. Through project-based learning and simulation-driven practice, learners gain hands-on experience in designing accurate robotic models and troubleshooting transformation issues for real-world automation applications.

Teacher: System AdministratorTeacher: SYAMSUL NIZAM BIN AZMEE

This course introduces the integration of Artificial Intelligence (AI) and Machine Learning (ML) with the Robot Operating System (ROS2) for intelligent robotics applications. Through hands-on modules, participants will learn navigation and object detection using TurtleBot3 and YOLO, explore the fundamentals of AI and ML, and apply algorithms such as k-NN, SVM, and k-means in robotic contexts. Using tools such as Gazebo, RViz2, Google Colab, and Python libraries, learners will gain practical experience in developing and deploying smart, data-driven robotic systems.

Teacher: System AdministratorTeacher: SYAMSUL NIZAM BIN AZMEE

This course introduces the Robot Management Framework (RMF) and its role in coordinating multi-robot systems across complex environments. Participants will learn to set up, configure, and optimise Open-RMF for efficient task scheduling, traffic management, and resource allocation. Through interactive lectures, simulations, and case studies, learners gain practical experience in managing robot fleets and applying RMF solutions to real-world settings such as warehouses, hospitals, and smart cities.