Robotics Learning Lab

This lab is a conducive environment for experiential learning of robotics. Students may access various types of workstations, equipment and computers. During hands-on activities or robotics competitions, students can also place their robot in the designated areas.

Address: Block N3.2-B1-03
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This course introduces the fundamentals of Artificial Intelligence (AI) and Machine Learning (ML), focusing on their real-world engineering applications. Students will explore AI/ML integration with autonomous vehicles and robotics through hands-on group projects, using various data sources and open-source software without requiring complex coding. Key applications include a chatbot, an object recognition tool, and the preparation of a mobile robot for autonomous navigation.

Teaching faculty: Dr Lum Guo Zhan (Course Coordinator), Dr Sim Siang Kok, Mr David Tee, Dr Wong Choon Yue

Students can experience real world mechatronics engineering by undertaking a mechatronics design project group assignment that will develop your engineering creativity, inventiveness and to develop a deeper insight to great Mechatronics System design. Students will gain relevant employable skills in mechatronics design engineering, execute systematic engineering design process (including product specification, conceptual design, embodiment design, detailed design, software programming, control, and hardware, sensors, logic and system performance optimization). Besides engineering knowledge, the students learn soft skills like leadership, importance of effective team communications, team management, resource and expertise planning, contingency management, creative problem solving and more.

Teaching faculty: Dr Heng Kok Hui, John Gerard

In this lab-based project, students explore motion control using a ball-screw system, gaining practical experience in robotics actuation and feedback control. Students will model a closed-loop system comprising key components in robotic platforms, simulate using MATLAB Simulink and fine-tune PID parameters, then apply them to the physical setup. Students will assess system performance, gather experimental data, and analyze the control strategy effectiveness. This direct transition from simulation to real hardware provides a valuable hands-on learning experience, not only reinforcing foundational control theory but offering unique insights, from modelling and simulation to actual hardware implementation.

Teaching faculty: Dr Ahmad Khairyanto Bin Ratmin