This module aims to provide students with an in-depth exploration of robotics and its integration with Artificial Intelligence (AI), focusing on robotic related concepts and topics such as navigation, localization, SLAM, the Robot Operating System (ROS) and AI's role in enhancing robotic capabilities.
A Demonstrate understanding of the fundamental concepts and principles of robotics and explain how AI can be leveraged to enhance robotic performance. B Critically analyze real-world problems and formalize them as robotic tasks, designing and developing robotic solutions to effectively address and solve these challenges. C Demonstrate proficiency in Robotic Operation System by navigating its architecture, integrating tools, and deploying robotic systems. D Apply robotic algorithms and simulation techniques, demonstrating proficiency in testing, optimizing, and refining these algorithms in real-world scenarios.
The teaching philosophy of the module follows very much the philosophy of Syntegrative Education. This philosophy is carried through in terms of assessment, with reduction on the use of exams and increase in coursework, especially problem-based assessments that are project focused. This module will be delivered through a combination of lectures, group discussions, case studies, and hands-on practical exercises etc. Lectures and group discussions are conducted using the Problem Based Learning paradigm focusing on student-centered learning, where they develop critical thinking and problem-solving skills to address open-ended problems that lacks a straightforward solution. This module is taught with an emphasis on student learning through practice and by projects, facilitated by a module leader, and where appropriate, industrial mentors. Students can identify particular areas of learning needs or interests according to the available project(s). They will conduct independent research to gather information and resources to better define the problem. Progress towards the learning outcomes will be facilitated and monitored, where students are guided to progressively address the given problem through tasks. Independent learning will form an important aspect of the educational activities in this module. Case studies will be used to provide students with real-world examples of how the concepts and techniques covered in this module can be applied. Lab/Practical sessions will allow students to apply the techniques and tools acquired to solve real-world industry focused problems. This module will leverage generative AI to enhance course content and teaching methods in line with the learning outcomes. By integrating advanced AI technologies, we aim to improve the efficiency of teaching and interaction, while fostering greater student autonomy and flexibility in learning.