Course Details in 2020/21 Session

Module Title	LM Intelligent Automation
School	School of Engineering
Department	Mechanical Engineering
Module Code	04 33354
Module Lead	Dr Marco Castellani
Level	Masters Level
Credits	20
Semester	Semester 2
Pre-requisites	LH Engineering Mathematics 3 - (04 23779)
Co-requisites
Restrictions	None
Contact Hours	Lecture-32 hours Seminar-2 hours Practical Classes and workshops-6 hours Guided independent study-160 hours Total: 200 hours
Exclusions
Description	The module is divided into two parts. In the first part, biologically-inspired computational intelligence methods will be analysed, and their applications to engineering and automation will be shown. This part will introduce the basic concepts of approximate reasoning, collective and decentralised intelligence, and present a number of cutting edge nature-inspired Swarm Intelligence paradigms, as well as well-established techniques such as Evolutionary Algorithms, Simulated Annealing, Neural Networks, and Fuzzy Logic. In the second part, the fundamental concepts of robotics will be presented. The second part of the module covers robot kinematics, dynamics, and control. It includes two hours of seminars where real robotics applications will be discussed by Mechanical Engineering members and invited speakers. The use of computational intelligence methods to solve robotics problems will be shown.
Learning Outcomes	By the end of the module students should be able to: Demonstrate a comprehensive understanding and knowledge of concepts from a range of artificial intelligence methods for solving complex engineering problems and the ability to apply them effectively in engineering projects. Apply computer-based models from a range of biologically-inspired computational intelligence methods for solving problems in engineering, and critically assess the limitations of particular cases. Generate an innovative design for products, systems, components or processes based on biologically-inspired computational intelligence methods, to fulfil new needs. Demonstrate a thorough understanding of current practice and its limitations in relation to biologically-inspired computational intelligence methods and some appreciation of likely new developments. Formulate, apply and critically analyse kinematic models of a dynamic robot, such as a manipulator. Demonstrate a comprehensive knowledge and understanding of robot dynamics. Demonstrate a wide understanding and knowledge of control methods for robotics manipulators. Demonstrate a comprehensive understanding and knowledge of the field of robotics and its applications.
Assessment	33354-01 : Module Mark : Mixed (100%)
Assessment Methods & Exceptions	Assessments: (50%) Canvas based summative assessment (50%) 2-hour closed book centrally timetabled exam in May/June assessment period (replaced by online assessment if closed book exam not possible). Supplementary/Reassessment Reassessment to match the main assessment method with due consideration made to any restrictions imposed at the time of reassessment. Students can carry forward passed assessment components from main assessment. Reassessment will normally only be allowed for students on the following programmes: 9757 MSc Advanced Mechanical Engineering, 9758 PG Diploma Advanced Mechanical Engineering, 9759 PG Certificate Advanced Mechanical Engineering, 222A MSc Advanced Mechanical Engineering (PT)
Other
Reading List