Programme And Module Handbook
Course Details in 2022/23 Session

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Module Title LM Intelligent Automation
SchoolSchool of Engineering
Department Mechanical Engineering
Module Code 04 33354
Module Lead Dr Marco Castellani
Level Masters Level
Credits 20
Semester Semester 2
Pre-requisites Engineering Mathematics 3 - (04 23779)
Restrictions None
Contact Hours Lecture-32 hours
Seminar-2 hours
Practical Classes and workshops-6 hours
Guided independent study-160 hours
Total: 200 hours
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 In-semester assessment: 20%
Exam: 80%
Reading List