MSc in Electrical Engineering

Course Information Package

Course Unit CodeAEEE552
Course Unit DetailsMSc Electrical Engineering (Technical Electives) -
Number of ECTS credits allocated7
Learning Outcomes of the course unitBy the end of the course, the students should be able to:
  1. Classify robot arm manipulators by coordinate system. Classify robot Arm manipulators by control method.
  2. Apply coordinate transformation techniques for rotation and transformation matrices. Assign coordinate frames, link and joint parameters to robot arm manipulators and interpret their kinematics in terms of DH representation and Jacobian transformation. Formulate robot arm manipulator dynamics in terms of the Lagrange-Euler approach.
  3. Identify the various types of robot driver in industrial robot arm manipulators.
  4. Identify the various type of sensors used in robot arm manipulators and judge the use of them according to the application.
  5. Acknowledge computer programming and machine vision techniques for industrial robot arm manipulators.
  6. Apply control engineering approaches and design PID controllers for the control of robot arm manipulators.
Mode of DeliveryFace-to-face
Recommended optional program componentsNONE
Course Contents

Robot Classification by coordinate system: Cylindrical coordinate robots, spherical coordinate robots, jointed arm robots, cartesian coordinate robots.

Classification by Control Method: Sequence control robots, playback robots, controlled path robots, adaptive robots, intelligent robots.

Kinematics and Dynamics: Coordinate transformations. Homogeneous transformation, DH Representation, Euler transformation matrix. Jacobian matrix transformation.

Robot Drives: Types of drive systems: Pneumatic, Hydraulic, Electric (brushed and brushless DC motors, stepper motors). Sources of mechanical errors and estimation.Remote centre compliance devices. Accuracy resolution and repeatability of robots. Mechanical components (springs, gears, belts, chains, joints, clutches, brakes, bearings).

Sensors: Potentiometers, synchros, resolvers, linear variable differential transformers, opto-interrupters, optical encoders, velocity sensors, accelerometers, proximity sensors, force and torque sensors.

Programming: AL programming language, Computer model and CAD simulation packages for Robotics applications.

Robot Control: Transfer Functions using Laplace transforms. Open loop, closed loop control, servo control robots Impulse response and step response. PID controllers. Position and orientation control of the robot.

Machine vision: Machine vision (data capture, image display, texture) extraction, object recognition. Imaging components. Point sensors, line sensors, planar sensors, volume sensors. Image representation. Picture coding. Existing vision systems Binary vision, Gray-level vision. Scanning methods.
Recommended and/or required reading:
  • J. J. Craig, Introduction to Robotics: Mechanics and Control, Prentice Hall, 2003.
  • S. B. Niku, Introduction to Robotics: Analysis, Systems, Applications, Prentice Hall, 2001.
  • E. Wise, Applied Robotics, Prompt Publications, 2000.
Planned learning activities and teaching methods

Students are taught the course through lectures (3 hours per week) in classrooms or lectures theatres, by means of traditional tools or using computer demonstration.

Auditory exercises, where examples regarding matter represented at the lectures, are solved and further, questions related to particular open-ended topic issues are compiled by the students and answered, during the lecture or assigned as homework.

Topic notes are compiled by students, during the lecture which serve to cover the main issues under consideration and can also be downloaded from the lecturer’s webpage. Students are also advised to use the subject’s textbook or reference books for further reading and practice in solving related exercises. Tutorial problems are also submitted as homework and these are solved during lectures or privately during lecturer’s office hours. Further literature search is encouraged by assigning students to identify a specific problem related to some issue, gather relevant scientific information about how others have addressed the problem and report this information in written or orally.

Students are assessed continuously and their knowledge is checked through tests with their assessment weight, date and time being set at the beginning of the semester via the course outline.

Students are prepared for final exam, by revision on the matter taught, problem solving and concept testing and are also trained to be able to deal with time constraints and revision timetable.

The final assessment of the students is formative and summative and is assured to comply with the subject’s expected learning outcomes and the quality of the course.
Assessment methods and criteria
Final Exam60%
Language of instructionEnglish
Work placement(s)NO