SHE Level 4
SCQF Credit Points 20.00
ECTS Credit Points 10.00
Module Code MHH623546
Module Leader Mario Mata
School School of Computing, Engineering and Built Environment
Subject Electronic Engineering
  • B (January start)

Pre-Requisite Knowledge

Digital Programmable Systems 2 or equivalent Digital Signal Processing

Summary of Content

An aim of the module is to provide the student with the required elements used in mechatronics, robotics and intelligent systems. In addition, the module's aim is to provide the student with the knowledge and skills required to design and implement an intelligent, automated robotic system considering factors require in implementing specific applications. Sensors, Data Acquisition, Computer Control Systems, Mechatronic Systems, Image Processing and Machine Vision, Mobile Robotics and Navigation, Machine Learning, Intelligent Control, Artificial Intelligence and Implementation of Intelligent Robotic Systems.


The teaching syllabus will cover the following areas: Sensors: Position and speed measurement, temperature measurement, vibration and acceleration measurement, IR sensors and accelerometers in relation to interaction of automated robotics to real world applications. Data Acquisition: Continuing from sensor theory, quantisation theory, Analogue to Digital Conversion, Digital to Analogue Conversion, sampling rate and aliasing, counter operations to allow for sensory interfaces Computer Control Systems: Open loop and close loop control systems theory, supervisory and sequential control Mechatronic Systems: Computer, microprocessor, microcontrollers and their applications in system control Image Processing and Machine Vision: Colour based object tracking, blob analysis, image thresholding, histogram calculation, motion analysis Mobile Robotics and Navigation: Methods of mobility such as wheeled, tracked etc. Navigational techniques, motion / path planning, behaviour based learning Machine Learning: Machine learning technique for classification within an intelligent robotic system; Bayesian Methods, Ensemble Learning, Support Vector Machines Intelligent Control: More advanced control systems involving Fuzzy Logic in an embedded environment for control of automated vehicles and robotic systems Artificial Intelligence: Artificial neural networks in robotics and intelligent control applications, single and multi-layer perceptron and machine intelligence and evolutionary systems Intelligent Robotic System Implementation: Intelligent robotic system implemented in C on an embedded device utilising control systems, sensory equipment and machine learning techniques to produce an intelligent automated robotic system.

Learning Outcomes

On completion of this module the student should be able to:Select and utilise appropriate sensory devices for a specific taskApply relevant data acquisition techniques in conjunction with sensory devices in embedded systems within a robotics environmentUtilise machine vision techniques such as object tracking for use with robotic systemsCritically evaluate the various methods of mobility and navigation for robotic systemsApply and implement various machine learning techniques available in robotic systemsDesign and implement an intelligent robotic system on an embedded platform utilising the various techniques aboveDemonstrate an awareness of the ethical issues associated with developing intelligent machines

Teaching / Learning Strategy

The University 'Strategy for Learning' documentation has informed the learning and teaching strategy for this module. The course material will be introduced through lectures and practical exercises based on lecture material will be applied during lab and tutorial sessions. Tutorials will be used to explain and elaborate on the lecture material. The laboratory work will provide the student with support to extend their embedded design skills within a robotic systems context. This will involve a number of formative laboratory exercises. This summative assessment of these technical skills will be in a final project. A workshop will be provided to address the social and ethical issues associated with machine learning and robotics. Guest lectures from industry will be utilised to contextualise the theoretical and practical work to real world global business scenarios. Full use will be made of GCU Learn to provide Lecture-based and related study materials, along with sample solutions of Tutorial and Laboratory exercises, thus encouraging the development of independent learning and allowing self-reflective feedback on student performance. Staff-based feedback on student performance for submitted work will be provided in line with the University feedback policy, with summative feedback and grades on the coursework assessment utilising GCU Learn. The additional interactive discussion features of GCU Learn will be utilised, as appropriate to the module, to stimulate independent and flexible student learning outwith scheduled class time.

Indicative Reading

Both the learned and the popular literature on image processing and machine vision will be used as a source of information for private study. The titles and electronic resources below are to be considered as indicative only. -360 1. Probabilistic Robotics; Wolfram Burgard, Dieter Fox, and Sebastian Thrun -360 2. Intelligent Systems, Control and Automation: Science and Engineering, Tzafestas, S.G. 3. Artificial Intelligence: A Modern Approach, Stuart Russell

Transferrable Skills

Specialist knowledge and application. Critical thinking and problem solving. Critical analysis. Communication skills, written, oral and listening. Numeracy. Effective Information retrieval and research skills. Computer literacy. Self confidence, self discipline & self reliance (independent working). Creativity, innovation & independent thinking. Knowledge of international affairs. Reliability, integrity, honesty and ethical awareness Entrepreneurial, independence and risk-taking. Presentation skills. Commercial awareness

Module Structure

Activity Total Hours
Practicals (PT) 12.00
Tutorials (FT) 12.00
Assessment (PT) 20.00
Lectures (PT) 24.00
Tutorials (PT) 12.00
Practicals (FT) 24.00
Independent Learning (FT) 120.00
Assessment (FT) 20.00
Independent Learning (PT) 132.00
Lectures (FT) 24.00

Assessment Methods

Component Duration Weighting Threshold Description
Coursework 2 n/a 25.00 n/a Coursework 2
Coursework 1 n/a 25.00 n/a Coursework 1
Exam (Exams Office) 2.00 50.00 35% Exam linked to Learning Outcomes