Course specification and structure
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PMROBWAI - MSc Robotics with Artificial Intelligence

Course Specification

Validation status Validated
Highest award Master of Science Level Masters
Possible interim awards Postgraduate Diploma, Postgraduate Certificate, Advanced Diploma in Professional Development
Total credits for course 180
Awarding institution London Metropolitan University
Teaching institutions London Metropolitan University
School School of Computing and Digital Media
Subject Area Communications Technology and Mathematics
Attendance options
Option Minimum duration Maximum duration
Full-time 1 YEARS 2 YEARS
Part-time 2 YEARS 4 YEARS
Course leader  

About the course and its strategy towards teaching and learning and towards blended learning/e-learning

Robotics and Artificial Intelligence (AI) are undergoing a major transformation, which is driven by the rise in computer processing power, the profusion of data, and the development of techniques such a ‘deep learning’. AI is everywhere from mobile phone apps to self-driving cars and, when combined with robotics, forms a vital part of our future. AI enabled Robots are growing beyond being the workhorses of industrial shop floors, and beginning to assume the roles of personal assistants, delivery vehicles, surgical assistants, assist doctors with medical diagnoses, exoskeletons, driverless vehicles, and unmanned aerial vehicles (UAVs), among many others.

While AI provides the foundation for creating smart systems to make intelligent choices, robotics is what actually makes the task to be executed. Bringing Robotics and AI together provides an opportunity to build intelligent systems that will make our lives better and safer. Robotics and AI are most certainly amongst the few candidate technologies to comprise the next innovation wave. As Robots and other autonomous systems continue to improve in functionality and decline in costs going forward, their likely impact on productivity will be even more significant. Companies around the world are increasing their use of Robots. With integration of AI and other improvements in Robotics (e.g., better machine vision, better sensors, etc.), robotics promises to see significantly improved pricing and performance over the next decade. Furthermore, the use of Robotics will increase productivity and has the potential to bring more manufacturing production work back to developed countries like the UK. Besides manufacturing sector, Robotics and AI technologies is also forecasted to have disruptive implications for a range of industries such as health care, transport and logistics, customer service, and home maintenance. It is, thus, no wonder that the World Economic Forum’s 2018 report on the future of jobs predicts a significantly increase demand in roles like Robotics engineers and AI and machine learning specialists .

Given that Robotics and AI have an important degree of interdependency, Governments are investing heavily, and industry is taking up the challenge to translate academic research to realize this potential. Robotic technology is already shaping the workplaces of tomorrow. The global market for industrial Robots is predicted to grow to over $70 billion by 2023 according to a recent report by Mordor Intelligence . Growth in the use of Robotic systems and AI is likely to affect workers in different ways across many sectors including manufacturing, health, automobile and others. We are now moving beyond traditional approaches to robotics that relied principally on automation and are entering a new phase of autonomous robotics and systems that requires advanced knowledge, understanding and skills and an interdisciplinary perspective across a broad range of disciplines. As a result, we need to train the next generation of graduate students of Robotic systems who can think differently and have the creativity and ingenuity to match the potential for Robotics to change our lives.

London Metropolitan University has a long tradition of promoting hands-on practical courses in the latest technologies. In fact, it was one of the first UK universities to deliver Radio Engineering in the late 1940’s. This tradition has continued with the new MSc course in Robotics with Artificial Intelligent that places a strong focus on practical hands-on approach in teaching and learning. This MSc degree course brings together these two areas, and offers conceptual grounding in intelligent systems, and the chance to apply theoretical knowledge in a practical setting. Students will learn about the development and design of robotic systems, artificial intelligence, robotic visions, sensors and actuators which enable robots to sense and interact with their physical environment. The programme will give students a solid awareness of the key concepts of Robotics with Artificial Intelligence. They will develop a wide-ranging skill set that are required by the employers and supports further research studies. Moreover, the course is designed to comply with the learning outcomes of the Institution of Engineering Technology (IET), which is an accrediting body under licence from the UK regulator, the Engineering Council.

Graduates of this MSc course will typically develop a range of key skills such as being able to use their initiative and take responsibility, solve problems in creative and innovative ways, make decisions in challenging situations, continue to learn independently and to develop professionally, including the ability to pursue further research where appropriate, and communicate effectively, with colleagues and a wider audience, in a variety of media. Students interested in pursuing a career in enterprise related with Robotics will have an option to take a business-oriented module. These skills have been identified by the government as being critical in curtailing the continued shortage of higher-level skills within the wider UK economy and we believe that students will be best placed following graduation in developing their career aspirations. The course will provide graduates with a fantastic platform from which to enter a wide range of challenging sectors: such as Finance, Engineering, IT & Technology, Manufacturing & Production, Transport & Logistics, Public Sector, Defence, Healthcare, Entertainment and Science.

The course is designed and developed to deliver an inclusive curriculum that provides all students, regardless of background and immutable characteristics, with an equal opportunity to achieve the learning outcomes of the programme of study. The aim is to improve the experience, skills and attainment of all students, including those in protected characteristic groups, by ensuring that all students, regardless of background, are able to participate fully and achieve at equal rates. The MSc programme will use a diverse range of voices and perspectives across course content, for example in reading lists, case studies, lecture content etc. The content of the course is contextualized where appropriate to show that white, male and western might now dominate current academic discoveries and theories, however this might not always have been the case historically. Acknowledged are any limitations in the demographic representation of course material. Stereotypes are avoided in course content and diversity celebrated. A range of examples will be provided when preparing lectures, reading lists or problem-based scenarios present equality in a positive light and a non-stereotypical way.

Teaching and Learning: Appropriate blended learning technologies, such as the University’s virtual learning environment (Weblearn), are used to facilitate and support student learning, delivering content; encouraging active learning; Providing formative and summative assessments with prompt feedback; enhance student engagement and learning.

Core and Optional modules in the course have a dedicated site in Weblearn where you will find not only the general information related to the module but also the weekly activities, lecture material (audio and video), coursework, practical workshop manuals and the module forum where students and academics can discuss various issues related to the module and course. Module leaders use this site regularly to communicate with their students including guidelines on how to prepare for assessment, supporting materials, and general feedback.

Most modules consist of a combination of lectures, small group teaching, practical work, directed reading and coursework assignments. At the end of the taught part of the course you will undertake an individual project associated with a research group. Small group teaching, including all practical work, and the individual project accommodate different learning styles. One-on-one tutorials can support full class lectures, when required.

The taught modules of the programme run over two semesters. The programme consists of four compulsory (or core) modules such as Robotic Systems, Computer Vision, and Artificial Intelligence, Sensors, and Actuators and Control. Students will have an opportunity to select an optional module from Machine Learning, Discrete Mathematical Structures, Statistical Modelling, Information Security, and Operations and Technology Management. The focus is on preparing students for their individual project and enhancing their dissertation writing skills.
Finally, the MSc project (in Summer / Spring semester) enables students to demonstrate their mastery of specialist techniques and relevant methods of enquiry, and their ability to design and deliver advanced applications, systems and solutions to a tight deadline, including the production of a substantial dissertation.

Assessments: Testing of the knowledge base is through a combination of unseen written examinations and assessed coursework in the form of problem-solving exercises, laboratory reports with literature review components, design exercises, and individual and small-group projects

Examinations are held at the end of Semester 1 (January) and at the end of Semester 2 (May/June). Students who have successfully completed taught module credits as explained in Section 24 may exit with a Postgraduate Certificate or Postgraduate Diploma.

The following is the normal pattern of study for a full-time student, completing the programme within 12 calendar months:
Semester 1: Two compulsory modules. Examinations are held in January.
Semester 2: Further two core modules. Examinations are held in May/June. One optional module can be selected in Sem 1 and Sem

Course aims

The course is designed to fulfil the University’s mission to provide students from diverse sociocultural backgrounds with the highest quality education in a research-led environment, maximizing employability, innovation and globally recognized graduate skills, and equipping them for their future.
Very much in line with the London Met’s strategic vision, the overall aim of the course includes producing graduates who will go out into the world of work as confident, values-driven and successful individuals, making a positive contribution to society and offering a constant flow of talent to support the global economy. At the core of the course, the students are provided with an opportunity to gain specialize knowledge in robotic systems and artificial intelligence by examining the structure and design of various robotic systems, sensors and actuators and their required controllers as well as the software and/or platform needed to establish communications between the components and thereby making intelligent decision.
The course satisfies the subject benchmark statements of the QAA for masters’ courses and the educational requirements of the Engineering Council UK for Chartered Engineer status.
The aims of this course are to:

• To provide postgraduates with a specialist understanding of techniques applicable in robotics, and a depth and breadth of both knowledge and skills sufficient to enable them to work in their chosen specialist subject as well as related engineering areas;

• To develop a comprehensive understanding of the relevant scientific principles and concepts relevant to robotic and artificial intelligence and the ability to critically evaluate and apply them effectively to a given problem / project;

• To undertake a substantial project involving critical application and integration of their knowledge in their undergraduate studies and the postgraduate taught modules from analysis synthesizing a solution taking into consideration, assessment of the limitations and sustainability (present and future);

• To develop/expand the ability of the students to work individually or as part of a team to generate innovative design for products (existing robots), systems, components (sensors and actuators) or processes to fulfil new needs, taking account of a range of commercial and industrial constraints;

• To enable the students to develop original ideas and solve complex problems in new or unfamiliar environments, based on advanced knowledge of the principles and methodologies of robotic systems and artificial intelligence;

• To make the students aware of the regulatory requirements and be able to make general evaluations of risk issues in the context of the robotics and artificial intelligence, including health & safety, environmental and commercial risk;

• Develop independent learning skills as required for continued professional development and lifelong learning;

• Communicate effectively orally and in writing to specialist and non-specialist audiences;

• Recognise legal and ethical issues of concern to business, professional bodies, and society, including but not limited to information security, and follow relevant guidelines to address these issues.

Course learning outcomes

The programme provides opportunities for students to develop and demonstrate knowledge and understanding, qualities, skills and other attributes related to robotic systems which encompass elements of hardware/software & mathematical models. The programme outcomes comply with the latest benchmark statements for master’s degree by the QAA and Engineering Council UK.

LO1 Knowledge and understanding
1.1 Have a comprehensive understanding of the scientific principles of robotics design and control and related disciplines, such as artificial intelligence, and a critical awareness of current problems and new insights.
1.2 Identify, classify, and describe the performance of robotic systems and components, as well as dynamic and kinematic systems, using analytical methods and modelling techniques.
1.3 Have a comprehensive knowledge and understanding of mathematical and computer models relevant to intelligent systems, and an appreciation of their limitations.
1.4 Clearly elaborate on the role of hardware and software in the design of robotic systems.
1.5 Gain knowledge and comprehensive understanding of design processes and methodologies and the ability to apply and adapt them in unfamiliar situations, considering a range of commercial and industrial constraints.
1.6 Understand legal requirements, professional and ethical conduct, and commercial and economic context of engineering processes.

LO2 Cognitive / intellectual skills
2.1 Originality in the application of knowledge, together with a practical understanding of how established techniques of research and enquiry are used to create and interpret knowledge in the robotics & AI related discipline.
2.2 Apply established computational and analytical techniques approach to the solution of complex problems related to robotic & AI and assess the limitations of the techniques employed.
2.3 Evaluate the latest techniques and technologies to generate an innovative design for products, systems, components or processes to fulfil new needs.
2.4 Formulate, design, analyse, implement, test and evaluate the performance of complex scenarios of robotic systems and related technologies. Consideration should be given to health and safety, risk management, security risks, sustainability, equality, diversity, inclusion, cultural, societal and environmental impact, commercial matters, codes of practice and industry standards. The proposed solutions should minimize adverse impacts.
2.5 The ability to make sound judgements in the absence of complete data and communicate their conclusions clearly to specialist and non-specialist audiences.
2.6 The ability to critically evaluate technical literature and current research to solve complex problems with consideration to the future needs of businesses through clearly articulated proposal(s), justifying results and defend conclusions.
2.7 While working collaboratively on solving a complex problem, plan tasks, effectively manage resources and budget and demonstrating leadership in areas of responsibility.

LO3 Key and transferable skills
3.1 Communicate and work effectively either individually or within a team, and exercise initiative and personal responsibility as a team member or leader.
3.2 The ability to demonstrate self-direction and originality in tackling and solving problems, and act autonomously in managing resources and time at a professional level.
3.3 The ability to continue to advance their knowledge and understanding related to robotics, and to develop new skills to a high level.
3.4 The ability to plan self-learning and improve performance, as the foundation for lifelong learning/CPD.
3.5 Be able to make decision in complex and unpredictable situation.
3.6 Demonstrate confidence, resilience, ambition and creativity and will act as inclusive, collaborative and socially responsible practitioners/professionals in their discipline. (ULO)

Principle QAA benchmark statements

1. QAA Subject Benchmark Statement for Engineering
2. Engineering Council UK - The Accreditation of Higher Education Programmes

Assessment strategy

A range of assessment methods is employed throughout the course.

• Case study reports and presentations
• Specialised laboratory workshops and logbooks
• Individual and group coursework
• Unseen examinations
• Individual viva

The method of assessment for each module is clearly described in the individual Module Guide which is made available to the students at the start of the semester via the dedicated Web site (Weblearn) providing students with comprehensive learning/teaching material including, practical workshops and Case studies. Module leaders use this site regularly to communicate with their students including guidelines on how to prepare for assessments, supporting materials, and general feedback.

Every piece of assessment such as coursework, assignments including workshop logbooks and reports accompanies comprehensive guidelines on how to succeed and achieve excellence. The guidelines include a detailed assessment category where the tutors use to assess students work (the actual marking criteria).

Group work or collaborative working will comprise a group of between two and six students. The aim of group work is to build skills relevant to employment, such as team-working, collaboration, organizational and personal time management. The module leader will assign and monitor the groups. Students will be required to submit individual reports in which they will indicate the activities undertaken by other members of the group. A small fraction of the coursework marks will be used for peer assessment to manage the problem of students not doing their fair share and enable greater student involvement in the assessment process.

The standard as well as the assessments of every piece of work is assessed and approved by an independent internal moderator as well as the external examiner from another institution or relevant industry.

The assessment of the final individual project will be by dissertation and a viva. The documentation consists of (a) an initial project proposal including background reading / research a comprehensive list of related references/bibliography, and (b) final project report, which is the comprehensive major report on the subject of the project reflecting in a coherent manner the entire learning experience of the student, submitted in September.

Students will be advised to submit their dissertation (whole or in parts) to their supervisor for formative feedback before their final submission.

The proposal will be assessed by the supervisor and moderated by another academic selected by the module leader. Students are provided with a comprehensive written feedback by both assessors.
Each dissertation is initially assessed by two assessors, one of which is the supervisor. The second assessor may be any member of staff in the School, preferably with an area of expertise pertinent to the project subject. During the viva the student will be given the opportunity to briefly describe his/her project and its main findings before a more detailed questioning. The viva gives the opportunity to the student to explain his/her research as well as prove that the work reported is the student's own. Also, full consideration of ethical and legal issues and professional approach to the project will be assessed throughout the viva.
For fairness and consistency, a panel designated by the Module Leader will look at all project reports as part of the moderation process.

Organised work experience, work based learning, sandwich year or year abroad

Exceptionally talented students may be invited to join and contribute to research and enterprise activities within the School’s research centres. In addition, students possessing extensive industrial experience and/or relevant professional qualifications may be given the opportunity to teach within the School. Some interested students may be offered to serve as student coaches in the School’s undergraduate courses.

Course specific regulations

Part-time students may select one or two modules per semester in consultation with the course leader. Two possible examples are as follows:

Example 1:
Year 1 (Semester 1/ Autumn): Robotic Systems, Artificial Intelligent
Year 1 (Semester 2 / Spring): Sensors, Actuators and Control, Computer Vision
Year 2 (Semester 1/ Autumn): Option (e.g. Operation Technology and Management)
Year 2 (Semester 2 / Spring): Option (e.g. Statistical Modelling and Forecasting)
Year 2 (Summer): MSc Project

Example 2:
Year 1 (Semester 1/ Autumn): Robotic Systems,
Year 1 (Semester 2 / Spring): Sensors, Actuators and Control
Year 2 (Semester 1/ Autumn): Artificial Intelligent and an option (e.g. Operation Technology and Management)
Year 2 (Semester 2 / Spring): Computer Vision and an option (e.g. Statistical Modelling and Forecasting)
Year 2 (Summer): MSc Project

N/A at the moment but will have additional clauses as required by IET once course is accredited by IET.

Modules required for interim awards

 To obtain the degree award of MSc Robotics with Artificial Intelligence the students need to complete successfully all 6 taught modules and the MSc project module (a minimum of 180 credits with a minimum mark of 50%.).
 Completing all six taught modules (a minimum of 120 taught credits excluding the Project with a minimum mark of 50%.) included in the course curriculum entitles the students to the PGDip Robotics with Artificial Intelligence award.
 Completing two out of three CT modules (CT7158 Robotics Systems, CT7159 Sensors, Actuators and Control, CT7160 Computer Vision) and CS7050 Artificial Intelligence (a minimum of 60 taught credits excluding the Project with a minimum mark of 50%) from the curriculum entitles the students to the PGCert Robotics with Artificial Intelligence award.

The MSc award only, is categorised into following specific grades with due consideration to borderline cases as per University regulations:
MSc with Distinction: This award is achieved by a student gaining an overall average mark on the programme of study of 70% and above.
MSc with Merit: This award is achieved by a student gaining an overall average mark on the programme of study between 60% and 69.99%.
MSc: This award is achieved by a student gaining an overall mark in the programme of study between 50% and 59.99%.

Arrangements for promoting reflective learning and personal development

Employability discussion with members of our Industrial liaison Group has revealed that employers need to be sure that graduates are able to take individual responsibility for their own and others’ work without supervision, that they are capable of assimilating and organising complete information quickly and effectively and that they are self-learners, capable of keeping abreast of new developments without organisational support. Our approach to teaching and learning is designed to produce master’s graduates who meet these criteria. From the outset, students will be expected to meet the basic professional requirement of taking responsibility for their own learning.

With engineering degrees lectures are extensively used to provide structure for each subject, to help to direct students’ further reading and self-study, to convey how the underlying engineering science is applied to discipline specific problems and to demonstrate approaches to problem-solving. Typically, student self-study after lectures is supported by tutorial or problem classes, where advice is given on request to students who have issues arising from their application or understanding of the lecture material. Other types of classes include longer “hands-on” practical laboratory/workshop/ computer sessions, seminar/presentation activities and design project work. Formative and summative feedback will be given on coursework to promote reflective learning and improve student knowledge and understanding. After the formal scheduled teaching element students are expected to propose project topics which better place them within the professional context as well as better utilize the knowledge and skills gained while studying.

There is an expectation that students will manage their own learning, with seminar/tutorial classes in which students present material they have researched themselves and independent work on assignments prevalent. This includes a team design activity too. Students undertake a major 60 credit individual project related to the specialist stream they are following. The Accrediting Institutions place a high importance on this project which must be passed to get the Degree.

Career, employability and opportunities for continuing professional development

The course places a strong emphasis on students’ employability and the development of
marketing skills takes place through a continuous process of self-development embedded in
all core modules. There is an underlying philosophy of linking the acquisition of knowledge to
real-world scenarios. Students are placed in the best possible position after graduation to pursue rewarding and successful careers in the Robotics and AI related disciplines. In fact, Robotics and AI play a large and increasing role in manufacturing, space exploration, the office and the home, with products including: driverless cars, unmanned air vehicles (UAVs), medicine, 3D printers, cash dispenser machines, robot floor cleaners, pharmaceuticals, toys etc. Graduates of the programme will be well equipped for careers in a range of industries and SMEs, from advanced manufacturing to oil and gas exploration, nuclear energy to railways and automotive, healthcare to defence. All of our master’s graduates will also be equipped to continue academic study at a higher level, for example for a PhD.

Examples of possible careers you can consider pursuing in robotics with AI are:
• Machine Learning Engineer
• Research Scientist
• Robotics Engineer
• Robotics Technician
• Research and Development in Robotics and Artificial Intelligence
• Agriculture, Healthcare, Transportation, Retail and Manufacturing
Robotics and AI Legal and Ethics – assist companies in navigating federal approvals, determining company policies and establishing ethics policies.

The School has two established Research Centres (Centre for Communications Technology, and Cyber Secure Centre) that carry out research & KTP projects as well as industry consultancies. Students on the course will have a chance to participate on related projects and gain real work experience should opportunities arise. With the establishment of the MSc course, the course team intends to create links with the robotics industry and explore opportunities for students studying on the course to undertake work placements. Alternatively, students will be given an opportunity to take Work Related Learning module (FC7W03) to gain experience and apply their knowledge to solve real world problems. This will be facilitated by periodically arranging workshops/seminars that will be delivered by invited guest from industry.

Career opportunities

The high demand for Robotics and Artificial Intelligence specialists in business and industry promises wider employability, a broader career path and higher starting salaries.

Careers in robotics include:

  • Robotics Control System Engineer
  • Electronics Engineer (Robotics)
  • Robotic Stems Electronics Engineer
  • Software Engineer/Developer – Robotics
  • Electro-Mechanical Robotics Engineer
  • Robotics Control System Engineer
  • Robotics/Computer Vision Engineer
  • Engineer in Robotics, Control & Automation Engineer – Robotics
  • Graduate Robotics Engineer
  • Computer Vision Engineer in Robotics
  • Robotic Systems Engineer
  • Embedded Software Engineer – Robotics, etc.

Careers in computer systems engineering include:

  • Computer/IT Systems and Support Engineer
  • Systems Engineering
  • IT System Support Engineer
  • Software Support Engineer
  • Application Support Consultant
  • System Support Engineer
  • Graduate System Support Engineer

Entry requirements

You'll be required to have:

  • a minimum of a lower second-class (2.2) honours degree (or equivalent) in electrical and electronic engineering, robotics, mechatronics, computer science or systems engineering, computing, computer networking, physics or a closely related discipline.

Graduates from other disciplines who have extensive relevant work experience will be considered on an individual basis, and may be asked to attend an interview.

Programming skills with at least one of the popular languages (e.g. Java, Python, C/C++) and computational tools such as Matlab/Maple as well as some familiarity with electronic devices would be highly desirable.

Official use and codes

Approved to run from 2021/22 Specification version 1 Specification status Validated
Original validation date 19 May 2021 Last validation date 19 May 2021  
JACS codes H670 (Robotics and Cybernetics): 100%
Route code ROBWAI

Course Structure

Stage 1 Level 07 September start Offered

Code Module title Info Type Credits Location Period Day Time
CS7050 Artificial Intelligence Core 20 NORTH AUT MON AM
CT7158 Robotic Systems Core 20 NORTH AUT MON AM
CT7159 Sensors, Actuators and Control Core 20 NORTH SPR MON AM
CT7160 Computer Vision Core 20 NORTH SPR MON PM
CT7P01 MSc Project Core 60 NORTH SPR WED PM
          NORTH SUM WED PM
          NORTH AUT WED PM
CS7052 Machine Learning Option 20 NORTH AUT WED AM
CS7064 Information Security Option 20 NORTH SPR THU AM
FC7W03 Work Related Learning Option 20 NORTH SPR WED PM
          NORTH AUT WED PM
MA7007 Statistical Modelling and Forecasting Option 20 NORTH SUM WED AM
          NORTH SPR WED PM
          NORTH SUM MON PM
MA7009 Discrete Mathematical Structures Option 20 NORTH AUT TUE PM
MN7001 Operations and Technology Management Option 20 NORTH AUT TUE AM
          NORTH AUT TUE PM

Stage 1 Level 07 January start Offered

Code Module title Info Type Credits Location Period Day Time
CS7050 Artificial Intelligence Core 20        
CT7158 Robotic Systems Core 20        
CT7159 Sensors, Actuators and Control Core 20 NORTH SPR MON AM
CT7160 Computer Vision Core 20 NORTH SPR MON PM
CT7P01 MSc Project Core 60 NORTH SPR WED PM
          NORTH SUM WED PM
CS7052 Machine Learning Option 20        
CS7064 Information Security Option 20 NORTH SPR THU AM
FC7W03 Work Related Learning Option 20 NORTH SPR WED PM
MA7007 Statistical Modelling and Forecasting Option 20 NORTH SUM WED AM
          NORTH SPR WED PM
          NORTH SUM MON PM
MA7009 Discrete Mathematical Structures Option 20        
MN7001 Operations and Technology Management Option 20