Robotics MSc

Robots are becoming ubiquitous in industrial environments as well as in everyday life. Industries including automotive, oil and gas, aerospace and energy have significant future growth in the service robot domain. The course provides insight into multiple application domains for intelligent and autonomous robot systems including industry, hazardous environments, health care, domestic/assistive robotics, and autonomous vehicles.

Unique in its focus on human aspects and supported by practical applications, this course will enhance your employment prospects by providing you with relevant theoretical knowledge and practical skills to meet rising global demand for professionals in this field.

Overview

Start dateOctober
DurationOne year full-time, two-three years part-time
DeliveryTaught modules 40%, group project 20%, individual research project 40%
QualificationMSc
Study typeFull-time / Part-time
CampusCranfield campus

Who is it for?

The course is suitable for those with essential engineering mathematics knowledge and basic computer programming skills who are looking to broaden their understanding of robotics uses, applications and science.

Typically our students come from backgrounds in aeronautics/aerospace engineering, mechanical engineering, electrical/electronic engineering, pure mathematics, computer science, software engineering, mechatronic engineering, information technology.

Why this course?

As the demand for robotics engineers is increasing, this MSc has been developed for graduates to equip themselves with the skills and knowledge required for associated careers. The course provides unique experience of the breadth of the field of robotics, preparing students with not only a broad understanding of their uses and applications in modern society but also the fundamental science behind them.

Robotics technologies are being increasingly adopted across various industries including automotive, oil and gas, aerospace and energy, as well as potential significant future growth in the service robot domain. Application domains for intelligent and autonomous robot systems include hazardous environments, health care, domestic/assistive robotics and autonomous vehicles.

With this MSc, your employability prospects will be enhanced thanks to the relevant theoretical knowledge and practical skills that you will gain, helping you to become successful robotics engineers and experts in robotics and to meet the rising global demands.

Discover the available to you as a student on this course.

Informed by industry

The course is directed by an Industrial Advisory Panel who meet twice a year to ensure that the MSc meets industry demand and provides the right mix of hands-on skills and up-to-date knowledge suitable to the wide variety of applications that the field addresses. This ensures that the what you learn is relevant to industry and that the skills you develop are those sought-out by industry employers.

Our industrial partner, ABB Robotics, deliver practical workshops at their state-of-the-art Milton Keynes training facility, which as a Cranfield student you will have the opportunity to benefit from.

A number of our industrial partners attend the annual student thesis presentations which take place at the end of July. This provides a prime opportunity for you to meet and network with key employers.

Course details

In this section

This course consists of eight one-week assessed modules, a group project and an individual project. Students are also supported in their learning and personal development through industry seminars, a group poster session, group discussions, group presentations, video demonstrations, case studies, laboratory experiments, coursework and project work. Students will receive hands-on experience with our range of conventional and collaborative robots located in the Aerospace Integration Research Centre - supporting practice workshops, student group projects and individual projects. You will participate in a study tour carried out at ABB Robotics in Milton Keynes where you will spend up to a week at our industrial partner ABB Robotics’ training centre, receiving practical robot training.

The new MSc in Robotics will use standard teaching and assessment methods as well as technology enhanced teaching (TET) methods such as a Virtual Learning Environment (VLE) to support different learning styles. Theories and fundamental of robotics will be taught in both lecture and workshop formats where videos and technology demonstrators will be used as teaching aids. For example, a collaborative robot will be used in the teaching of human-robot interaction and virtual reality technology will be used in teaching digital robotics. Lecture videos will be available on the VLE to provide an interactive learning experience. Students will receive hands-on experience on programming industrial robots, initially in a robot simulation lab, which follows a practical robot-programming workshop using real industrial robot supported by ABB Robotics. The use of robotics technology will enable students to see robot engineering phenomena first-hand where they can test engineering principles and theories with real devices.

Course delivery

Taught modules 40%, group project 20%, individual research project 40%

The group design project is intended to provide you with the invaluable experience of delivering a project within an industry-structured team. The project allows you to develop a range of skills including learning how to establish team member roles and responsibilities, project management, delivering technical presentations and gaining experience of working in teams that include members with a variety of expertise and often with members who are based remotely.

2019 group project: In-To-Net-Bot
Students worked to explore the feasibility of a self-learning ball catching robot system that can potentially solve logistics and mobility issues in human-robot interaction. The main objective was to use a combination of engineering and artificial intelligence methods in providing the robot the ability to predict trajectory of an object in-flight, and to generate the robot motions required to catch the object.

Part-time students are encouraged to participate in a group project as it provides a wealth of learning opportunities. However, an option of an individual dissertation is available if agreed with the Course Director.

The individual research project allows you to delve deeper into an area of specific interest. It is very common for industrial partners to put forward real-world problems or areas of development as potential research thesis topics. For part-time students it is common that their research thesis is undertaken in collaboration with their place of work.

Previous projects have included:

The design of a novel human-robot collaboration decision-making framework for the Flying Co-Worker,
Accurate tether-based localisation system for tank-inspection robotic crawler,
Design of a modular self-assembling robot system (MoSARS).

Modules

Keeping our courses up-to-date and current requires constant innovation and change. The modules we offer reflect the needs of business and industry and the research interests of our staff and, as a result, may change or be withdrawn due to research developments, legislation changes or for a variety of other reasons. Changes may also be designed to improve the student learning experience or to respond to feedback from students, external examiners, accreditation bodies and industrial advisory panels.

To give you a taster, we have listed the compulsory and elective (where applicable) modules which are currently affiliated with this course. All modules are indicative only, and may be subject to change for your year of entry.

Course modules

Compulsory modules
All the modules in the following list need to be taken as part of this course.

Aim	Robotics technologies are being increasingly adopted across various industries, these include automotive, oil and gas, aerospace and energy as well as potential significant future growth in the service robot domain. The aim of this module is to introduce students to different types of robots and their practical uses. Students will experience offline programming of industrial robot as well as hands on programming during a study tour on the last day of the module.
Syllabus	• Introduction to Robotics and history of robots • Classification of robotic systems • Industrial Robots • Basic control of robots • Robot sensing • Robotic applications • Offline programming of industrial robot • Practical robot programming
Intended learning outcomes	On successful completion of this module you should be able to: 1. Distinguish different types of robots across different domains, appraise their characteristics and examine their common applications. 2. Examine critical components of different robot systems and critique the functionalities and performance of different configurations. 3. Distinguish industrial robot programming methods and techniques, and construct robot procedures for automated operations. 4. Demonstrate a systematic approach in constructing an offline programme using professional robot simulation software package. 5. Develop an industrial robot programme and integrate onto a real robot to perform simple tasks.

Aim	This module aims to provide students with the fundamental knowledge for solving robot control problems that will be applicable in the design of robot control systems. The syllabus covers control theories that are essential for the control of robot manipulators as well as mobile robots.
Syllabus
Intended learning outcomes	On successful completion of this module you should be able to: 1. Examine a given control problem and appraise the suitability of a control system as a solution 2. Construct Forward Kinematic and Inverse Kinematic solutions for a Multi Degree of Freedom Robots 3. Design a feedback control system for solving a real world situation 4. Construct the path and motion planning of a robot manipulator for solving a practical task

Aim	The aim of this module is to provide students with the necessary knowledge and understanding for the application of machine learning & artificial intelligence techniques to real world industrial problems within the domain of robotics and beyond.
Syllabus	Introduction to Machine Learning Theory Applications. Decision tree modelling, logical reasoning. Probability theory and Bayesian methods. Classification methods and clustering techniques. Bio-inspired artificial intelligence algorithms. Reinforcement learning. Case study for robotics applications.
Intended learning outcomes	On successful completion of this module a student should be able to: Construct a wide range of machine learning techniques to solve industry problems particularly within the domain of robotics. Appraise the application of machine learning approaches to a wider set of data mining and classification type problems. Using a provided implementation, plan machine learning analysis on suitable forms of computer and robotics data. Examine the concepts and operation of a range of machine learning algorithms in order to facilitate re-implementation in a software programming environment with which they are already familiar Develop programme in solving machine learning problems through interactive learning workshops.

Aim

Syllabus

Introduction to Machine Learning Theory Applications.
Decision tree modelling, logical reasoning.
Probability theory and Bayesian methods.
Classification methods and clustering techniques.
Bio-inspired artificial intelligence algorithms.
Reinforcement learning.
Case study for robotics applications.

Intended learning outcomes

On successful completion of this module a student should be able to:

Construct a wide range of machine learning techniques to solve industry problems particularly within the domain of robotics.
Appraise the application of machine learning approaches to a wider set of data mining and classification type problems.
Using a provided implementation, plan machine learning analysis on suitable forms of computer and robotics data.
Examine the concepts and operation of a range of machine learning algorithms in order to facilitate re-implementation in a software programming environment with which they are already familiar
Develop programme in solving machine learning problems through interactive learning workshops.

Aim	This module aims to answer the question ‘what is object oriented programming (OOP)’ and to provide the students with the understanding and skills necessary to write well designed and robust OO programs in C++. Students will learn how to write C++ code, starting from fundamental programming aspects and progressing through to advanced OOP. Hands-on programming sessions form an essential part of the module.
Syllabus	• Programming concepts and their implementation • The OOP methodology, classes, abstraction and encapsulation. • Destructors and memory management. • Stream input and output. • Function and operator overloading. • Inheritance and aggregation. • Polymorphism and virtual functions. • Templates, Exception handling. • The C++ Standard Library and STL.
Intended learning outcomes	On successful completion of this module you should be able to: 1. Examine the principles of the object oriented programming methodology - abstraction, encapsulation, inheritance and aggregation – and implement in the development of C++ programs. 2. Create robust C++ programs of simple to moderate complexity given a suitable specification. 3. Develop C++ programs through implementation of the Standard Template Library . 4. Construct robust C++ programs using development environments and associated software engineering tools.

Aim	The aim of this module is to introduce you to human-robot interaction design and technology, and their applications. You will learn about natural user interface and its applications in interaction. There will be opportunities to experience and practice hands-on programming of industrial collaborative robots to carry out basis tasks.
Syllabus	• Introduction to Human-Robot Interaction • Human-Robot Interactive Systems • Interaction Design/ Collaboration and teamwork • Natural User Interface • Natural Language User Interface • Safe Human-Robot Interaction • Robot programming for collaborative tasks
Intended learning outcomes	On successful completion of this module a student should be able to: 1. Examine different types of HRI system and appraise fundamental HRI theory and principles in HRI systems design. 2. Differentiate different types of human-robot interface, distinguish their functionalities and investigate novel applications. 3. Implement HRI and safety principles to develop a safe HRI system that complies to current robot standards. 4. Appraise and implement intuitive user interfaces in enhancing the efficiency and seamlessness of the interaction between humans and robots. 5. Design and construct a robot programme for carrying out human-robot collaborative tasks

Aim	The most powerful method of sensing available to humans is vision. In computing and robotics visual information is represented as a digital image. In order to process visual information in computer systems we need to know about processing digital images. By processing visual information we can develop automated visual interpretation and understanding – artificial vision, itself a large part of wider field of the Artificial Intelligence. In order to achieve this we must be able to extract high-level visual information such as edges and regions from images and additionally allow for the efficient storage of large amounts of visual data which can then be used in robotics applications.
Syllabus	• Image Applications • Image Representation • Image Capture Hardware • Image Sampling & Noise • Image Geometry & Locality, Processing Operations Upon Images • Camera Projection / Convolution Model • Image Transformation • Image Enhancement • Stereo Vision and Object Tracking • Case Study: Vision Based Metrology in Robotics
Intended learning outcomes	On successful completion of this module you should be able to: 1. Appraise the applications of common digital image representations techniques in solving Machine Vision problems. 2. Demonstrate a systematic application of Local and Global Image Transformations, and Basic Image Feature extraction for image processing. 3. Construct computer programme for implementing Image processing in Frequency domain. 4. Construct computer programme for implementing counter noise in digital images. 5. Analyse and appraise the robotics applications of three dimensional Stereo vision based systems and Object Tracking.

Aim

Syllabus

Intended learning outcomes

On successful completion of this module you should be able to:

1. Appraise the applications of common digital image representations techniques in solving Machine Vision problems.
2. Demonstrate a systematic application of Local and Global Image Transformations, and Basic Image Feature extraction for image processing.
3. Construct computer programme for implementing Image processing in Frequency domain.
4. Construct computer programme for implementing counter noise in digital images.
5. Analyse and appraise the robotics applications of three dimensional Stereo vision based systems and Object Tracking.

Aim	The aim of this module is to introduce the students to the algorithms suitable for real life problems concerning autonomy in robotics: path planning, task allocation, robotic perception, mapping and navigation, cooperative robotics, including accuracy assessment and uncertainty reduction for these applications.
Syllabus	Introduction to autonomy aspects (1 hour) Sensing technologies, perception and estimation (6 hours) Practical session on estimation and filtering (3 hours) SLAM (4 hours) Practical session on SLAM (3 hours) Autonomy in Robotics: path planning and following (4 hours) Practical session on path planning and following (3 hours) Multi-agent task allocation (3 hours) Case study: Space Robotics applications (2 hours)
Intended learning outcomes	On successful completion of this module you should be able to: 1) Examine fundamental meaning and appraise the applicability of AI methods for robotics with multiple degrees of autonomy. 2) Examine commonly used AI algorithms and develop solutions to practical examples in context of the autonomous robotics. 3) Appraise suitable methods for autonomy in common robotics applications and analyse their performance in real-life scenarios. 4) Develop and enhance the level of autonomy in robotic systems through implementation of AI algorithms, and critique the performance of the algorithm in a simulated environment.

Aim

Syllabus

Introduction to autonomy aspects (1 hour)
Sensing technologies, perception and estimation (6 hours)
Practical session on estimation and filtering (3 hours)
SLAM (4 hours)
Practical session on SLAM (3 hours)
Autonomy in Robotics: path planning and following (4 hours)
Practical session on path planning and following (3 hours)
Multi-agent task allocation (3 hours)
Case study: Space Robotics applications (2 hours)

Intended learning outcomes

On successful completion of this module you should be able to:

1) Examine fundamental meaning and appraise the applicability of AI methods for robotics with multiple degrees of autonomy.
2) Examine commonly used AI algorithms and develop solutions to practical examples in context of the autonomous robotics.
3) Appraise suitable methods for autonomy in common robotics applications and analyse their performance in real-life scenarios.
4) Develop and enhance the level of autonomy in robotic systems through implementation of AI algorithms, and critique the performance of the algorithm in a simulated environment.

Aim	As robots and robotic devices are increasingly becoming part of our day-to-day lives it is vital that we know how to effectively integrate them with people new advanced systems. This module explores the changing landscape of robotics in society and how we comply with evolving demands and perspectives of ethics and safety standards. With a focus on fundamental human factors and psychological principles we examine how to optimise the integration of people with different types of robotic systems and environments. Students will be given the opportunity to apply practical human analysis tools and techniques that complement traditional engineering approaches to enhance usability and performance in the design of safe human-robot interaction.
Syllabus	Fundamental psychology: exploring basic principles of human cognition, how we process the many types of information we encounter and make sense of it all, to understand how people interpret and respond to situations with robot systems. Human performance and error: applying knowledge of the key factors that shape human performance, decision-making and situation awareness, the typical sources of error, and practical methods of analysis and classification, to the design of human-robot systems. Social cognition and behaviour: understanding how aspects of a person’s surrounding culture and social environment can impact on their attitudes and behaviours towards robots, and on distributed cognition across ‘hybrid’ multi-agent systems. HCI and user-centred design: evaluating fundamental principles and methods for the design of human-system interfaces, displays and controls, to optimise both the functional and emotional impacts of human-robot interactions. Safety standards and ethics: appraising current formal standards and legal requirements for human-robot design in relation to the evolving landscape of robotics in society, with particular focus on ethical implications and emerging issues
Intended learning outcomes	On successful completion of this module you should be able to: 1. Examine how humans receive, process and utilise information in their interactions with robot systems 2. Analyse human factors affecting system performance and identify impacts from the surrounding environment on human-robot interaction 3. Appraise the design of systems using a user-centred approach 4. Interpret requirements of current legislation and safety standards for the design of human-robot systems and understand key ethical principles 5. Select and apply appropriate methods for analysing the design of human-robot systems and interactions across contexts

Teaching team

老司机福利社 is a leader in Applied Mathematics and Computing applications, and you will be taught by leading academic staff. The course also includes lectures by leading academics and industry specialists. External speakers include Simon Turner of ABB Robotics who is highly experienced in delivering professional robot training. The Course Director for this programme is Dr Gilbert Tang.

Accreditation

The Robotics MSc is accredited by the Institution of Mechanical Engineers (IMechE) on behalf of the Engineering Council as meeting the requirements for further learning for registration as a Chartered Engineer (CEng). Candidates must hold a CEng accredited BEng/BSc (Hons) undergraduate first degree to show that they have satisfied the educational base for CEng registration.

Your career

The MSc in Robotics is designed to equip you with the skills required to pursue a successful career working in the UK and overseas.

The International Federation of Robotics’ Executive Summary report shows that the deployment of industrial robots has constantly increased in the last decade and it is predicted that global robot installations will continue to increase over the coming years. The British Automation and Robot Association has highlighted that robotics is one of the key technologies needed to ensure competitiveness in the manufacturing sector and, according to the US Bureau of Labor Statistics (BLS), the mechanical engineering field, which includes robotics engineers, is projected to experience continued employment growth.

Typical jobs that our graduates go into include:

Robotics Engineer	Mechanical Project Engineer
Embedded Software Engineer	Researcher
Software Engineer	Automation Engineer
Test Engineer	Electrical Engineer
Consultant

Our students have been employed at the following companies:

Nissan	Edvance
Carl Zeiss	Alten
Nu Quantum	Wootzano
JLR

Cranfield’s Career Service is dedicated to helping you meet your career aspirations. You will have access to career coaching and advice, CV development, interview practice, access to hundreds of available jobs via our Symplicity platform and opportunities to meet recruiting employers at our careers fairs. Our strong reputation and links with potential employers provide you with outstanding opportunities to secure interesting jobs and develop successful careers. Support continues after graduation and as a Cranfield alumnus, you have free life-long access to a range of career resources to help you continue your education and enhance your career.

Part-time route

We welcome students looking to enhance their career prospects whilst continuing in full-time employment. The part-time study option that we offer is designed to provide a manageable balance that allows you to continue employment with minimal disruption whilst also benefiting from the full breadth of learning opportunities and facilities available to all students. The University is very well located for visiting part-time students from all over the world and offers a range of library and support facilities to support your studies.

As a part-time student you will be required to attend teaching on campus in one-week blocks, for a total of 9 blocks over the 2-3 year period that you are with us. Teaching blocks are typically run during the period from October to March, followed by independent study and project work where contact with your supervisors and cohort can take place in person or online. Students looking to study towards the MSc will commence their studies in the October intake whereas students who opt for the research-based MRes may commence either in October or January.

We believe that this setup allows you to personally and professionally manage your time between work, study and family commitments, whilst also working towards achieving a Master's degree.

My Group project focused on research around deploying a robot in a hotel foyer for check in and human interaction. For my Individual Project I focused on low latency real time 3D environment rendering for collaborative tele-robotic applications. It was an amazing experience and it taught me how to be a better researcher.

My previous experience and my passion for robots enabled me to hunt for a university that provides a master's degree which is research-intensive and industry-orientated in the field of robotics. I found 老司机福利社, which is renowned for its intensive research. I was very excited to begin my journey to study an MSc in Robotics at Cranfield.

Robotics and AI have been my passion since my formative years. The Robotics MSc structure and content offered by 老司机福利社 inspired my choice to enrol. Furthermore, I was impressed by the extensive lab facilities and the wide range of industrial connections that 老司机福利社 has.