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    Department of Automatic Control and Systems Engineering, Faculty of Engineering

    Become an expert in robotics and autonomous systems, with the skills to join the next generation of engineers. You’ll learn about machine and artificial intelligence (AI), robotic sensing and perception, control and planning and robotic devices and systems.
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    Course description

    Robotics is increasingly important to a variety of sectors, including manufacturing, healthcare and aerospace. Autonomous systems and robotics, the Internet of Things, smart grids and cloud computing are also being used more widely. 

    This course helps you develop your knowledge and skills in the key areas of robotics and autonomous systems. You'll learn about machine and artificial intelligence (AI), robotic sensing and perception, control and planning and robotic devices and systems.

    You’ll be able to apply your skills across many engineering disciplines and you’ll use industry-standard CAD and hardware tools to design and analyse mechatronic systems.

    You can choose optional modules, including working with companies on real opportunities and problems experienced by industry. 

    You'll also complete a research-level dissertation project where you will take the lead to advance your knowledge and skills in robotics.


    Accredited by the Engineering Council UK, Institution of Engineering and Technology and the Institute of Measurement and Control.


    A selection of modules is available each year - some examples are below. There may be changes before you start your course. From May of the year of entry, formal programme regulations will be available in our Programme Regulations Finder.

    Core modules:

    Data Modelling and Machine Intelligence

    All of our lives are affected by machine intelligence and data models - Google is a very visible example. But if you are a victim of identity theft, if you want a loan to buy a house or if you want to pass through immigration at an airport, a model derived from data using some form of machine learning technique will be involved.
    Engineers increasingly look to machine intelligence techniques such as neural networks and other machine learning methods to solve problems that are not amenable to conventional analysis e.g. by application of Newton's and Kirchhoff's laws, and other physical principles. Instead they use measurements of system variables to compute a model of the process that can then be used in design, analysis and forecasting. System identification is a specific example of data modelling.
    We will look at the underlying principles of machine learning, the advantages and limitations of the various approaches and effective ways of applying them with the aim of making you a competent practitioner.

    15 credits
    Foundations of Robotics

    This is an introductory module on the foundations of robotics. The aim of this module is to consolidate fundamental robotics engineering aspects, including ethical ones, as well as introduce relevant topics to those new to the discipline. The module is separated into five distinct themes:
    (a) Introduction to robotics and robot ethics
    (b) Introductory maths
    (c) Systems modelling and simulation
    (d) Control systems analysis and design
    (e) Introduction to programming

    15 credits
    Mechatronics for Robotics

    This course focuses on the methods for developing and analysing mechanical, electrical, computational, and control systems, emphasising their integration into advanced mechatronic systems. It aims to equip students with the competencies necessary to design, analyse, fabricate, integrate, and evaluate complex mechatronic systems. The curriculum includes lectures that cover the essential principles of mechatronic systems, touching on areas such as Components, Integration, and Control: system fabrication, a variety of sensors and instrumentation, actuation methods, digital data acquisition, signal preprocessing, hardware interfacing, as well as programming for microcontrollers and their peripherals. Additionally, the module features practical sessions that focus on the real use of mechatronic components. A significant component of the course is a comprehensive project, where students engage in the development and refinement of a fully functional mechatronic system.

    15 credits
    Manipulator Robotics

    The module aims to explore robotic manipulators, from theoretical concepts and modelling to practical implementations. You will be introduced to the different types and applications of robotic manipulators. An emphasis is placed on modelling and simulation. Sensing and actuation is also covered, with a focus on control of robot manipulators. You will be exposed to a wide range of practical applications of robotic manipulators, and encouraged to discuss and reflect on the implications of using robots (e.g. ethical considerations, safety, social and economic impacts), especially within manufacturing.

    15 credits
    Machine Vision

    The module gives knowledge of machine vision methods for a broad range of applications. It introduces you to image and video processing models and methods and provides you with skills on how to embed them in autonomous systems. You will be able to apply the acquired knowledge to both industrial and research areas.

    15 credits
    Mobile Robotics and Autonomous Systems

    Robotics and autonomous systems are having an increasing impact on society and the way we live. From advanced manufacturing and surgical robots to unmanned aerial systems and driverless cars, this exciting area is presenting increasing technological challenges. This module provides you with the advanced knowledge and understanding to apply control and systems engineering concepts to the closely related disciplines of robotics and autonomous systems. The module covers theoretical and technical analysis, and design aspects of mobile and manipulator robots with reference to their applications. The module further covers advanced techniques in autonomous decision making for robots and autonomous vehicles.

    15 credits
    Multisensor and Decision Systems

    The ability to use data and information from multiple sources and make informed decisions based on that data is key to many applications, e.g. manufacturing, aerospace, robotics, finance and healthcare. Through effective use of multisensory data and decision making we can reduce uncertainty, improve robustness and reliability, enhance efficiency and ultimately improve the performance of systems. In this module you will develop an in depth knowledge and understanding of multisensor and decision systems and the underlying mathematics and algorithms. You will develop your confidence in solving complex problems requiring the application of multisensory and decision techniques to a wide variety of applications.

    15 credits
    Robotics Project and Dissertation

    The aim of the project is to give you the opportunity to develop further your advanced knowledge and skills and apply these to a specific problem or set of problems. It builds on the taught modules and develops a greater level of independence. You will be allocated a project supervisor with whom you will develop the project specification and who will provide overall guidance on the project. However, you are expected to demonstrate a high level of initiative and independence. You will also develop skills in creative and critical thinking, analysis, reflection, effective project management and communication. The project is very different from many of your taught modules where the lecturer takes the lead in your learning. In the project, you are expected to take the lead and the supervisor is expected to provide overall guidance and help.

    60 credits

    Optional modules - examples include:

    Deep Learning

    An important field within artificial intelligence is machine learning, which enables systems to learn from data rather than being explicitly programmed to solve a task. Conventional machine learning algorithms tend to rely on a human to carefully engineer and extract features to present to a machine learning algorithm, which can be time-consuming and difficult. A deep learning system, by contrast, takes raw data as input and learns to extract features automatically. This approach has led to significant improvements in processing images, video, speech and audio. Deep learning has also had an impact on the design of intelligent agents, giving rise to the area of deep reinforcement learning, which is where an agent learns in a reward-based framework. An example of deep reinforcement learning is where the Google DeepMind team designed an agent that learned to play Atari computer games to better-than-human-expert level.

    15 credits
    Industrial Training Programme (ITP) in Computational Intelligence

    This module will provide an insight into advanced computational intelligence systems via industry-relevant project work. This will be in collaboration with an industrial partner. The industrial partner will set a real technical challenge and your group will undertake practical and theoretical work and present a report that will also require an in-depth literature review. To supplement the main technical challenge there will be focused technical seminars on relevant topics. These topics will be provided by both academics and industry engineers. In addition, the industrial partner will provide seminars relevant to both professional and technical skills to help you complete the project.

    15 credits

    The content of our courses is reviewed annually to make sure it's up-to-date and relevant. Individual modules are occasionally updated or withdrawn. This is in response to discoveries through our world-leading research; funding changes; professional accreditation requirements; student or employer feedback; outcomes of reviews; and variations in staff or student numbers. In the event of any change we'll consult and inform students in good time and take reasonable steps to minimise disruption.

    Open days

    An open day gives you the best opportunity to hear first-hand from our current students and staff about our courses.

    You may also be able to pre-book a department visit as part of a campus tour.Open days and campus tours


    1 year full-time


    There are lectures, seminars, tutorials, individual assignments and a major research project.


    You’ll be assessed by exams, coursework assignments and a project dissertation.

    Your career

    Our courses are informed by our strong links with industry and our world-leading research. Graduates of this course go on to work as software engineers, developers and programmers or project engineers.

    Some graduates choose to work in research or follow up their studies with a research degree.


    Department of Automatic Control and Systems Engineering

    Photo of the Amy Johnson building 2023

    We are the only department in the UK dedicated to Control and Systems Engineering.

    Our engineering graduates imagine, design and develop the advanced technologies and solutions that address big societal challenges.

    We have a diverse and vibrant community of students and staff from all over the world and we are committed to provide an inclusive and supportive learning and working environment. 

    As a student of the Faculty of Engineering you’ll be able to participate in student-led teams and societies where you can apply your engineering skills for example; SunrIde which launch rockets to record altitudes, MarsWorks who develop a Mars rover and Sheffield Bionics who create new technology for healthcare. All of this will help you develop skills for your chosen career. 

    We are home to the Rolls-Royce University Technology Centre and we’re an integral part of Sheffield Robotics and the Insigneo Institute. We have research contracts with major institutions like the European Space Agency, as well as our many academic and industrial partners. These connections mean our teaching is based on the latest thinking.

    Student profiles

    Photo of masters student Yibin Gao controlling a robot

    I've learnt about the six degrees of freedom used in industry

    Yibin Gao MSc Robotics

    Yibin is from China and knew the University of Sheffield had high quality courses. They did a pre-masters course in Science and Engineering at the University of Sheffield International College (USIC) before starting the MSc Robotics course.

    Entry requirements

    Minimum 2:1 undergraduate honours degree in an engineering or mathematics subject. We may also consider science subjects with a significant amount of programming modules.

    We also consider a wide range of international qualifications:

    Entry requirements for international students

    Overall IELTS score of 6.5 with a minimum of 6.0 in each component, or equivalent.

    Pathway programme for international students

    If you're an international student who does not meet the entry requirements for this course, you have the opportunity to apply for a pre-masters programme in Science and Engineering at the University of Sheffield International College. This course is designed to develop your English language and academic skills. Upon successful completion, you can progress to degree level study at the University of Sheffield.

    If you have any questions about entry requirements, please contact the department.


    You can apply now using our Postgraduate Online Application Form. It's a quick and easy process.

    Apply now


    +44 114 222 5644

    Any supervisors and research areas listed are indicative and may change before the start of the course.

    Our student protection plan

    Recognition of professional qualifications: from 1 January 2021, in order to have any UK professional qualifications recognised for work in an EU country across a number of regulated and other professions you need to apply to the host country for recognition. Read information from the UK government and the EU Regulated Professions Database.