Mechatronic and Robotic Engineering MEng

Core modules:

Group Project and Engineering Skills

This module will help you develop the fundamental practical and professional skills that underpin electrical, electronic and mechatronic engineering. It will also help you develop the personal attributes essential in an engineer of any discipline. 

Five types of activities are used: —(1) lab-based activities to develop specific engineering skills and encourage the internalisation of theory;(2) an extended group project to develop an embedded system using the systems engineering approach. Students apply and develop technical and transferable skills simultaneously whilst working with partially open-ended problems; (3) address sustainability of the extended project using the UN's sustainable development goals; (4) programming skill lectures and laboratories to develop embedded programming abilities and support the extended project; and (5) a focussed, week-long, cross-faculty interdisciplinary design activity taken alongside students studying different engineering disciplines, addressing the ethical, social, economical and sustainability of solutions to engineering challenges of the 21st century. It will equip you with essential teamwork, design, problem-solving and communication skills. Particular attention will be paid to employability, sustainability, and inclusivity. Through real-life engineering projects, you will be introduced to tackling complex challenges.

The skills which you will develop include critical thinking, problem solving, adaptability in the face of unexpected challenges and professionalism. You will also develop the ability to use systems engineering approaches, to use specific pieces of hardware and software, to work effectively individually and in a group as an engineer, to approach challenges ethically and with a professional mindset, and to communicate effectively.

40 credits

Engineering Modelling and Mathematics

This module serves as an introduction to common system analysis tools and their application to simple mechatronic systems. 

You will study fundamental mathematics topics and be introduced to the first principles of modelling and system behaviour. You will focus predominantly on first-order linear systems. 

The tools you use in this module will be applied to a wide breadth of engineering applications.

20 credits

System Analysis and Machine Learning

This module is an extension of system analysis tools for application to high order, non-linear and discrete mechatronic and AI systems.

You will continue your study of fundamental, but slightly more advanced, mathematics topics. We will show you how to generalise and extend first-principles modelling and system behaviours to a broader range of systems. 

We will also introduce you to computer tools used in electrical, mechatronic and computer engineering.

20 credits

Electric and Magnetic Circuits

This module provides a comprehensive foundation in the analysis of circuits and networks, essential for any aspiring electrical engineer. You will learn both direct current (DC) and alternating current (AC) circuits, equipping you with the tools and techniques necessary to understand and solve electrical circuits and networks.

You will learn to apply fundamental circuit theorems and analysis methods to determine voltage, current, and power in various circuit configurations. We will investigate the transient and steady-state responses of first and second-order circuits, both in the time and frequency domains, providing a deep understanding of circuit dynamics. 

The second half of the module will be dedicated to magnetic circuits, including the analysis of transformers, motors, and generators, crucial components in power systems and electromechanical devices. We will discuss the interaction between electrical circuits and magnetic circuits and introduce the idea of mutual coupling and transformers. Finally, you will gain insight into the structure and operation of electrical networks, providing context for the practical application of the principles learnt throughout the module.

20 credits

Analogue and Digital Electronics

This module provides a comprehensive introduction to the fundamental principles of both digital and analogue electronics, forming the foundations for further studies in electronic engineering. We will explore the building blocks of modern electronic systems, from the logic gates that underpin digital systems to the semiconductor devices that enable analogue signal processing.

In the digital domain, you will learn Boolean algebra, apply logic manipulation techniques, and design both combinational and sequential logic networks, understanding their application in practical logic circuits. Furthermore, you will be introduced to hardware description languages (HDLs) and learn to analyse and simulate digital components and structures.

Transitioning to analogue electronics, we will introduce semiconductor materials, exploring the behaviour of diodes and semiconductor transistors. You will learn to apply circuit analysis principles to predict the behaviour of semiconductor devices in circuits. You will gain an understanding on the use of the transistor as switches and develop your ability to design and analyse transistor-based circuits. Furthermore, we will introduce operational amplifiers (op-amps), exploring their versatile applications. The module will conclude with an overview of integrated circuit manufacturing, from semiconductor boules to packaged ICs.

20 credits

Industrial and Design Projects

This module will give you the chance to apply the knowledge and skills you have acquired in your first year to engineering problems. The module has three elements: —(1) Project management approaches, taught mainly in lectures.(2) The Sheffield industrial project scheme (SHIPS) where you work on an industrial project in small groups, presenting a poster to industrial partners and your colleagues.(3) An extended design project where you apply project management and technical skills to model, design, implement and test an artefact. The list of available projects will vary from year to year, but will always encompass a range of topics from different aspects of electrical and electronic engineering.

As part of this module, you will also undertake a focussed, week-long, cross-faculty interdisciplinary design activity, aimed at equipping you with essential teamwork, design, problem-solving, and communication skills. In addition to a focus on employability, sustainability and inclusivity, you will apply more advanced engineering technical knowledge to industry-relevant, complex and interdisciplinary problems.

20 credits

Control and Communication Systems

In this module, you will learn how to model, analyse, and design both control and communication systems. You will explore how signals are represented, transmitted, received, and affected by noise, and how to calculate key performance metrics. You will also be introduced to the foundations of radio-frequency circuit analysis which is the cornerstone of modern communication architectures.  

You will also study the role of feedback in shaping dynamic system behaviour, learning to analyse stability and performance, and design controllers using classical and modern methods. Throughout the module, you will apply these concepts to practical engineering problems, integrating modelling, analysis, and design. By the end, you will have the skills to evaluate system performance, design effective solutions, and apply your knowledge to real-world communication and control challenges. 

You will gain practical experience in applying theoretical concepts in the laboratory and reporting the experimental results.

20 credits

Applied Engineering Mathematics

This module introduces advanced mathematical methods used to solve core problems in electrical, electronic, and mechatronic engineering systems.

You will learn to use mathematical techniques to analyse signals, enabling the calculation of signal power and frequency response. You will also learn to model linear systems mathematically, decomposing them into characteristic modes to calculate time evolution, determine stability conditions, and implement noise reduction strategies.

You will gain foundational skills in regression, optimisation, and probability, enabling you to develop the machine learning and artificial intelligence algorithms used in modern autonomous systems.

Finally, you will explore engineering problems in multiple dimensions through vector calculus—the natural language of fields and waves. This framework will enable you to design and analyse physical components such as transformers, transmission lines, waveguides, and semiconductor devices.

20 credits

Embedded Systems Engineering

This module covers embedded and system engineering. Systems engineering is a structured process for transforming stakeholder needs into a complete system solution through requirements analysis, design, implementation, testing and deployment. Most systems today are embedded systems and are made up of both hardware and software, underpinned by digital electronics and microcontroller-based embedded hardware. In this module, you will learn digital electronics concepts in embedded hardware and follow a software engineering approach to use them in an engineering design within a project context. 

The use of object oriented programming will be introduced, and you will write computer programs for embedded hardware. You will also study the interface between hardware and software, including how digital input/output (I/O) and common peripherals are used to connect software to real devices, and how embedded architecture affects design decisions and performance.

You will work on an embedded systems project that ties in all the concepts learnt in the module. From the outset, you will be performing system engineering inclusively, by designing and developing embedded systems in a way that is accessible, equitable and responsive to the diverse needs of users, developers and environments.

20 credits

Robot Foundations

This module delivers a comprehensive foundation in the design, intelligence and integration of modern robotic systems. The module begins with the physical aspects of robotics, examining diverse motion mechanisms, mobility modalities and the fundamental components—actuators, mechanisms and sensors—that dictate robot function. You will cover essential concepts in mechanical design and systems integration necessary for robot fabrication and assembly. You will also investigate the human element early on, covering human-robot interaction (HRI) and the broader societal and ethical implications of robotic technology. 

The focus then transitions to robot autonomy and perception. You will establish a strong grounding in computer vision, learning how visual data is processed in order for a robot to interpret its environment. This leads into the fundamentals of artificial intelligence (AI) in robotics, where you will be introduced to how modern AI techniques enable robots to perform complex tasks with advanced sensory perception.

20 credits

Robot Statics and Dynamics

Engineering statics, dynamics and biomechanics are at the heart of understanding mechatronics systems, robotics and the human body. In this module, we will cover two-dimensional statics (such as force and moment systems), dynamics (how objects and bodies move) and their application to robotic structures and the human body. The module also introduces you to the fundamental principles of robot motion from both static (kinematic) and dynamic perspectives. You will gain practical experience with the robot operating system (ROS), a platform widely used  in robotics. 

Initially, you will learn the fundamental concepts and techniques used in engineering statics and dynamics. Two-dimensional statics are covered including force and moment systems, free body diagrams, equilibrium, friction and the application to typical structures. An introduction to the use of the work-energy methods in dynamics will be given. The treatment concentrates on physical understanding and applications in robotics.

We will then introduce you to some essential concepts in machine vision for robotic control. You will learn how to process and use visual sensor data to control the motion of robotic systems in practical settings. The module follows a task-based approach, in which visual sensor data is used to design strategies for robot motion control.

Theoretical content will be delivered through lectures and the use of physical systems such as robotic arms.

20 credits

Third year core modules:

Individual Project

This module is an important project where you will demonstrate your engineering competence at bachelor level. Working individually under the supervision of a member of academic staff, you will undertake an individual project to further develop your skills and gain new project-specific knowledge through independent learning and experimentation. To do this, you will need to draw on all the skills, knowledge and experience you have acquired during your studies. 

As part of the project, you will undertake a critical evaluation of technical literature, plan and manage time and resources in your project and convey the results to an audience using a combination of written and non-written techniques appropriate to the background of the audience.

Alongside your project, you will learn the fundamentals of engineering management, finance, law and the commercial context in which industrial projects sit.

40 credits

Industrial Automation and Digitalisation

Industrial automation and digitalisation has become an important feature today, especially in this age of rapid production and high precision. Knowledge and skill in this area has therefore become increasingly necessary. 

This module provides an integrated introduction to modern industrial automation and the emerging digital technologies shaping Industry 4.0. You will develop core competencies in PLC programming, HMI design, instrumentation, and feedback control (PID tuning and implementation), with hands-on experience implementing automation solutions using industrial hardware and software tools. 

Building on these foundations, the module explores cyber-physical production systems and digitalisation in manufacturing, including virtual commissioning, digital shadows and digital twin principles.

20 credits

Machine Learning and Optimisation

Machine learning is a component of artificial intelligence that enables a computer to learn how to perform a task from data or simulations rather than being explicitly programmed for every possible scenario. Machine learning is currently being applied in a wide array of technology sectors, including robotics and autonomous systems, healthcare, bioinformatics and finance, and has experienced a huge growth in industry in recent years.  

In the first semester, the focus is on the theory and geometry of convex optimisation. You will study objective function properties, constrained and unconstrained search, and techniques for transforming complex constraints into manageable mathematical forms. In the second semester, the focus moves to the machine learning pipeline, where you will study optimisation-driven model identification and the problem of achieving good generalisation on unseen data. You will conclude the module with an analysis of the ethical issues and mitigation strategies arising from training and deploying machine learning systems.

You will study through a combination of lectures, computational laboratories, and project work, implementing optimisation and machine learning algorithms using industry-standard software, and developing practical skills needed for research and industrial environments.

20 credits

Robotic Systems

A central challenge in modern robotics is the requirement to operate effectively outside of highly controlled factory environments. Advanced robotic systems must move across complex terrain, interact with delicate objects, or operate at extreme scales. This shift necessitates the development of highly adaptable systems that integrate advanced mechanical designs with sophisticated sensing and control.

In this module, you will learn advanced design principles across four distinct robotic domains, including:-Locomotion, studying the kinematics and dynamics of limbed, underwater, and aerial systems.-Grasping and manipulation, where you will investigate robot gripper/hand design and the mechanics of contact.-Soft and biomimetic robotics, focusing on novel materials and bio-inspired movement, -Modular and micro-robotic systems, studying integration and scaling challenges in this advanced domain.

The module will be taught through a combination of lectures, computational laboratories, and systems design projects. Through these activities, you will develop the design principles and practical skills needed to model, design, integrate, control and evaluate complex mechatronic systems using industry-standard platforms.

20 credits

Third year optional modules:

Space Systems and Spacecraft Design

This module provides a foundation in space systems engineering, from the required background theory to mission requirements and spacecraft design. You will learn how mission objectives define spacecraft elements and the impacts of launch and space environments on hardware. The module includes applying orbital mechanics to derive deltaV requirements, linking trajectory physics directly to propulsion specifications. You will learn how subsystem trade-offs ensure designs are technically justified and optimised within strict constraints.

You will develop preliminary mission design budgets including mass, power and link budgets. The module covers concept-of-operation (CONOPS) and launch-system constraints. You will also evaluate non-technical factors such as international regulations and policy. 

Through theoretical examination and project-based work, you will gain the analytical tools to design viable space missions within complex engineering and regulatory frameworks.

20 credits

Digital Signal Processing

Digital signal processing (DSP) is a fundamental discipline at the intersection of mathematics, engineering and computer science that deals with the manipulation, analysis, and interpretation of signals represented in digital form. These signals can be derived from various sources such as audio, video, images, sensor data, and communication systems. At its core, DSP involves the transformation of analog signals into digital representations through a process called sampling. Once in digital form, signals can be processed, analysed, and modified using a variety of algorithms and techniques. DSP plays a crucial role in a wide range of applications including telecommunications, audio and video processing, medical imaging, radar systems and control systems.

In this module you will learn the principles of discrete-time sampled systems, including difference equations and stability. You will learn to describe digital signals and systems in different domains (e.g. time, frequency and z domains) and use a range of methods to determine the output of a digital system for a given digital input. The concept of transforms, including the fast Fourier transform (FFT), will be introduced to you. Building on these core principles, digital filter design for finite and infinite impulse response (i.e., FIR and IIR) filters and their applications are addressed, before moving on to practical implementation and testing of DSP systems. You will learn both software and hardware based implementations and their limitations. The module concludes with a look at the extension of the core principles to 2D digital signals, i.e. image processing.

20 credits

Control Systems Design

In this module, we will show you how to design, implement and evaluate modern control systems from start to finish. You will explore how to model and analyse dynamic systems using both first-principles and data-driven approaches, and how to identify system behaviour when models are imperfect. We'll introduce state feedback, observers, and Kalman filtering to help you estimate system states in the presence of noise and uncertainty. You will then learn to design advanced controllers, including linear quadratic regulators and linear model predictive control, and test them in both simulation and on a real system using hardware-in-the-loop setups. 

Throughout, we will emphasise responsible engineering practice, including considerations of safety, efficiency, resource use and environmental impact. By the end of the module, you will have the skills and confidence to take a control concept from theory through to real-world implementation and evaluation, considering both technical performance and sustainability.

20 credits

Fourth year core modules:

Group Project

The project, performed under the supervision of either two academic supervisors, or one academic supervisor and one second external marker from an industrial partner, takes the form of a multidisciplinary investigative or design project usually with a significant industrial input. Students are divided into multidisciplinary teams and presened with the project brief by the industrialists involved. Project activities are based in the research labs of the supervisor(s) although students may also have to make use of the facilities, normally within the Department. Students hold regular minuted progress management meetings with group members rotating their group management responsibilities.

45 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

- Robot Navigation
- Advanced Robotics

Fourth year optional modules:

- Reinforcement Learning
- Nonlinear Systems and Control
- Optimal and Predictive Control
- Real-time Control and Embedded Systems
- Control of Electrical Machines and Drives

Third year core modules:

Individual Project

This module is an important project where you will demonstrate your engineering competence at bachelor level. Working individually under the supervision of a member of academic staff, you will undertake an individual project to further develop your skills and gain new project-specific knowledge through independent learning and experimentation. To do this, you will need to draw on all the skills, knowledge and experience you have acquired during your studies. 

As part of the project, you will undertake a critical evaluation of technical literature, plan and manage time and resources in your project and convey the results to an audience using a combination of written and non-written techniques appropriate to the background of the audience.

Alongside your project, you will learn the fundamentals of engineering management, finance, law and the commercial context in which industrial projects sit.

40 credits

Digital Signal Processing

Digital signal processing (DSP) is a fundamental discipline at the intersection of mathematics, engineering and computer science that deals with the manipulation, analysis, and interpretation of signals represented in digital form. These signals can be derived from various sources such as audio, video, images, sensor data, and communication systems. At its core, DSP involves the transformation of analog signals into digital representations through a process called sampling. Once in digital form, signals can be processed, analysed, and modified using a variety of algorithms and techniques. DSP plays a crucial role in a wide range of applications including telecommunications, audio and video processing, medical imaging, radar systems and control systems.

In this module you will learn the principles of discrete-time sampled systems, including difference equations and stability. You will learn to describe digital signals and systems in different domains (e.g. time, frequency and z domains) and use a range of methods to determine the output of a digital system for a given digital input. The concept of transforms, including the fast Fourier transform (FFT), will be introduced to you. Building on these core principles, digital filter design for finite and infinite impulse response (i.e., FIR and IIR) filters and their applications are addressed, before moving on to practical implementation and testing of DSP systems. You will learn both software and hardware based implementations and their limitations. The module concludes with a look at the extension of the core principles to 2D digital signals, i.e. image processing.

20 credits

Machine Learning and Optimisation

Machine learning is a component of artificial intelligence that enables a computer to learn how to perform a task from data or simulations rather than being explicitly programmed for every possible scenario. Machine learning is currently being applied in a wide array of technology sectors, including robotics and autonomous systems, healthcare, bioinformatics and finance, and has experienced a huge growth in industry in recent years.  

In the first semester, the focus is on the theory and geometry of convex optimisation. You will study objective function properties, constrained and unconstrained search, and techniques for transforming complex constraints into manageable mathematical forms. In the second semester, the focus moves to the machine learning pipeline, where you will study optimisation-driven model identification and the problem of achieving good generalisation on unseen data. You will conclude the module with an analysis of the ethical issues and mitigation strategies arising from training and deploying machine learning systems.

You will study through a combination of lectures, computational laboratories, and project work, implementing optimisation and machine learning algorithms using industry-standard software, and developing practical skills needed for research and industrial environments.

20 credits

Control Systems Design

In this module, we will show you how to design, implement and evaluate modern control systems from start to finish. You will explore how to model and analyse dynamic systems using both first-principles and data-driven approaches, and how to identify system behaviour when models are imperfect. We'll introduce state feedback, observers, and Kalman filtering to help you estimate system states in the presence of noise and uncertainty. You will then learn to design advanced controllers, including linear quadratic regulators and linear model predictive control, and test them in both simulation and on a real system using hardware-in-the-loop setups. 

Throughout, we will emphasise responsible engineering practice, including considerations of safety, efficiency, resource use and environmental impact. By the end of the module, you will have the skills and confidence to take a control concept from theory through to real-world implementation and evaluation, considering both technical performance and sustainability.

20 credits

Third year optional modules:

Robotic Systems

A central challenge in modern robotics is the requirement to operate effectively outside of highly controlled factory environments. Advanced robotic systems must move across complex terrain, interact with delicate objects, or operate at extreme scales. This shift necessitates the development of highly adaptable systems that integrate advanced mechanical designs with sophisticated sensing and control.

In this module, you will learn advanced design principles across four distinct robotic domains, including:-Locomotion, studying the kinematics and dynamics of limbed, underwater, and aerial systems.-Grasping and manipulation, where you will investigate robot gripper/hand design and the mechanics of contact.-Soft and biomimetic robotics, focusing on novel materials and bio-inspired movement, -Modular and micro-robotic systems, studying integration and scaling challenges in this advanced domain.

The module will be taught through a combination of lectures, computational laboratories, and systems design projects. Through these activities, you will develop the design principles and practical skills needed to model, design, integrate, control and evaluate complex mechatronic systems using industry-standard platforms.

20 credits

Industrial Automation and Digitalisation

Industrial automation and digitalisation has become an important feature today, especially in this age of rapid production and high precision. Knowledge and skill in this area has therefore become increasingly necessary. 

This module provides an integrated introduction to modern industrial automation and the emerging digital technologies shaping Industry 4.0. You will develop core competencies in PLC programming, HMI design, instrumentation, and feedback control (PID tuning and implementation), with hands-on experience implementing automation solutions using industrial hardware and software tools. 

Building on these foundations, the module explores cyber-physical production systems and digitalisation in manufacturing, including virtual commissioning, digital shadows and digital twin principles.

20 credits

Space Systems and Spacecraft Design

This module provides a foundation in space systems engineering, from the required background theory to mission requirements and spacecraft design. You will learn how mission objectives define spacecraft elements and the impacts of launch and space environments on hardware. The module includes applying orbital mechanics to derive deltaV requirements, linking trajectory physics directly to propulsion specifications. You will learn how subsystem trade-offs ensure designs are technically justified and optimised within strict constraints.

You will develop preliminary mission design budgets including mass, power and link budgets. The module covers concept-of-operation (CONOPS) and launch-system constraints. You will also evaluate non-technical factors such as international regulations and policy. 

Through theoretical examination and project-based work, you will gain the analytical tools to design viable space missions within complex engineering and regulatory frameworks.

20 credits

Fourth year core modules:

Group Project

The project, performed under the supervision of either two academic supervisors, or one academic supervisor and one second external marker from an industrial partner, takes the form of a multidisciplinary investigative or design project usually with a significant industrial input. Students are divided into multidisciplinary teams and presened with the project brief by the industrialists involved. Project activities are based in the research labs of the supervisor(s) although students may also have to make use of the facilities, normally within the Department. Students hold regular minuted progress management meetings with group members rotating their group management responsibilities.

45 credits

Reinforcement Learning

This module aims to teach students the theory and implementation of reinforcement learning. Topics include: Supervised learning: the backpropagation algorithm (as prerequisite for Deep reinforcement learning). Reinforcement Learning: Temporal Difference Learning (Q learning, SARSA), Deep Reinforcement Learning, Advanced Topics. As well as the material taught in class, students are expected to self-study relevant books and research articles and produce reports in research article styles.

10 credits

- Nonlinear Systems and Control
- Optimal and Predictive Control
- Real-time Control and Embedded Systems

Fourth year optional modules:

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

- Robot Navigation
- Advanced Robotics