Overview
- Start dateOctober
- DurationMSc: Full-time one year;
- DeliveryTaught modules 40%, individual research project 40%, group project 20%
- QualificationMSc
- Study typeFull-time
- CampusCranfield campus
Who is it for?
This course provides engineering, physics or mathematics graduates with the advanced skills which can be applied to the security, defence, marine, environmental and aerospace industries.
Why this course?
This course is unique in that it offers a combination of subjects much sought after in the autonomous vehicle industry and not covered in a single MSc course anywhere else. Successful graduates of our MSc course become conversant in key aspects of autonomy which places them at an advantage in today's competitive employment market.
The Autonomous Vehicle Dynamics and Control MSc includes eight taught compulsory modules, which are generally delivered from October to March. The course begins with the fundamentals of autonomous vehicle dynamics and control, and progresses to the core subjects of guidance and navigation, sensor fusion, advanced control, decision making, AI for autonomous systems.
The taught part of the course is followed by individual research projects (IRPs) and the topic of each of the IRPs is provided by one of the member of the Industrial Advisory Board. The real-world relevance of the IRP topics is another unique feature of the MSc course and can be another effective differentiator in the job market.
Informed by industry
The relevant, competent and pro-active Industrial Advisory Board includes:
- Boeing UK
- Connected Places Catapult
- Thales
- Spirent
- Ultra Electronics
- BAE Systems
- Barnard Microsystems
- QinetiQ
- FlyLogix Limited
- Jaguar Land Rover UK
- MBDA
- General Atomics Aeronautical Systems UK
- Blue Bear Systems Research Ltd.
- Rolls-Royce
- TTTech Computertechnik
- Lockheed Martin UK
- Northrop Grumman
- BioCarbon Engineering
- QuadSAT
- HEROTECH8
Members of the Board not only continuously advise on updating the course content but also provide topics for individual research projects (IRPs). After the final oral exams in early September, all students present posters summarising their IRPs to the whole Industrial Advisory Board, thus exposing their work to seasoned professionals and potential employers. The IRPs benefit from our own lab where real autonomous vehicles can be designed and tested.
Course details
The taught course element consists of lectures in three areas: dynamics, control systems, and autonomous systems and technology. The MSc course consists of two equally weighted components, taught modules and individual research project, and a group project.
Course delivery
Taught modules 40%, individual research project 40%, group project 20%
Group project
Individual project
Our industry partners sponsor individual research projects allowing you to choose a topic that is commercially relevant and current. Topics are chosen during the first teaching period in October and you begin work during the second half of the MSc course (May-August). The project allows you to delve deeper into an area of specific interest, taking the theory from the taught modules and joining it with practical experience.
Projects encompass various aspects of operations, not only concerned with design but including payloads, civil applications, system, sensors and other feasibility studies industry wishes to explore.
For the duration of the project, each student is assigned both a university and industry supervisor. In recent years, students have been based at sponsor companies for sections of their research and have been given access to company software/facilities.
During the thesis project all students give regular presentations to the course team and class, which provides an opportunity to improve your presentation skills and learn more about the broad range of industry-sponsored projects.
Previous projects have included:
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.
Introduction to Unmanned Aircraft Systems (UAS)
Aim |
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Syllabus |
• UAS design parameters • UAS composite materials & structures • UAS: Strength of structures • UAS flight demonstration: Indoor flight • UAS components: mechanical & electrical • Overview of UAS Power & Propulsion • Overview of UAS Guidance & Navigation • Overview of UAS Sensor Fusion • Overview of Artificial Intelligence for UAS • Overview of UAS Operations • UAS flight demonstration: Outdoor flight |
Intended learning outcomes |
On successful completion of this module a student should be able to: 1) Specify the main practical applications of Unmanned Aircraft Systems (UAS) and define their engineering subsystems. |
UAS Modelling and Simulation
Aim |
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Syllabus |
• Model building; model testing, validation and management; trimming and numerical linearisation (10 hours). • Control implementation and testing on COTS FCS |
Intended learning outcomes |
On successful completion of this module a student should be able to: 1. Evaluate and implement an example UAV in terms of their aerodynamic, control, mass and inertia characteristics. Appraise and critically compare the resultant motion. |
Sensor Fusion
Aim |
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Syllabus |
• Statistical Analysis (4 lectures)
• Linear Kalman Filter and Linear Kalman Smoother (5 lectures) • Inertial navigation (3 lectures) • Constrained filters (1 lecture) • Sensor Integration architectures and Multiple sensor fusion (3 lectures) • Non-linear filters (EKF, UKF and Particle Filters) (5 lectures) • Case Study: Inertial navigation (3 lectures) • Case Study: Multiple sensor fusion (3 lectures) |
Intended learning outcomes |
On successful completion of this module a student should be able to: 1. Understand the fundamental principles in stochastic processes and in estimation theory. |
Artificial Intelligence for Autonomous Systems
Aim |
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Syllabus |
• AS sensors and imaging (4 lectures) • Demo: Indoor autonomous surveillance (2 lectures) • AI Algorithms: Supervised Learning―Neural Networks (7 lectures) • AI Algorithms: Supervised Learning―Lab (3 lectures) • AI Algorithms: Unsupervised Learning―Clustering (3 lectures) • Automated Reasoning: Bayesian Networks (2 lectures) • Relevance Filtering: Visual Attention (2 lectures) • Case Study: AI for AS (4 lectures) |
Intended learning outcomes |
On successful completion of this module a student should be able to: 1) Formulate the fundamental meaning and applicability of Artificial Intelligence (AI) algorithms for Autonomous Systems (AS). |
Guidance and Navigation for UAS
Aim |
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Syllabus |
• Introduction on navigation and guidance systems;
• Path planning for autonomous systems • Path following for autonomous systems • UAV (Unmanned Aerial Vehicle) guidance systems; • Guidance approaches: conventional guidance such as PN (Proportional Navigation), geometric guidance, and optimal guidance; • Navigation approaches: navigation systems, GNSS (Global Navigation Satellite System), terrain based navigation, SLAM (Simultaneous Localisation and Mapping); • Cooperative guidance and collision avoidance. |
Intended learning outcomes |
On successful completion of this module a student should be able to: 1. Evaluate the fundamentals of the various guidance techniques and their properties. |
Autonomous Vehicle Control Systems
Aim |
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Syllabus |
• Overview of autonomous vehicles performance requirement;
• System identification and state-space modelling; • Uncertainty representation; • Pole placement and gain scheduling; • H-infinity control: loop shaping approach and LMI approach; • Optimal and non-linear control system design and its application to autonomous vehicles; • Gain scheduling |
Intended learning outcomes |
On successful completion of this module a student should be able to: 1. Appraise the nature, purpose, design procedure of advanced control systems for autonomous vehicles; |
UAS Dynamics and Control
Aim |
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Syllabus |
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Intended learning outcomes |
On successful completion of this module a student should be able to:
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Logic and Automated Reasoning
Module Leader |
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Aim |
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Syllabus |
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Intended learning outcomes |
On successful completion of this module a student should be able to: 1. Analyse syntax and semantics of first-order logic and different knowledge representation using the appropriate formal language. 2. Use logical programming to build knowledge bases. 3. Design strategies to solve assigned reasoning problems detailing the execution steps to perform the given tasks. |
Teaching team
You will be taught by Cranfield's experienced academic staff. Our staff are practitioners as well as tutors, with clients which include the members of the Industrial Advisory Board and beyond. Knowledge gained working with our clients is continually fed back into the teaching programme, to ensure provision of durable and transferrable skills practised on problems relevant to industry. Additionally, experienced members of the Industrial Advisory Board deliveriIndustrial seminars in which they share their experience and explain the research and development proprieties of their companies. The Course Director for this programme is Dr Argyrios Zolotas.
This MSc course was interesting and coherent, and the cutting edge technology tailored towards aeronautical industry’s upcoming developments. I had the opportunity to experience new challenges and work on impressively ambitious projects which were highly rewarding. It provided me with the skills and knowledge I needed to start my career, and I found the job I wanted immediately after completing the MSc. Moreover, with brilliant staff and outstanding facilities, Cranfield is a wonderful place to study.
Autonomous Vehicle Dynamics and Control is a very unique course. It has a lot of interesting areas. In particular, the cutting edge technology. This course will definitely help me in my future career. Cranfield is a very good learning environment.
The overall experience has been really positive, and has provided me with the needed tools to find a job that I love and where I can put forth all the knowledge that I gained during the MSc.
Your career
The industry-led education makes Cranfield graduates some of the most desirable all over the world for recruitment by companies competing in the autonomous vehicle market including:
- BAE Systems,
- Defence Science and Technology Laboratory,
- MBDA,
- Other companies from our Industrial Advisory Board.
Graduates from this course will be equipped with the advanced skills which could be applied to the security, defence, marine, environmental and aerospace industries. This approach offers you a wide range of career choices as an autonomous systems engineer, design engineer or in an operations role, at graduation and in the future. Others decide to continue their education through PhD studies available within Cranfield University or elsewhere.
How to apply
Online application form. UK students are normally expected to attend an interview and financial support is best discussed at this time. Overseas and EU students may be interviewed by telephone.