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Robotics and Autonomous Systems (RAS) are being increasingly used as elements in safety-critical applications in a variety of domains. These technologies provide many challenges to current system safety engineering methods and assurance techniques. In this module, we will identify the nature of the safety challenges - technical, engineering and social - posed by RAS and consider their implications for legislation and regulatory guidance for engineering practice.
In this module, we will consider the challenges posed to safety engineering techniques and praxis by Robotics and Autonomous Systems in three broad areas:
By the end of this module, students will be able to:
This course is suitable for:
A basic understanding of system safety terminology and lifecycle via prior learning or industrial experience. It is useful for you to have taken our Foundations of System Safety Engineering and Computers and Safety courses, but if you have not, please email us with your details so we can assess your suitability for taking this course.
No prior knowledge of RAS is required for this module - we will provide an introduction to the technologies sufficient for understanding of the safety aspects during the module.
The course takes place over one week at the University of York. This week consists of a mixture of lectures and practicals, but we expect you to put in around 30 hours of private study.
Over the week, there will be a series of lectures and a number of case studies. The case studies give you the chance to work through an example to reinforce your learning from the lectures. This is also a chance to gain other insights from the experience and knowledge of other delegates. You will also be able to call on the experience and knowledge of our specialised teaching staff during these sessions.
The module ends with an assessed exercise, which you have the option of completing. It takes approximately 35 hours in addition to the scheduled teaching time and can be completed on or off site. All assessed exercises are open (so you won't take an exam in supervised conditions), and comprise a report, case study, or documented piece of software.
If you choose to take and pass your assessment, your results can count towards the completion of the MSc in Safety Critical Systems Engineering. Our MSc in Safety Critical Systems Engineering is an accredited course, recognised by both the BCS, the Chartered Institute for IT and the Institution of Engineering and Technology (IET) for the purposes of partial fulfilment of the educational requirement for CEng registration.
|Russell, S.J. and Norvig, P.||Artificial Intelligence: a modern approach||Malaysia: Pearson Education Limited||2016|
|Marcus, G. and Davis, E.||Robooting AI: Building artificial intelligence we can trust||Pantheon||2019|
|Goodfellow, I. et al||Deep learning (Vol 1)||Cambridge: MIT press||2016|
|Géron, Aurélien.||Hands-on machine learning with Scikit-Kearn, Keras, and TensorFlow: Concepts, tools and techniques to build intelligent systems||O'Reilly Media||2019|
|Topol, Eric||Deep medicine: how artificial intelligence can make healthcare human again||Hachette UK||2019|
|Liu, Yun et al||"How to read articles that use machine learning: users' guides to the medical literature"||Jama 322 18||2019|
|Chen, Po-Hsuan Cameron, Yun Liu and Lily Peng||"How to develop machine learning models for healthcare"||Nature materials 18.5:410||2019|
|Assuring Autonomy Body of Knowledge||https://www.york.ac.uk/assuring-autonomy/body-of-knowledge/|
Due to the Corvid-19 situation, teaching for the Autumn term will take place online with a combination of pre-recorded lectures and live exercises. The next ADTS instance will commence w/c 5th April 2021 and live exercises will take place w/c 19th April 2021.
Before booking, please read our Booking Conditions (PDF , 104kb).
If you have any queries, please contact Heather Taylor, our course administrator, or call 01904 325536.