Thales-Bristol Partnership in Hybrid Autonomous Systems Engineering
Building on their Memorandum of Understanding (2013), Thales and Bristol formalised and expanded their collaboration in The Thales Bristol Strategic Agreement (TBSA) which focusses on the key challenges that confront modern engineering. It sets out a framework for interdisciplinary collaboration that organises state-of-the-art integrative research efforts around a set of Thales use cases that, between them, represent the future of autonomous systems engineering in private sector, public sector, defence, blue-light and civilian settings, both in terms of physical systems, e.g., robots or drones, and information systems, e.g., smart networks and autonomous control systems. The TBSA has established engagement between Bristol and all levels of Thales in terms of detailed strategy and asset sharing, intellectual property and patents, sabbatical exchanges and embedded researchers, studentship support, and opportunities for leveraging research funding. It represents an ideal platform upon which to pursue T-B PHASE, a radical research programme addressing fundamental challenges that must be overcome for UK engineering to operate confidently within the emerging design space of hybrid autonomous systems: where engineered autonomous systems interoperate effectively with natural autonomous systems (individual people, crowds, human-controlled and tele-operated systems) in environments that are open and evolving.
The T-B PHASE Prosperity Partnership will co-locate at Bristol a world-class, diverse, and strongly integrated team of Bristol and Thales researchers spanning the disciplines of computer science, artificial intelligence, engineering mathematics, and robotics with specific expertise in autonomous systems engineering to address the Hybrid Autonomous Systems Engineering “R3 Challenge”: Robustness, Resilience, Regulation.
T-B PHASE Summary
Hybrid autonomous systems are those where groups of people are in direct, ongoing interaction with groups of autonomous robots or autonomous software.
One prominent current example involves rush-hour traffic made up of a mixture of cars driven by people and cars driven by smart algorithms. However, emerging technologies in robotics, AI and ICT mean that hybrid autonomous systems of this kind will become increasingly common in a much wider set of situations:
Emerging technologies in robotics, AI and ICT mean that hybrid autonomous systems of this kind will become increasingly common in a much wider set of situations:
- a mixture of autonomous and human-operated drones making deliveries or monitoring public spaces;
- a mixture of human traders and autonomous trading agents buying and selling stocks;
- a mixture of autonomous and human-operated trains and trams providing efficient, integrated public transport;
- autonomous systems assisting with search and rescue missions in disaster areas that are difficult or dangerous to access;
- robot carers assisting care workers with the provision of social care in the home
In each of these cases smooth, reliable, safe interaction amongst machines and people will be key to success. But how can we guarantee that self-driving cars won't cause a crash or gridlock? How can we understand how autonomous systems will respond to new situations (both acute shocks and long-term gradual changes in their environment), or changes in the way that people interact with them? Consequently, as we enter this new design space, a crucial challenge for the engineers of hybrid autonomous systems across these settings is ensuring that the system behaviour is Robust and Resilient and that it meets Regulatory demands: the R3 Challenge.
T-B PHASE directly addresses this R3 Challenge for Hybrid Autonomous Systems Engineering, by bringing together expertise in robotics, AI, and systems engineering at the University of Bristol and Thales in a five-year project that targets fundamental autonomous system design problems in the context of three real-world Thales use cases: Hybrid Low-Level Flight, Hybrid Rail Systems, and Hybrid Search & Rescue.
Bristol and Thales have a long-standing track record of research collaboration, and by jointly pursuing fundamental research questions in the context of highly practical design problems, alongside a programme of engagement with industry, the public and regulatory bodies, T-B PHASE will significantly advance our capability to operate confidently in one of the most important emerging areas for modern engineering.
The business benefits of T-B PHASE are:
- Liability and responsibility for the behaviour of complex systems strongly inhibits the deployment of hybrid autonomous systems in the real world. By embedding Robustness, Resilience and Regulation as part of the development life cycle, T-B PHASE will provide those who commission and develop hybrid autonomous systems with tools that enable early-stage evaluation and demonstration in the development lifecycle.
- T-B PHASE will accelerate the adoption of new hybrid autonomous systems by reducing the costs of development, the risks of deployment, and the length of the development life-cycle. Current approaches involve exhaustive real-world validation and verification campaigns that are not scalable or sustainable for systems with emergent properties.
- Public and consumer acceptance of autonomous hybrid systems is currently fragile. T-B PHASE will improve the transparency of system Robustness and Resilience, which is an essential aspect of building acceptance and evolving Regulation frameworks that are suitable for hybrid autonomous systems.
- Developing a critical mass of skilled researchers in autonomous systems engineering will benefit multiple relevant sectors in the UK, stimulating further technology development in this area and creating longer term strategic benefits for the UK engineering sector.
- T-B PHASE will contribute to advancing the development of regulatory frameworks for autonomous systems, which require attention to what can be assured at design stage and what can be assured post-deployment through online monitoring and adaptation.