Mission Planning for Long-Term Deployment using Probabilistic Environment Models

1 Brief description of the content of the research including potential impact

As mobile robots become more advanced and affordable, they are being deployed in increasingly complex monitoring tasks in a variety of environments, from agricultural croplands to oceans and rainforests. In many cases, robots offer enhanced opportunities for environment modelling compared with fixed sensors, as they can cover more ground and remove the need for permanent installation of sensors in the environment. However, this leads to the question of how the robot should decide where to go to acquire the most useful information. My research will aim to develop algorithms that improve the capabilities of robotic monitoring systems, with potential applications in agriculture, ecology, renewable energy and many other areas.

2 Aims and objectives

The aim of the proposed work is broadly to improve the efficiency of robotic monitoring systems, enabling them to provide more useful information within their operating constraints. Particular objectives to be addressed will include:

1. Designing a framework for spatiotemporal modelling that can be used by robots to capture a rich representation of their environments. A robot deployed in a monitoring context must be able to model its environment in a way that can usefully inform its planning system.

2. Demonstrating efficient informative planning with a rich underlying environment model. Existing work on persistent modelling and informative planning has typically used fairly simple environment models. We look to extend these capabilities to the richer models described above.

3. Incorporating practical constraints such as battery life in an integrated manner, to allow the system to proactively plan around its need to charge.

3 Novelty of the research methodology

Robotic technologies have developed dramatically in recent years and continue to do so, and as such
research into robotic monitoring still presents many avenues for further exploration. Much work has been done on the algorithmic foundations of this work, but we hope to both extend these foundations and - importantly - validate them through deployment on real robots, as well as through simulation testing.

4 Alignment to EPSRC strategies and research areas

The proposed work aligns strongly with the Robotics research area described by the EPSRC. This
research area acknowledges the potential for robotics research to contribute in the near future to
disruptive technologies in many major industrial sectors, including automotive, aerospace, nuclear,
oil and gas, agriculture, space, manufacturing, defence and construction. This research area, and the
proposed work, are also strongly connected with the Artificial Intelligence Technologies area,
within which one of the focuses is toward development of autonomous and intelligent technologies.

5 Companies or collaborators involved

The proposed work is supported by Amazon Web Services (AWS)

AIMS's impact will be felt across domains of acute need within the UK. We expect AIMS to benefit: UK economic performance, through start-up creation; existing UK firms, both through research and addressing skills needs; UK health, by contributing to cancer research, and quality of life, through the delivery of autonomous vehicles; UK public understanding of and policy related to the transformational societal change engendered by autonomous systems.

Autonomous systems are acknowledged by essentially all stakeholders as important to the future UK economy. PwC claim that there is a £232 billion opportunity offered by AI to the UK economy by 2030 (10% of GDP). AIMS has an excellent track record of leadership in spinout creation, and will continue to foster the commercial projects of its students, through the provision of training in IP, licensing and entrepreneurship. With the help of Oxford Science Innovation (investment fund) and Oxford University Innovation (technology transfer office), student projects will be evaluated for commercial potential.

AIMS will also concretely contribute to UK economic competitiveness by meeting the UK's needs for experts in autonomous systems. To meet this need, AIMS will train cohorts with advanced skills that span the breadth of AI, machine learning, robotics, verification and sensor systems. The relevance of the training to the needs of industry will be ensured by the industrial partnerships at the heart of AIMS. These partnerships will also ensure that AIMS will produce research that directly targets UK industrial needs. Our partners span a wide range of UK sectors, including energy, transport, infrastructure, factory automation, finance, health, space and other extreme environments.

The autonomous systems that AIMS will enable also offer the prospect of epochal change in the UK's quality of life and health. As put by former Digital Secretary Matt Hancock, "whether it's improving travel, making banking easier or helping people live longer, AI is already revolutionising our economy and our society." AIMS will help to realise this potential through its delivery of trained experts and targeted research. In particular, two of the four Grand Challenge missions in the UK Industrial Strategy highlight the positive societal impact underpinned by autonomous systems. The "Artificial Intelligence and data" challenge has as its mission to "Use data, Artificial Intelligence and innovation to transform the prevention, early diagnosis and treatment of chronic diseases by 2030". To this mission, AIMS will contribute the outputs of its research pillar on cancer research. The "Future of mobility" challenge highlights the importance the autonomous vehicles will have in making transport "safer, cleaner and better connected." To this challenge, AIMS offers the world-leading research of its robotic systems research pillar.

AIMS will further promote the positive realisation of autonomous technologies through direct influence on policy. The world-leading academics amongst AIMS's supervisory pool are well-connected to policy formation e.g. Prof Osborne serving as a Commissioner on the Independent Commission on the Future of Work. Further, Dr Dan Mawson, Head of the Economy Unit; Economy and Strategic Analysis Team at BEIS will serve as an advisor to AIMS, ensuring bidirectional influence between policy objectives and AIMS research and training.

Broad understanding of autonomous systems is crucial in making a society robust to the transformations they will engender. AIMS will foster such understanding through its provision of opportunities for AIMS students to directly engage with the public. Given the broad societal importance of getting autonomous systems right, AIMS will deliver core training on the ethical, governance, economic and societal implications of autonomous systems.