Non-myopic approaches to sensing and surveying

This PhD project tackles the development of high quality but efficient multi-sensor non-myopic sensor management algorithms for controlling sensors and unmanned autonomous systems (UAS) such as unmanned aerial vehicles (UAVs) and unmanned ground vehicles (UGVs). These distributed algorithms are needed in order to exploit the ever-growing capability of autonomous systems for security, monitoring and surveying applications. The algorithms are used to make decisions on things such as where the platforms travel, what direction the sensors are pointed in, and what configuration the sensing will take. Sensor management algorithms typically use Bayesian information theoretic approaches to evaluating the "utility" or "value" of different combinations of sensor and platform actions. Even when such action combinations are evaluated over a single discrete time-step ("myopic" approaches), the problem begins to suffer from combinatorial explosion as the number of sensors is increased or the space of actions enlarges. In order to achieve high quality task choices, non-myopic approaches that consider different combinations of actions over multiple time-steps are required; these approaches are able to trade off short-term gain for higher long-term gain. Non-myopic approaches are able to identify and mitigate for real-world challenges such as obstacles (to gathering information). In general, as the number of timesteps over which the solution is optimised increases, the identified solution will approach a globally optimal solution. There is extensive literature on myopic sensor management techniques and some literature on applied non-myopic approaches. A large amount of prior and relevant work also exists in other areas of computer science and control theory such as the travelling salesman problem and receding horizon control. However, the non-myopic sensor management problem remains under-researched, in large part due to the associated computational challenges. Realised algorithms often only operate on short horizons (e.g. 2/3/4 time-steps) and as such there is still a great deal of potential to improve the effectiveness of such approaches. This project will investigate novel formulations of sensor management problems, as well as the use of Sequential Monte Carlo techniques for identifying high quality solutions. Due to the high levels of computational complexity, a key focus of the project is likely to be understanding the potential for exploiting multi-core computing hardware such as GPUs and/or FPGAs. Another area of interest is how such approaches can generate solutions as part of a human-machine teaming concept, in order that they promote trust and transparency and help end-users understand the inherent trade-offs in the optimisation process.

This CDT's focus on using "Future Computing Systems" to move "Towards a Data-driven Future" resonates strongly with two themes of non-academic organisation. In both themes, albeit for slightly different reasons, commodity data science is insufficient and there is a hunger both for the future leaders that this CDT will produce and the high-performance solutions that the students will develop.

The first theme is associated with defence and security. In this context, operational performance is of paramount importance. Government organisations (e.g., Dstl, GCHQ and the NCA) will benefit from our graduates' ability to configure many-core hardware to maximise the ability to extract value from the available data. The CDT's projects and graduates will achieve societal impact by enabling these government organisations to better protect the world's population from threats posed by, for example, international terrorism and organised crime.

There is then a supply chain of industrial organisations that deliver to government organisations (both in the UK and overseas). These industrial organisations (e.g., Cubica, Denbridge Marine, FeatureSpace, Leonardo, MBDA, Ordnance Survey, QinetiQ, RiskAware, Sintela, THALES (Aveillant) and Vision4ce) operate in a globally competitive marketplace where operational performance is a key driver. The skilled graduates that this CDT will provide (and the projects that will comprise the students' PhDs) are critical to these organisations' ability to develop and deliver high-performance products and services. We therefore anticipate economic impact to result from this CDT.

The second theme is associated with high-value and high-volume manufacturing. In these contexts, profit margins are very sensitive to operational costs. For example, a change to the configuration of a production line for an aerosol manufactured by Unilever might "only" cut costs by 1p for each aerosol, but when multiplied by half a billion aerosols each year, the impact on profit can be significant. In this context, industry (e.g., Renishaw, Rolls Royce, Schlumberger, ShopDirect and Unilever) is therefore motivated to optimise operational costs by learning from historic data. This CDT's graduates (and their projects) will help these organisations to perform such data-driven optimisation and thereby enable the CDT to achieve further economic impact.

Other organisations (e.g., IBM) provide hardware, software and advice to those operating in these themes. The CDT's graduates will ensure these organisations can be globally competitive.

The specific organisations mentioned above are the CDT's current partners. These organisations have all agreed to co-fund studentships. That commitment indicates that, in the short term, they are likely to be the focus for the CDT's impact. However, other organisations are likely to benefit in the future. While two (Lockheed Martin and Arup) have articulated their support in letters that are attached to this proposal, we anticipate impact via a larger portfolio of organisations (e.g., via studentships but also via those organisations recruiting the CDT's graduates either immediately after the CDT or later in the students' careers). Those organisations are likely to include those inhabiting the two themes described above, but also others. For example, an entrepreneurial CDT student might identify a niche in another market sector where Distributed Algorithms can deliver substantial commercial or societal gains. Predicting where such niches might be is challenging, though it seems likely that sectors that are yet to fully embrace Data Science while also involving significant turn-over are those that will have the most to gain: we hypothesise that niches might be identified in health and actuarial science, for example.

As well as training the CDT students to be the leaders of tomorrow in Distributed Algorithms, we will also achieve impact by training the CDT's industrial supervisors.