Unmanned systems are growing fast, and there is an urgent need to improve the robustness and efficiency of such systems. Quadrotors are one prime example, which can be used in a variety of different domains. This includes infrastructure inspection, disaster management, search and rescue, precise agriculture, and package delivery. The government has shown a huge interest in autonomous vehicles. The release of the Future of Transport: rural strategy highlights the opportunities for drones to make deliveries in rural or isolated towns and to help reduce pollution. Furthermore, reports have shown the self-driving vehicle industry to be worth nearly £42 billion by 2035. Autonomous vehicles rely on highly accurate localization and mapping techniques which can be very difficult in cluttered and dynamic scenes. Dead-reckoning based methods which rely on previous estimates work in these scenarios but fall victim to propagated error which leads to inaccuracies in the long run. This has led to research in the loop closure which utilizes previously seen landmarks to re-localize the vehicle. The most common form of self-localization within autonomous vehicles comes from Simultaneous Localization and Mapping, which is a technique that utilizes detected landmarks and control inputs to estimate the position and orientation of the vehicle within a generated map. The assumption of static landmarks however still provides an issue within the previously mentioned dynamic environments, as static landmarks are needed to be filtered from dynamic landmarks. Dynamic-SLAM methods modify the existing method by providing this filtering technique but still lack robustness when dynamic objects fill up the majority of the environment. We hope to tackle this problem using data-driven approaches. Reinforcement learning has been shown as a viable solution for navigation within mapless and dynamic environments. We hope to train the reinforcement learning agent, through a series of simulation environments, the ability to navigate in a dynamic and cluttered environment using onboard camera depth sensors. Building on work already done but that would not have been able to take place during the PhD. An experimental quadrotor has already been developed and we hope to utilize this within Ryerson University's drone arena to validate the proposed hypothesis. The key outputs of this project will be the development of reinforcement learning techniques to navigate within a mapless environment to aid with the mapping process in a dynamic scene. This novel technique provides an alternative solution to the current advances in dynamic-SLAM. We hope that reinforcement learning-based techniques will improve dynamic-SLAM's ability to be utilized. Furthermore, such a technical solution can be easily applied to industrial applications and is supposed to, in practice, fill the gap between autonomous control and popular artificial intelligence techniques We believe that the proposed research brings the strength of robotics research from our partners in Canada to significantly improve the accessibility of AI techniques in autonomous robotics, and further strengthen the UK's role as the global leader in the creation of industrial autonomy solutions. Such a role aligns with the current UK research roadmap, with at least £800 million to ensure the UK can gain a competitive advantage in the creation of artificial intelligence and industrial autonomy.

Data capture via imaging and sensing has become a common aspect of our existence and helps extend human vision and perception. Whether it is a microscope used for cell counting or the latest version of autonomous vehicle which aims to see through the fog; the sensing apparatus is expensive and limited in functionality. For example, the cameras of a self-driving car may white out due to exposure to excessive light when coming out of a tunnel. In many of applications, hardware (that captures data) and algorithms (which recover meaningful information from data) are treated decoupled entities; first capture data, extract information later. Hence, there is a limit to what can be recovered from the data based on the limitations of the hardware. Can we go beyond such limitations? The purpose of this research is to achieve a synergistic balance between hardware and algorithms by means of a co-design, so that popularly held limits in data capture and imaging can be broken, thus making the invisible, visible. Questions that we seek to answer include: Can we do bio-imaging with low-cost sensors (e.g. Microsoft Kinect)? Can we capture information beyond the usual dynamic range? Can we non-invasively classify blood cells by inferring cell geometry? Can we remove reflections in photographs? Can we see through diffusive media? These questions require us to go beyond the conventional barriers (e.g. dynamic range, spatio-temporal resolution, how fast the data is captured etc). The work in this proposal relies a co-design approach where carefully optimized capture process yields computationally encoded measurements from which the information is decoded using recovery algorithms. This approach is used to modify hardware and develop new algorithms to recover information. Application areas span from bio-imaging (cell-classification, fluorescence lifetime imaging, terahertz spectroscopy), consumer imaging (autonomous vehicles) to conceptualization of new sensing hardware. Three specific barriers are considered: (1) Dynamic Range Barrier. We propose the use of recording measurements that are non-linearly mapped by modulo operations. This is a fundamentally new way of sensing or digitising information and is largely unexplored. Our initial work shows that a simple correction to the Nyquist rate linked with Shannon's sampling theory allows for recovery of a bandlimited signal from modulo information. Remarkably, the sampling bound is independent of the the threshold. In this proposal we study a larger class of signals including sum-of-sinusoids, sparse signals and smooth signal and their link with application areas such as direction-of-arrival estimation and beamforming. (2) Resolution Barrier. Recovering spikes from low-pass filtered measurements is a classical problem and is known as super-resolution. However, in many practical cases of interest, the pulse or filter may be distorted due to physical properties of propagation and transmission. Such cases can not be handled well by existing signal models. Inspired by problems in spectroscopy, ground penetrating radar, photoacoustic imaging and ultra-wide band arrays, on which we base our experiments, in this work we take a step towards recovering spikes from time-varying pulses and prepare algorithms for non-ideal super-resolution. Furthermore, when the pulse or filter is smooth and not necessarily bandlimited, optimial bandwith selction for sparse-deconvolution is an open problem that is addressed in this work. (3) Bandwidth Barrier. We define the notion of bandwidth in context of Special Affine Fourier transforms which generalises a number of well known transformations. This allows us to prepare a unifying approach for studying sampling theory which is applicable to a wider class of signal models. Our algorithms are validated on experimentally acquired data with the help of inter-disciplinary and multi-university collaborations

AHRC: Natalie Ilsley: AH/L503903/1 This 3-month project will be realized at Ryerson University in Toronto, under the supervision of Professor Irene Gammel. The project aims to contribute to the study of trauma by using resilience as an analytical lens. It will offer theoretical and context-informed understandings of marginalized narratives by drawing on my expertise in the arts, humanities and the political sciences. The project will, therefore, generate fresh theoretical understandings of trauma and resilience thinking; facilitate analysis and enrich awareness of marginalized narratives as cultural and literary responses to global pandemics; and enhance the capacity for interdisciplinary research and collaborations in the UK and Canada. The expected outcomes are as follows: a co-written journal article, a virtual network for postgraduates and early career researchers, and a series of creative outputs.

EPSRC : Charles Gillott : EP/R513313/1 In an attempt to limit global temperature increase to 1.5 degrees C, the UK is committed to reducing greenhouse gas emissions to net-zero by 2050. The built environment is currently responsible for over 40% of the UK's greenhouse gas emissions, and is its largest contributor of waste. An increasing proportion of built emissions come from embodied carbon, referring to emissions from the extraction and manufacture of materials; construction, maintenance and demolition of buildings; and the processing and disposal of waste. A circular economy (CE) attempts to reduce resource consumption and waste generation - and thus embodied carbon - by retaining materials at their highest level of usefulness for as long as possible. Increasingly, policy promoting a CE is being seen across the globe, with strategies to achieving this including the retention of existing buildings, reuse of components, and recycling of materials. This project will identify policies that are promotive of CE in the Canadian and UK built environments, including those that apply at the national (e.g. building regulations in the UK and national construction codes in Canada) and sub-national level (e.g. local authority or provincial planning requirements). How policies promote different CE strategies will be assessed, allowing the homogeneity and characteristics of the policy landscape to be compared within and across the two countries. Building upon this, the success of different policy instruments in influencing construction practice will be considered. This will result in cross contextual learnings, and the formation of recommendations to increase adoption of a CE in the Canadian and UK built environments.

This project studies development of high power DC transmission networks. There is currently significant interest in developing technologies that will enable interconnection of distributed DC sources to DC networks in multi MW power sizes. The application fields include offshore renewable power parks, North Sea Supergrid, subsea power supplies in oil industry and many more. A medium power DC network test rig will be developed at Aberdeen University which will include DC transformers and fault isolation components. The project will investigate efficient, light-weight DC transformer topologies that will enable cost-effective power exchange between DC systems at wide varying voltage levels. The DC test rig will enable practical testing of DC circuit breaker which will be one of the crucial enabling technologies for DC networks. The project further investigates the operational and control principles of future large DC power networks. This project strengthens collaborative links between University of Aberdeen and Ryerson University LEDAR laboratory.