Precise characterization of geomaterials improves subsurface energy extraction and storage. Understanding geomaterial property, and the complexities between petrophysics and geomechanics, plays a key role in maintaining energy security and the transition to a net zero global carbon economy. Multiple sectors demand accurate and rapid characterization of geomaterial conditions, requiring the extraction of core plugs in the field for full-field characterization and analysis in the laboratory. We present a novel technique for the non-invasive characterization of geomaterials by using Frequency Modulated Continuous Wave (FMCW) radar in the K-band, representing a new application of microwave radar. We collect data through the delivery of FMCW wave interactions with geomaterials under static and dynamic conditions and show that FMCW can detect fluid presence, differentiate fluid type, indicate the presence of metallic inclusions and detect imminent failure in loaded sandstones by up to 15 s, allowing for greater control in loading up to a failure event. Such precursors have the potential to significantly enhance our understanding of, and ability to model, geomaterial dynamics. This low-cost sensing method is easily deployable, provides quicker and more accessible data than many state-of-the-art systems, and new insights into geomaterial behavior under dynamic conditions.

Simultaneous Localization and Mapping (SLAM) is a fundamental problem in the field of robotics, enabling autonomous robots to navigate and create maps of unknown environments. Nevertheless, the SLAM methods that use cameras face problems in maintaining accurate localization over extended periods across various challenging conditions and scenarios. Following advances in neuroscience, we propose NeoSLAM, a novel long-term visual SLAM, which uses computational models of the brain to deal with this problem. Inspired by the human neocortex, NeoSLAM is based on a hierarchical temporal memory model that has the potential to identify temporal sequences of spatial patterns using sparse distributed representations. Being known to have a high representational capacity and high tolerance to noise, sparse distributed representations have several properties, enabling the development of a novel neuroscience-based loop-closure detector that allows for real-time performance, especially in resource-constrained robotic systems. The proposed method has been thoroughly evaluated in terms of environmental complexity by using a wheeled robot deployed in the field and demonstrated that the accuracy of loop-closure detection was improved compared with the traditional RatSLAM system.

The nuclear energy sector can benefit from mobile robots for remote inspection and handling, reducing human exposure to radiation.Advances in cyber-physical systems have improved robotic platforms in this sector through digital twin (DT) technology.DTs enhance situational awareness for robot operators, crucial for safety in the nuclear energy sector, and their value is anticipated to increase with the growing complexity of cyber-physical systems.The primary motivation of this work is to rapidly develop and evaluate a robot fleet interface that accounts for these benefits in the context of nuclear environments.Here, we introduce a multimodal immersive DT platform for cyber-physical robot fleets based on the ROS-Unity 3D framework.The system design enables fleet monitoring and management by integrating building information models, mission parameters, robot sensor data, and multimodal user interaction through traditional and virtual reality interfaces.A modified heuristic evaluation approach, which accounts for the positive and negative aspects of the interface, was introduced to accelerate the iterative design process of our DT platform.Robot operators from leading nuclear research institutions (Sellafield Ltd. and the Japan Atomic Energy Agency) performed a simulated robot inspection mission while providing valuable insights into the design elements of the cyber-physical system.The three usability themes that emerged and inspired our design recommendations for future developers include increasing the interface's flexibility, considering each robot's individuality, and adapting the platform to expand sensor visualization capabilities.

Flooding associated with Hurricane Maria in 2017 had devastating consequences for lives and livelihoods in Puerto Rico. Yet, an understanding of current and future flood risk on small islands like Puerto Rico is limited. Thus, efforts to build resilience to flooding associated with hurricanes remain constrained. Here, we take an event set of hurricane rainfall estimates from a synthetic hurricane rainfall simulator as the input to an event-based rainfall-driven flood inundation model using the hydrodynamic code LISFLOOD-FP. Validation of our model against high-water-mark data for Hurricane Maria demonstrates the suitability of this model for estimating flood hazard in Puerto Rico. We produce event-based flood hazard and population exposure estimates for the present day and the future under the 1.5 and 2 ∘C Paris Agreement goals. Population exposure to flooding from hurricane rainfall in Puerto Rico for the present-day climate is approximately 8 %–10 % of the current population for a 5-year return period, with an increase in population exposure to flooding by 2 %–15 % and 1 %–20 % under 1.5 and 2 ∘C futures (5-year return period). This research demonstrates the significance of the 1.5 ∘C Paris Agreement goal for Small Island Developing States, providing the first event-based estimates of flooding from hurricane rainfall under climate change for a small island.

Since 2015, there has been an increase in articles on anomaly detection in robotic systems, reflecting its growing importance in improving the robustness and reliability of the increasingly utilized autonomous robots. This review paper investigates the literature on the detection of anomalies in Autonomous Robotic Missions (ARMs). It reveals different perspectives on anomaly and juxtaposition to fault detection. To reach a consensus, we infer a unified understanding of anomalies that encapsulate their various characteristics observed in ARMs and propose a classification of anomalies in terms of spatial, temporal, and spatiotemporal elements based on their fundamental features. Further, the paper discusses the implications of the proposed unified understanding and classification in ARMs and provides future directions. We envisage a study surrounding the specific use of the term anomaly, and methods for their detection could contribute to and accelerate the research and development of a universal anomaly detection system for ARMs.

The occurrence of manufacturing defects in wind turbine blade (WTB) production can result in significant increases in operation and maintenance costs and lead to severe and disastrous consequences. Therefore, inspection during the manufacturing process is crucial to ensure consistent fabrication of composite materials. Non-contact sensing techniques, such as Frequency Modulated Continuous Wave (FMCW) radar, are becoming increasingly popular as they offer a full view of these complex structures during curing. In this paper, we enhance the quality assurance of manufacturing utilizing FMCW radar as a non-destructive sensing modality. Additionally, a novel anomaly detection pipeline is developed that offers the following advantages: (1) We use the analytic representation of the Intermediate Frequency signal of the FMCW radar as a feature to disentangle material-specific and round-trip delay information from the received wave. (2) We propose a novel anomaly detection methodology called focus Support Vector Data Description (focus-SVDD). This methodology involves defining the limit boundaries of the dataset after removing healthy data features, thereby focusing on the attributes of anomalies. (3) The proposed method employs a complex-valued autoencoder to remove healthy features and we introduces a new activation function called Exponential Amplitude Decay (EAD). EAD takes advantage of the Rayleigh distribution, which characterizes an instantaneous amplitude signal. The effectiveness of the proposed method is demonstrated through its application to collected data, where it shows superior performance compared to other state-of-the-art unsupervised anomaly detection methods. This method is expected to make a significant contribution not only to structural health monitoring but also to the field of deep complex-valued data processing and SVDD application.

Frequency Modulated Continuous Wave (FMCW) radar is a low power, compact mechanism which can be used for non-destructive health monitoring and inspection of surface and subsurface materials.It enables the detection of defects that are internal to the analyzed structural element and not visible from the surface.The key benefits of this technology are that it offers a non-contact monitoring tool at reduced costs, risk and duration of inspection.Although a recent study proposed the use of FMCW radar sensing for composite material characterization, it has not yet been applied to the context of health monitoring.In this work, we propose to study the feasibility of FMCW radar to detect anomalies in monolithic surfaces.We propose to consider the analytic representation of the difference between the emitted signal and the incident wave to limit the interferences between the echo delay and gain difference between both signals.From a methodological perspective, we propose a complex-valued-AutoEncoder (AE) with a new activation function.We compare the proposed methodology to other state of the art methods.

Nuclear facilities have a regulatory requirement to measure radiation levels within Post Operational Clean Out (POCO) around nuclear facilities each year, resulting in a trend towards robotic deployments to gain an improved understanding during nuclear decommissioning phases. The UK Nuclear Decommissioning Authority supports the view that human‐in‐the‐loop (HITL) robotic deployments are a solution to improve procedures and reduce risks within radiation characterisation of nuclear sites. The authors present a novel implementation of a Cyber‐Physical System (CPS) deployed in an analogue nuclear environment, comprised of a multi‐robot (MR) team coordinated by a HITL operator through a digital twin interface. The development of the CPS created efficient partnerships across systems including robots, digital systems and human. This was presented as a multi‐staged mission within an inspection scenario for the heterogeneous Symbiotic Multi‐Robot Fleet (SMuRF). Symbiotic interactions were achieved across the SMuRF where robots utilised automated collaborative governance to work together, where a single robot would face challenges in full characterisation of radiation. Key contributions include the demonstration of symbiotic autonomy and query‐based learning of an autonomous mission supporting scalable autonomy and autonomy as a service. The coordination of the CPS was a success and displayed further challenges and improvements related to future MR fleets.

Robotic platforms are highly programmable, scalable and versatile to complete several tasks including Inspection, Maintenance and Repair (IMR). Mobile robotics offer reduced restrictions in operating environments, resulting in greater flexibility; operation at height, dangerous areas and repetitive tasks. Cyber Physical Systems (CPS) allow for robotics and artificial intelligence to adapt and repurpose at pace, allowing for the addressment of new challenges in CPS. A challenge exists within robotics to secure an effective partnership in a wide range of areas which include shared workspaces and Beyond Visual Line of Sight (BVLOS). Current sensing methods are limited to visual line of sight and are unable to capture data beyond doorways or walls, therefore, a robot is unable to sense if it is safe to open a door. In addition, if a robot could sense beyond a door then a robot could update safety precaution in advance of the mission e.g. when a human is about to enter a shared workspace. These challenges are represented as safety, trust and resilience which inhibits the persistent deployment of CPS. This paper evaluates the use of frequency modulated continuous wave radar sensing for through-door detection of metal, humans and infrastructure to increase situational awareness. The results validate the use of the sensor to detect an object BVOLS of a closed doorway where a methodology is presented for increased safety compliance for robots via foresight monitoring.6

Mobile robot autonomy has made significant advances in recent years, with navigation algorithms well developed and used commercially in certain well-defined environments, such as warehouses. The common link in usage scenarios is that the environments in which the robots are utilized have a high degree of certainty. Operating environments are often designed to be robot friendly, for example augmented reality markers are strategically placed and the ground is typically smooth, level, and clear of debris. For robots to be useful in a wider range of environments, especially environments that are not sanitized for their use, robots must be able to handle uncertainty. This requires a robot to incorporate new sensors and sources of information, and to be able to use this information to make decisions regarding navigation and the overall mission. When using autonomous mobile robots in unstructured and poorly defined environments, such as a natural disaster site or in a rural environment, ground condition is of critical importance and is a common cause of failure. Examples include loss of traction due to high levels of ground water, hidden cavities, or material boundary failures. To evaluate a non-contact sensing method to mitigate these risks, Frequency Modulated Continuous Wave (FMCW) radar is integrated with an Unmanned Ground Vehicle (UGV), representing a novel application of FMCW to detect new measurands for Robotic Autonomous Systems (RAS) navigation, informing on terrain integrity and adding to the state-of-the-art in sensing for optimized autonomous path planning. In this paper, the FMCW is first evaluated in a desktop setting to determine its performance in anticipated ground conditions. The FMCW is then fixed to a UGV and the sensor system is tested and validated in a representative environment containing regions with significant levels of ground water saturation.

Heterogeneous robot teams face challenges in reducing human interventions due to robot failure and environmental challenges in dangerous unstructured environments. It is therefore necessary to create a methodology to assess robot fleet failure rates to reduce the requirement for costly human intervention. A solution to this problem includes robots with the ability to work together symbiotically to overcome mission resilience challenges. However, robotic platforms generally lack built-in interconnectivity with other platforms from different vendors. This work aims to tackle this issue by enabling the functionality through a bidirectional digital twin. The twin enables the human operator to transmit and receive information to and from the heterogeneous multi-robot fleet. This digital twin considers autonomy for mission resilience and human-led decision making to enable the resilience of a multi-robot fleet. Decision-making within the digital twin triggers the symbiotic digital architecture to preserve mission continuity via adaptive planning in the robotic team. This creates the cooperation, corroboration, and collaboration of diverse robots to leverage the capability via a multi robot fleet, supporting the recovery of a failed robot. This research enables for robots to overcome resilience issues ahead of the requirement for human intervention.

Wind turbine blades (WTBs) are critical sub-systems consisting of composite multi-layer material structures. WTB inspection is a complex and labour intensive process, and failure of it can lead to substantial energy and economic losses to asset owners. In this paper, we proposed a novel non-destructive evaluation method for blade composite materials, which employs Frequency Modulated Continuous Wave (FMCW) radar, robotics and machine learning (ML) analytics. We show that using FMCW raster scan data, our ML algorithms (SVM, BP, Decision Tree and Naïve Bayes) can distinguish different types of composite materials with accuracy of over 97.5%. The best performance is achieved by SVM algorithms, with 94.3% accuracy. Furthermore, the proposed method can also achieve solid results for detecting surface defect: interlaminar porosity with 80% accuracy overall. In particular, the SVM classifier shows highest accuracy of 92.5% to 98.9%. We also show the ability to detect air voids of 1mm differences within the composite material WT structure with 94.1% accuracy performance using SVM, and 84.5% using Naïve Bayes. Lastly, we create a digital twin of the physical composite sample to support the integration and qualitative analysis of the FMCW data with respect to composite sample characteristics. The proposed method explores a new sensing modality for non-contact surface and subsurface for composite materials, and offer insights for developing alternative, more cost-effective inspection and maintenance regimes.

Subsea power cables are critical assets for electrical transmission and distribution networks, and highly relevant to regional, national, and international energy security and decarbonization given the growth in offshore renewable energy generation. Existing condition monitoring techniques are restricted to highly constrained online monitoring systems that only prioritize internal failure modes, representing only 30% of cable failure mechanisms, and has limited capacity to provide precursor indicators of such failures or damages. To overcome these limitations, we propose an innovative fusion prognostics approach that can provide the in situ integrity analysis of the subsea cable. In this paper, we developed low-frequency wide-band sonar (LFWBS) technology to collect acoustic response data from different subsea power cable sample types, with different inner structure configurations, and collate signatures from induced physical failure modes as to obtain integrity data at various cable degradation levels. We demonstrate how a machine learning approach, e.g., SVM, KNN, BP, and CNN algorithms, can be used for integrity analysis under a hybrid, holistic condition monitoring framework. The results of data analysis demonstrate the ability to distinguish subsea cables by differences of 5 mm in diameter and cable types, as well as achieving an overall 95%+ accuracy rate to detect different cable degradation stages. We also present a tailored, hybrid prognostic and health management solution for subsea cables, for cable remaining useful life (RUL) prediction. Our findings addresses a clear capability and knowledge gap in evaluating and forecasting subsea cable RUL. Thus, supporting a more advanced asset management and planning capability for critical subsea power cables.

Increasingly, high value industrial markets are driving trends for improved functionality and resilience from resident autonomous systems. This led to an increase in multi-robot fleets that aim to leverage the complementary attributes of the diverse platforms. In this paper we introduce a novel bio-inspired Symbiotic System of Systems Approach (SSOSA) for designing the operational governance of a multi-robot fleet consisting of ground-based quadruped and wheeled platforms. SSOSA couples the MR-fleet to the resident infrastructure monitoring systems into one collaborative digital commons. The hyper visibility of the integrated distributed systems, achieved through a latency bidirectional communication network, supports collaboration, coordination and corroboration (3C) across the integrated systems. In our experiment, we demonstrate how an operator can activate a pre-determined autonomous mission and utilize SSOSA to overcome intrinsic and external risks to the autonomous missions. We demonstrate how resilience can be enhanced by local collaboration between SPOT and Husky wherein we detect a replacement battery, and utilize the manipulator arm of SPOT to support a Clearpath Husky A200 wheeled robotic platform. This allows for increased resilience of an autonomous mission as robots can collaborate to ensure the battery state of the Husky robot. Overall, these initial results demonstrate the value of a SSOSA approach in addressing a key operational barrier to scalable autonomy, the resilience.

Rural communities in the Horn of Africa Drylands (HAD) rely on the availability of soil moisture for crop growth and groundwater for drinking water supply for people and livestock. Recent negative trends in March-May rainfall (‘long rains’) have decreased soil moisture with negative consequences for the livelihoods in HAD communities, who have become increasingly vulnerable to multi-season droughts affecting crops and livestock. These increasingly common failed ‘long rains’, propagate into agricultural drought, causing famines, and lead to major humanitarian intervention across HAD. However, the links between seasonal rainfall (‘long rains’ and ‘short rains’ in October-December) and regional groundwater storage in HAD have not been explored. We examined trends in seasonal rainfall from various gridded datasets alongside an analysis of total water storage (TWS) from GRACE satellite data. Multiple rainfall datasets corroborate declining ‘long rains’ and increasing ‘short rains’, and a 3-hr (MSWEP) dataset reveals the disproportionate contribution of extreme rainfall to totals within both seasons. We also found that TWS generally increased across the HAD region between 2002 and 2017, and that the GRACE TWS signal is primarily composed of groundwater storage changes for this region, rather than trends in soil moisture. We then found that groundwater storage variability correlates strongly with seasonal rainfall on interannual and decadal scales, and it is particularly correlated with extreme rainfall in both rainy seasons. We highlight the importance of increasingly large Indian Ocean Dipole events in dominating extreme rainfall and correspondingly high TWS and groundwater recharge within the October-December rainy season. While groundwater recharge in HAD by high-intensity rainfall is generally high for the March-May rainy season, it is increasing for the ‘short rains’ season. These findings raise the possibility that increasing groundwater availability across HAD could be exploited to offset the ‘long rains’ decline, potentially mitigating their climate change impacts on soil moisture, crops, and drinking water supplies.

The UK has set plans to increase offshore wind capacity from 22GW to 154GW by 2030. With such tremendous growth, the sector is now looking to Robotics and Artificial Intelligence (RAI) in order to tackle lifecycle service barriers as to support sustainable and profitable offshore wind energy production. Today, RAI applications are predominately being used to support short term objectives in operation and maintenance. However, moving forward, RAI has the potential to play a critical role throughout the full lifecycle of offshore wind infrastructure, from surveying, planning, design, logistics, operational support, training and decommissioning. This paper presents one of the first systematic reviews of RAI for the offshore renewable energy sector. The state-of-the-art in RAI is analyzed with respect to offshore energy requirements, from both industry and academia, in terms of current and future requirements. Our review also includes a detailed evaluation of investment, regulation and skills development required to support the adoption of RAI. The key trends identified through a detailed analysis of patent and academic publication databases provide insights to barriers such as certification of autonomous platforms for safety compliance and reliability, the need for digital architectures for scalability in autonomous fleets, adaptive mission planning for resilient resident operations and optimization of human machine interaction for trusted partnerships between people and autonomous assistants. Our study concludes with identification of technological priorities and outlines their integration into a new ‘symbiotic digital architecture’ to deliver the future of offshore wind farm lifecycle management.

Upscaling of the energy network represents a challenge on the resilience of the electricity network and grid to ensure that there is electricity available to meet demand. Cost-effective maintenance of high power electrical lines are of critical importance and overhead linesmen face several safety concerns during inspection of catenary lines which include working in remote areas, at height and facing adverse weather conditions. Whilst some technological advancements have been made via robotic sensing and in-situ sensing systems, these proposed solutions are inadequate for hazardous weather conditions and still include risks to workers. This article presents preliminary results obtained using a Frequency Modulated Continuous Wave (FMCW) radar sensing employed as an alternative solution for the measurement of sag in high power transmission lines. The results indicate that the FMCW radar sensor can detect multiple overhead lines within a single scan and the relevant overhead line sag. Results when compared against a laser distance meter provide an average accuracy of 95.5%.

Lung surfactant protein D (SP-D) and Dendritic cell-specific intercellular adhesion molecules-3 grabbing non-integrin (DC-SIGN) are pathogen recognising C-type lectin receptors. SP-D has a crucial immune function in detecting and clearing pulmonary pathogens; DC-SIGN is involved in facilitating dendritic cell interaction with naïve T cells to mount an anti-viral immune response. SP-D and DC-SIGN have been shown to interact with various viruses, including SARS-CoV-2, an enveloped RNA virus that causes COVID-19. A recombinant fragment of human SP-D (rfhSP-D) comprising of α-helical neck region, carbohydrate recognition domain, and eight N-terminal Gly-X-Y repeats has been shown to bind SARS-CoV-2 Spike protein and inhibit SARS-CoV-2 replication by preventing viral entry in Vero cells and HEK293T cells expressing ACE2. DC-SIGN has also been shown to act as a cell surface receptor for SARS-CoV-2 independent of ACE2. Since rfhSP-D is known to interact with SARS-CoV-2 Spike protein and DC-SIGN, this study was aimed at investigating the potential of rfhSP-D in modulating SARS-CoV-2 infection. Coincubation of rfhSP-D with Spike protein improved the Spike Protein: DC-SIGN interaction. Molecular dynamic studies revealed that rfhSP-D stabilised the interaction between DC-SIGN and Spike protein. Cell binding analysis with DC-SIGN expressing HEK 293T and THP- 1 cells and rfhSP-D treated SARS-CoV-2 Spike pseudotypes confirmed the increased binding. Furthermore, infection assays using the pseudotypes revealed their increased uptake by DC-SIGN expressing cells. The immunomodulatory effect of rfhSP-D on the DC-SIGN: Spike protein interaction on DC-SIGN expressing epithelial and macrophage-like cell lines was also assessed by measuring the mRNA expression of cytokines and chemokines. RT-qPCR analysis showed that rfhSP-D treatment downregulated the mRNA expression levels of pro-inflammatory cytokines and chemokines such as TNF-α, IFN-α, IL-1β, IL- 6, IL-8, and RANTES (as well as NF-κB) in DC-SIGN expressing cells challenged by Spike protein. Furthermore, rfhSP-D treatment was found to downregulate the mRNA levels of MHC class II in DC expressing THP-1 when compared to the untreated controls. We conclude that rfhSP-D helps stabilise the interaction between SARS- CoV-2 Spike protein and DC-SIGN and increases viral uptake by macrophages via DC-SIGN, suggesting an additional role for rfhSP-D in SARS-CoV-2 infection.

Candida tropicalisis an opportunistic fungal pathogen and is one of the most frequently isolated non-albicans species. It can cause localised as well as invasive systemic infections particularly in immunocompromised patients. Increased resistance to common anti-fungal drugs is an emerging problem. In order to establish disseminated infections, Candida has evolved several strategies to escape the host immune system. A detailed understanding of how C. tropicalis escapes the host immune attack is needed as it can help develop novel anti-fungal therapies. Secreted aspartyl proteinases (Saps) of C. albicans have been shown to be determinants of virulence and immune evasion. However, the immune evasion properties of C. tropicalis Saps have been poorly characterised. This study investigated the immune evasion properties of C. tropicalis secreted aspartic protease 1 (Sapt1).Sapt1 was recombinantly produced using a Kluyveromyces lactis yeast expression system. A range of complement proteins and immunogloublins were screened to test if Sapt1 had any proteolytic activity. Sapt1 efficiently cleaved human mannose-binding lectin (MBL) and collectin-11, which are the initiating molecules of the lectin pathway of the complement system, but not l-ficolin. In addition, Sapt1 cleaved DC-SIGN, the receptor on antigen presenting dendritic cells. Proteolysis was prominent in acidic condition (pH 5.2), a characteristic of aspartyl protease. No proteolytic activity was detected against complement proteins C1q, C3, C3b, IgG and IgA. In view of the ability of Sapt1 to cleave MBL and collectin-11, we found that Sapt1 could prevent activation of the complement lectin pathway. RT-qPCR analysis using three different C. tropicalis clinical isolates (oral, blood and peritoneal dialysis fluid) revealed relatively higher levels of mRNA expression of Sapt1 gene when compared to a reference strain; Sapt1 protein was found to be secreted by all the tested strains. Lectin pathway and its initiating components are crucial to provide front line defence against Candida infections. For the first time, we have shown that a Candida protease can proteolytically degrade the key initiating components of lectin pathway and inhibit complement activation. Findings from this study highlight the importance of exploring Sapt1 as a potential therapeutic target. We conclude that C. tropicalis secretes Sapt1 to target the complement lectin pathway, a key pattern recognition and clearance mechanism, for its survival and pathogenesis.