Cap and pin insulators are widely used in electricity transmission towers in power systems and are one of their key functional elements. This paper reports on a successful application of using electric field as an index to detect and locate shorted insulator units in a cap and pin string. Electro-optical (EO) probes were used to measure the electrical field, and a six axis robotic arm, holding the probes, enabled electrical field mapping at different spatial locations. The electric field distribution was first plotted according to the measurements performed by EO probes travelling along straight lines parallel to the string, and then a closer examination between insulator sheds was conducted to evaluate the possibility of higher accuracy locating of shorted insulator units. The measured changes in electric field distribution were compared with predictions from corresponding electrostatic finite element analysis (FEA) models, confirming the ability of locating shorted units in the string based on observation of the field spatial distribution change. The electric field monitoring system was also further developed to achieve operator-free and automated data acquisition and recording. The reported methodology exhibits promising potential in live condition monitoring of insulator strings in high voltage (HV) towers.

A mobile robot platform was developed with on-board, stand-off LIBS and Raman probes as part of a broader system for in situ 'total characterisation' in nuclear environments. This includes gamma spectrometry and 3D imaging via LIDAR and photogrammetry. All characterisation techniques were guided by a 3D point-cloud model generated from the robot's imaging systems, enabling precise positioning near target zones. The LIBS probe operated at a stand-off distance of 10 cm and successfully detected lead in a single shot, with signal quality comparable to benchtop instruments. It also distinguished metals such as stainless steels, nickel, and Incoloy using principal component analysis. The Raman probe used a collimated laser beam and acquired spectra from several metres away. It identified organic materials common in decommissioning environments, including plastics and EDTA, and differentiated concentrations of dibutyl and tributyl phosphate in low-odour kerosene, organophosphates relevant to uranium and plutonium removal. Both probes were tested in a simulated hot cell environment, operating entirely on battery power. Contamination detection of non-radioactive analogues of radioactive decay products like strontium and caesium on stainless steel and cement was demonstrated, supporting clean-up and disposal operations and decision making. Spectral, spatial, and radiological data are integrated into a database that updates a digital twin, enabling layered visualisation in a virtual environment. The robot can be deployed for periodic surveys, such as annual inspections of disused hot cells, to monitor environmental degradation. This data-driven approach supports auditable decision-making for waste disposal and remediation priorities.

Positioning of underwater robots in enclosed or congested aquatic environments remains unsolved for field operations. Existing systems are generally suited for use in large open marine environments, not the enclosed and congested aquatic facilities common to industrial settings. In such environments, existing systems can suffer from strong echoes, multipath effects, poor coverage, and the reliance on new infrastructure and an assortment of features. Accurate and readily deployable positioning is a prerequisite for performing repeatable autonomous missions. The Collaborative Aquatic Positioning (CAP) system presented in this article uses a mixture of collaborative robotics and sensor fusion to solve this problem. The proposed positioning system is deployed in a large water tank, and repeatable autonomous missions are performed. Experimental results show that the system achieves real-time performance with mean Euclidean distance (MED) errors of 85.5-123.4 mm.

Positioning of underwater robots in confined and cluttered spaces remains a key challenge for field operations. Existing systems are mostly designed for large, open-water environments and struggle in industrial settings due to poor coverage, reliance on external infrastructure, and the need for feature-rich surroundings. Multipath effects from continuous sound reflections further degrade signal quality, reducing accuracy and reliability. Accurate and easily deployable positioning is essential for repeatable autonomous missions; however, this requirement has created a technological bottleneck limiting underwater robotic deployment. This paper presents the Collaborative Aquatic Positioning (CAP) system, which integrates collaborative robotics and sensor fusion to overcome these limitations. Inspired by the "mother-ship" concept, the surface vehicle acts as a mobile leader to assist in positioning a submerged robot, enabling localization even in GPS-denied and highly constrained environments. The system is validated in a large test tank through repeatable autonomous missions using CAP's position estimates for real-time trajectory control. Experimental results demonstrate a mean Euclidean distance (MED) error of 70 mm, achieved in real time without requiring fixed infrastructure, extensive calibration, or environmental features. CAP leverages advances in mobile robot sensing and leader-follower control to deliver a step change in accurate, practical, and infrastructure-free underwater localization.

Results obtained from characterizing the capabilities of a collimated cerium bromide (CeBr3) detector developed for use on robotic platforms are reported. The detector's field-of-view is collimated by a lead slit, and experiments have been performed with point radiation sources configured in a-priori known geometric scenarios. The collimated detector is scanned over radiation sources using gimbal control, acquiring a spectrum at each angle to obtain energy-resolved angular responses. These responses are then approximated by mathematical transforms to enhance localization accuracy. Various combinations of radionuclides have been used to demonstrate the effectiveness of this approach. The results reveal that expressing X-ray and gamma-ray angular responses with an appropriate transform improves the resolving power of the slit collimator. This technique offers distinct advantages in robot deployments by enabling additional in-situ characterization capabilities without imposing additional limitations or requirements.

Composite insulators are widely adopted in power systems as they are vital transmission network assets, thanks to their beneficial properties such as lightweight, simple maintenance and deployment, and hydrophobicity. Unlike traditional inspection methods, such as visual inspection, monitoring the changes in the electric field profile could be a non-invasive aging indicator to assess the insulation integrity of composite insulators. In this paper, the changes in the electric field profile of a 66 kV composite insulator due to artificial defects were investigated using electro-optic (EO) probes. By implementing two probes, the $E_{z}$ and $E_{y}$ components were taken into consideration. The results show that adopting such EO probes to measure the electric field profile changes is very promising for the early detection of composite insulator degradation and the avoidance of unplanned outages due to insulation failure.

Designing an obstacle avoidance algorithm that incorporates the stochastic nature of human-robot-environment interactions is challenging. In high risk activities, such as those found in nuclear environments, a comprehensive approach towards handling uncertainty is essential. In this article, in the context of safe teleoperation of robots, an automated iterative sampling procedure based on Bayesian optimization is proposed, where the robot is trained to predict the behaviour of a human operator. Specifically, a Gaussian process regression model is used to learn an effective representation of a safe stop manoeuvre, required for implementing an obstacle avoidance shared control algorithm. This model is then used to predict the future time duration to execute a safe stop manoeuvre, given the current real-world circumstances. The control algorithm expects this value to be reasonably high; if not, it will gradually reduce the human operator's authority. A distinctive attribute of the proposed method is the use of statistical confidence metrics as tuning parameters, intended to provide a statistical indication of whether or not an obstacle will be avoided. The proof-of-concept experiments were carried out using three robotic platforms suited for use in nuclear robotics, an amphibious SuperDroid HD2 robot equipped with a Velodyne VLP16 (a 3D lidar), an AgileX Scout Mini R&D Pro land robot fitted with a Realsense D435 depth camera, and a Husarion ROSBot 2.0 Pro supplied with an RPLIDAR A3 (a 2D lidar). The test results show that the proposed Bayesian optimization method uses 8 times less data compared to an exhaustive grid approach, and that it provides a robot-agnostic, robust obstacle avoidance.

An adaptive approach driven by Bayesian Optimization is described for applications where remote radiation measurements made with robots are constrained by stringent upper thresholds on the mass and power payload of the necessary instrumentation, as well as by the time window within which measurements must be made, ultimately affecting their quality and maximum area coverage. The algorithm presented in this paper is applied to a gimbal assembly the comprises a collimated cerium bromide detector to perform γ-localization. A Gaussian Process models the angular distribution of radiation, and measurement locations are dynamically selected via a composite acquisition function, which combines the Expected improvement and Uncertainty Confidence Bounds functions. Convergence is driven by monitoring the rate of change of predictions and associated mean uncertainty. This approach enables accurate characterization of radiation fields while requiring up to 85% fewer measurements than conventional raster-type scanning. Its performance is evaluated in simulation, using previously obtained datasets used as measurement look-up tables, and validated in turn with hardware implementation, real-time scans.

Advancements in radiation detection for robotic deployments are described concerning the use of low-density, multifunctional, metal-foam materials as collimators. The use of nichrome (NiCr) metal foam, poly(methyl methacrylate) (PMMA) foam with stainless steel powder, and 3D-printed tungsten foam experimentally to isolate radioactive isotopes in constrained environments has been explored. This research demonstrates that these materials used in this way might reduce the payload associated with heavy metal collimators significantly relative to conventional, homogeneous alternatives such as solid lead and tungsten, and hence that they might enable spatial characterisation tasks that would otherwise be infeasible due to payload constraints—particularly in robotic systems where the use of conventional high-Z, dense collimators can limit their flexibility. The results suggest that metal foams and related materials can make collimation-aided localisation viable in such constrained settings, offering advantages in mass and characterisation granularity.

This paper reports the use of electro-optical (EO) probes in mapping electric field in known high voltage (HV) geometries (plane-plane and sphere-sphere). The measurements are performed autonomously with the use of a robotic arm and the development of an automatic data acquisition unit. The measured electric field profiles were then compared with finite element analysis (FEA) models calculated in COMSOL software and showed a good agreement with COMSOL. This work is part of the de-risking of non-contact electric field measurements for identifying defects on live cap and pin insulator strings on HV towers.

Accurate characterization of radiation hotspots is a critical requirement for monitoring and decommissioning operations in the nuclear industry, particularly where the arrangement of contamination is complex, and the availability of ground-truth data is limited. This article develops a novel stochastic modelling approach that alleviates challenges often present in such operations. Initially, the experimentally derived angular responses of a collimated single detector apparatus at different energy regions (counts over radiation footprints) are expressed by two functions: the Fourier transform of a rectangular pulse (approximated by a sinc function) and a Moffat function. Subsequently, these are both framed within a Dynamic Linear Regression (DLR) model. The resulting Moffat/sinc-DLR models enhance the quality of the fit to experimental data, and improve the accuracy and resolution of radiation localization, thus showcasing the value of such methods for radiation characterization tasks.

Radioactive contamination monitoring is an important part of radiological protection. Automation of surface monitoring poses difficulties, with a major challenge being determining the coverage of a radiation probe over an object in close proximity without collision. We propose a new accessibility framework to determine if radiation probes, modelled as convex hulls, collide with 3D point clouds representing the objects. We explain how to structure and analyze point clouds to extract properties such as the surface normal for each point. Our method for approximating radiation probes is demonstrated using the BP4 probe. This approach models both the probe and the sensor's effective scan volume with geometric primitives, providing a computationally efficient way to detect collisions with flat surfaces. The accessibility assessment builds on common methods within computer science for determining intersections. A laptop in various positions was used to demonstrate that the framework can efficiently categorise points as accessible or inaccessible, identifying unscannable regions. The output of this framework can then be used to plan collision-free paths over objects and will be the foundation of a robotic survey assistant.

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.

Accurate fuel debris location is crucial part of the decommissioning of the Fukushima Nuclear Power plants. Conventional methods face challenges due to extreme radiation and complex structure of the materials involved. In this study, we propose a novel approach utilising neutron detection and machine learning to estimate fuel material location. Geant4 simulations and pythonTM scripts have been used to generate a comprehensive dataset to train a machine learning model using MATLAB’s regression learner. A Gaussian Process Regression model was chosen for training and prediction. The results show excellent prediction performance to estimate the corium thickness effectively and to locate the nuclear fuel material with a mean square error (MSE) of 0.01. By combining the machine learning with nuclear simulation codes, this promises to enhance the nuclear decommissioning efforts to retrieve nuclear fuel debris.

The paper describes a sensor system utilising Ultra Wide Band (UWB) ultrasonic beacons to emulate ionising radiation intensity mapping within complex environments. By strategically deploying a UWB beacon onto a robot platform and integrating this with its onboard systems, and by positioning stationary ground beacons around the environment, it is possible to emulate synthetic ionising radiation intensity. This involves setting up a system that can estimate the robot position based on distances to each beacon, and subsequently applying the inverse square law to determine radiation intensity. The system will provide continuous radiation intensity estimates as the robot navigates its environment, providing a suitable simulation alternative to an ionising radiation detector that requires the use of radioactive sources. Through experimental validation and calibration, the accuracy of radiation intensity calculations is verified, showing reliable performance in comparison to currently utilised technologies. Finally, an example deployment and the achieved performance is shown demonstrating the possible application of the technology.

Deploying robots in extreme environments brings many hazards which an operator must avoid during teleoperation. In a nuclear setting, intensity of ionising radiation (alpha, beta, gamma, neutron) is not only important to monitor from a safety perspective, but also to protect robot systems which are susceptible to radiation induced damage. Therefore, robot operators must avoid ionising radiation whilst managing many other threats and information streams simultaneously. This work provides a non-visual method to communicate radiation dose rate, by imitating the clicking sound of a Geiger counter for the operator, using affordable and ubiquitous hardware. The operator is then free to use visual cues to monitor other important aspects. The system accurately emulates realistic clicks due to stochastic radioactive decay rather than use a steady repetitive tempo, with average rate of audio events governed by measured radiation dose rate on a remote robot. This system readily aids an operator to identify and avoid regions of elevated radiation intensity against background, and can be adopted by any ROS compatible robot platform.

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.

A technique for the in-situ localisation of radioactivity is described whereby the energy-resolved angular photon response of a collimated inorganic scintillation detector is used to derive the spatial arrangement of a variety of radioactive source configurations. The influence of photon radiation (X- and γ ray) incident on the collimated detector is expressed mathematically, by way of a sinc transform embedded to a dynamic linear regression model, to increase the spatial accuracy of the localisation. This approach is tested experimentally with two pairs of like radionuclides, two pairs of different combinations of radionuclides and three different radionuclides to demonstrate its combined isotopic discrimination and spatial localisation capabilities. A fit based on the model referred to above is observed to reproduce the data for the combined X- and γ-ray regions of interest effectively. This allows for increased resolution via interpolation between the data which is observed to improve location accuracy significantly. This research is relevant to applications in autonomous robotic exploration tasks and for the characterisation of contaminated environments associated with nuclear legacies and radiological emergencies.

Industrial energy consumption accounts for 50% of global use and manufacturers that invest in energy waste reduction strategies can have a significant impact on emission reduction while ensuring they operate within energy usage limits. Exceeding these limits can result in taxation from national and international policy makers and charges from national energy providers. For example, the UK Climate Change Levy, charged to businesses at 0.554 p/kWh can equate to 7.28% of a manufacturing business's energy bill based on an average total usage rate of 7.61 p/kWh.There has been growing interest in optimising the process energy consumption of machining when machine tools are responsible for 13% of industrial energy consumption, generating 16 million tonnes of CO2 emissions in the UK alone but demonstrate less than 30% energy efficiency (Gutowski et al., 2006).This paper presents the design, development and validation of a novel automated Design of Experiments (DoE) toolset that forms part of a larger Cyber-Physical System (CPS). The CPS offers the capability to automate, characterise and predict the power of three-phase industrial machining processes and to select the machining toolpath that optimises energy consumption. Validation of the DoE toolset has been conducted through automation of an industrial three-phase Hurco VM1 computer numerical control (CNC) machine and energy feature extraction with a Hidden Markov Model.

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.