Conor McGinn

As with all remotely controlled robots, successful teleoperation of social and telepresence robots relies greatly on operator situation awareness; however, existing situation awareness measurements, most being originally created for military purposes, are not adapted to the context of social interaction. We propose an objective technique for telepresence evaluation based on the widely accepted Situation Awareness Global Assessment Technique, adjusted to suit social contexts. This was trialled in a between-subjects participant study ( n = 56) comparing the effect of mono and spatial (binaural) audio feedback on operator situation awareness during robot teleoperation in a simulated social telepresence scenario. Subjective data were also recorded, including questions adapted from Witmer and Singer’s Presence Questionnaire, as well as qualitative feedback from participants. No significant differences in situation awareness measurements were detected; however, correlations observed between measures call for further research. This study and its findings are a potential starting point for the development of social situation awareness assessment techniques, which can inform future social and telepresence robot design decisions.

The use of mobile ultraviolet-C (UV-C) disinfection devices for the decontamination of surfaces in hospitals and other settings has increased dramatically in recent years. The efficacy of these devices relies on the UV-C dose they deliver to surfaces. This dose is dependent on the room layout, the shadowing, the position of the UV-C source, lamp degradation, humidity and other factors, making it challenging to estimate. Furthermore, since UV-C exposure is regulated, personnel in the room must not be exposed to UV-C doses beyond occupational limits. We proposed a systematic method to monitor the UV-C dose administered to surfaces during a robotic disinfection procedure. This was achieved using a distributed network of wireless UV-C sensors that provide real-time measurements to a robotic platform and operator. These sensors were validated for their linearity and cosine response. To ensure operators could safely remain in the area, a wearable sensor was incorporated to monitor the UV-C exposure of an operator, and it provided an audible warning upon exposure and, if necessary, ceased the UV-C emission from the robot. Enhanced disinfection procedures could then be conducted as items in the room could be rearranged during the procedure to maximise the UV-C fluence delivered to otherwise inaccessible surfaces while allowing UVC disinfection to occur in parallel with traditional cleaning. The system was tested for the terminal disinfection of a hospital ward. During the procedure, the robot was manually positioned in the room by the operator repeatedly, who then used feedback from the sensors to ensure the desired UV-C dose was achieved while also conducting other cleaning tasks. An analysis verified the practicality of this disinfection methodology while highlighting factors which could affect its adoption.

Objective. New technologies, including robots comprising germ-killing UV lamps, are increasingly being used to decontaminate hospitals and prevent the spread of COVID-19 and other superbugs. Existing approaches for modelling the irradiance field surrounding mobile UV disinfection robots are limited by their inability to capture the physics of their bespoke geometrical configurations and do not account for reflections. The goal of this research was to extend current models to address these limitations and to subsequently verify these models using empirically collected data. Approach. Two distinct parametric models were developed to describe a multi-lamp robotic UV system and adapted to incorporate the effects of irradiance amplification from the device's reflectors. The first model was derived from electromagnetic wave theory while the second was derived from conservation of energy and diffusion methods. Both models were tuned using data from empirical testing of an existing UV robot, and then validated using an independent set of measurements from the same device. Results. For each parameter, predictions made using the conservation of energy method were found to closely approximate the empirical data, offering more accurate estimates of the 3D irradiance field than the electromagnetic wave theory model. Significance. The versatility of the proposed method ensures that it can be easily adapted to different embodiments, providing a systematic way for researchers to develop accurate numerical models of custom UV robots, which may be used to inform deployment and/or to improve the accuracy of virtual simulation.

Ultraviolet germicidal irradiation (UVGI) technologies have emerged as a promising alternative to biocides as a means of surface disinfection in hospitals and other healthcare settings. This paper reviews the methods used by researchers and clinicians in deploying and evaluating the efficacy of UVGI technology. The type of UVGI technology used, the clinical setting where the device was deployed, and the methods of environmental testing that the researchers followed are investigated. The findings suggest that clinical UVGI deployments have been growing steadily since 2010 and have increased dramatically since the start of the COVID‐19 pandemic. Hardware platforms and operating procedures vary considerably between studies. Most studies measure efficacy of the technology based on the objective measurement of bacterial bioburden reduction; however, studies conducted over longer durations have examined the impact of UVGI on the reduction of healthcare associated infections (HCAIs). Future trends include increased automation and the use of UVGI technologies that are safer for use around people. Although existing evidence seems to support the efficacy of UVGI as a tool capable of reducing HCAIs, more research is needed to measure the magnitude of these effects and to establish recommended best practices.

A robot’s ability to grasp moving objects depends on the availability of real-time sensor data in both the far-field and near-field of the gripper. This research investigates the potential contribution of tactile sensing to a task of grasping an object in motion. It was hypothesised that combining tactile sensor data with a reactive grasping strategy could improve its robustness to prediction errors, leading to a better, more adaptive performance. Using a two-finger gripper, we evaluated the performance of two algorithms to grasp a ball rolling on a horizontal plane at a range of speeds and gripper contact points. The first approach involved an adaptive grasping strategy initiated by tactile sensors in the fingers. The second strategy initiated the grasp based on a prediction of the position of the object relative to the gripper, and provided a proxy to a vision-based object tracking system. It was found that the integration of tactile sensor feedback resulted in a higher observed grasp robustness, especially when the gripper–ball contact point was displaced from the centre of the gripper. These findings demonstrate the performance gains that can be attained by incorporating near-field sensor data into the grasp strategy and motivate further research on how this strategy might be expanded for use in different manipulator designs and in more complex grasp scenarios.

The importance of infection control procedures in hospital radiology departments has become increasingly apparent in recent months as the impact of COVID-19 has spread across the world. Existing disinfectant procedures that rely on the manual application of chemical-based disinfectants are time consuming, resource intensive and prone to high degrees of human error. Alternative non-touch disinfection methods, such as Ultraviolet Germicidal Irradiation (UVGI), have the potential to overcome many of the limitations of existing approaches while significantly improving workflow and equipment utilization. The aim of this research was to investigate the germicidal effectiveness and the practical feasibility of using a robotic UVGI device for disinfecting surfaces in a radiology setting. We present the design of a robotic UVGI platform that can be deployed alongside human workers and can operate autonomously within cramped rooms, thereby addressing two important requirements necessary for integrating the technology within radiology settings. In one hospital, we conducted experiments in a CT and X-ray room. In a second hospital, we investigated the germicidal performance of the robot when deployed to disinfect a CT room in <15 minutes, a period which is estimated to be 2–4 times faster than current practice for disinfecting rooms after infectious (or potentially infectious) patients. Findings from both test sites show that UVGI successfully inactivated all of measurable microbial load on 22 out of 24 surfaces. On the remaining two surfaces, UVGI reduced the microbial load by 84 and 95%, respectively. The study also exposes some of the challenges of manually disinfecting radiology suites, revealing high concentrations of microbial load in hard-to-reach places. Our findings provide compelling evidence that UVGI can effectively inactivate microbes on commonly touched surfaces in radiology suites, even if they were only exposed to relatively short bursts of irradiation. Despite the short irradiation period, we demonstrated the ability to inactivate microbes with more complex cell structures and requiring higher UV inactivation energies than SARS-CoV-2, thus indicating high likelihood of effectiveness against coronavirus.