Deployment Of Robots Research Articles

Exploring the Use of Terrestrial Robots for Atmospheric Electricity Measurement

Measuring the electric field is a central goal in electrostatics research, such as the study of electrostatics in atmospheric processes. It serves as a key indicator for various atmospheric phenomena, including the presence of lightning, dust or charged clouds. Traditionally, electric field measurements have been conducted from static platforms, with limited mobile measurements from airborne platforms such as balloons or, more recently, Unmanned Aerial Vehicles (UAVs). Here, we explore the potential of terrestrial robots to measure the electric field with some level of autonomy, such as during supervised navigation between user-defined waypoints. We mount a field mill on a four-wheeled rover and use a Global Navigation Satellite System (GNSS) to track the position of its measurements during an outdoor survey. The robot has a depth camera for 3D terrain mapping to contextualise local field measurements. We present plans for future research, including the use of semi-autonomous identification and exploration of electrostatic ‘hotspots’; and deployment of multiple robots (e.g., six) in a ‘sparse swarm’ configuration. We consider opportunities to employ such a robot system in environmental science or space research, for instance on Mars or the moon, where an understanding of electrostatic processes could be significant for future space missions.

파인 다이닝 레스토랑과 캐주얼 다이닝 레스토랑의 서비스 로봇

Service robots equipped with artificial intelligence are continuously advancing in both appearance and functionality. With these ongoing developments, there is a growing necessity to investigate consumer perceptions of service robots' anthropomorphism and functions within the food service industry using an optimal framework model. This study examines trust, perceived risk, and acceptance intentions concerning service robots based on their level of anthropomorphism in casual dining and fine dining restaurants. A one-way analysis of variance was conducted to assess differences in trust and perceived risk among service robots with varying levels of anthropomorphism (low, medium, and high). Additionally, multiple regression analysis was employed to explore the impact of trust and perceived risk on the intention to accept service robots. The findings revealed that trust in robots and perceived risk varied significantly between casual dining and fine dining establishments, contingent on the anthropomorphism level of the service robot. In line with human-robot interaction theory, this study delineates consumer perceptions regarding the utilization of restaurant service robots, taking into account the level of anthropomorphism. Moreover, it expands the framework concerning service robots by integrating restaurant consumers' trust, perceived risk, and acceptance intentions towards these robots. This research underscores the importance of considering human-robot interaction and addressing consumers' preferences, benefits, and safety in the deployment of service robots within the restaurant industry. It also offers practical implications for stakeholders in the restaurant service robot sector.

Opportunities for Generative Artificial Intelligence to Accelerate Deployment of Human-Supervised Autonomous Robots

Autonomous robots have the potential to supplement human capabilities while reducing cognitive and physical burden. However, deploying such systems in natural settings is currently a time-consuming process that revolves around a human’s ability to research, design, test, and evaluate the robot – thereby introducing unnecessary bottlenecks and significant delays to technology adoption. The current work in the field of human-robot interaction (HRI) has historically focused on robot use even though humans play a critical role during autonomous system design and deployment. We argue that the scope of HRI must be expanded beyond that of the current views within the scientific community, to include all phases of system development, deployment, and use. Furthermore, to facilitate the pursuit of this new expanded scope, we present eight opportunities for technological advances in HRI and autonomy using Generative AI that, if realized, could have transformational impact on the fielding of human-supervised autonomous robots. Broadly speaking, our identified opportunities relate to interaction and trustworthiness, collaboration and cooperation, robot motion, robot perception, synthetic scenario generation, testing and evaluation, failure detection, and robot design.

Intermittent Deployment for Large-Scale Multi-Robot Forage Perception: Data Synthesis, Prediction, and Planning

Monitoring the health and vigor of grasslands is vital for informing management decisions to optimize rotational grazing in agriculture applications. To take advantage of forage resources and improve land productivity, we require knowledge of pastureland growth patterns that is simply unavailable at the state of the art. In this paper, we propose to deploy a team of robots to monitor the evolution of an unknown pastureland environment to fulfill the above goal. To monitor such an environment, which usually evolves slowly, we need to design a strategy for rapid assessment of the environment over large areas at a low cost. Thus, we propose an integrated pipeline comprising data synthesis, deep neural network training, and prediction along with a multi-robot deployment algorithm that monitors pasturelands intermittently. Specifically, using expert-informed agricultural data coupled with novel data synthesis in ROS Gazebo, we first propose a new neural network architecture to learn the spatiotemporal dynamics of the environment. Such predictions help us to understand pastureland growth patterns on large scales and make appropriate monitoring decisions for the future. Based on our predictions, we then design an intermittent multi-robot deployment policy for low-cost monitoring. Finally, we compare the proposed pipeline with other methods, from data synthesis to prediction and planning, to corroborate our pipeline’s performance. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —Pasturelands are an integral part of agricultural production in the United States. To take full advantage of the forage resource and avoid environmental degradation, pastureland must be managed optimally. This paper focuses on the question of how to deploy robot teams to sense and model physical processes over varying timescales. The goal of this work is to develop a new integrated pipeline for the long-term deployment of heterogeneous robot teams grounded in the problem of autonomous monitoring in precision grazing to improve land productivity. By using the proposed pipeline in grassland ecosystem management, we will have a better understanding of the physical environment while respecting energy budgets.

Powering In-Field Continuous Robotic Systems Using Solar Energy Systems

Highlights Because of variable weather conditions, battery capacity must be at least 2.5 times larger than daily energy demands for continuous off-grid power. At a minimum, the nominal solar panel array size must be large enough to provide the daily energy demand in 3 hours. Because of a trade-off relationship, minimal battery capacity or solar panel size requires more than the minimum for the other component. Limiting operation to March through October reduced required sizes. Adding a backup generator with minimal usage of 50 kg of propane per year also reduced sizes. Abstract. Continuous in-field robotic and automated systems are challenged by the need for a continuous supply of power. This off-grid power supply can be diesel, gasoline, propane, or alternative energy sources like solar or wind. This study investigates the required solar panel arrays and energy storage capacities for an off-grid solar-battery power supply system. This study used 22 years of historical weather data from Lexington, Kentucky, and processed it with the National Renewable Energy Laboratory’s System Advisor Model to model hourly energy output from solar panel arrays of 2 kW, 3 kW, 4 kW, 5 kW, 10 kW, 15 kW, and 20 kW. This was combined with an energy storage model (simulated battery capacities of 5 kWh, 10 kWh, 15 kWh, 20 kWh, 30 kWh, 40 kWh, 50 kWh, and 60 kWh) and an energy use model (simulated daily energy demands of 3.6 kWh, 6 kWh, 12 kWh, 18 kWh, and 24 kWh) to determine which systems could operate over the entire 22 years without reaching critical minimum levels. For a 3.6 kWh daily energy demand, 32 combinations of solar panel arrays and battery capacities remained above critical levels, while this was only 12 combinations for a 6 kWh daily energy demand and no combination could support larger energy demands. An example a feasible system to support a 6 kWh energy demand is one that uses a 15 kW panel array (42 standard panels with an approximate system hardware cost of $22,650) and 30 kWh of energy storage capacity (which with lead acid batteries would cost $11,661 and require a volume of 300 L). There is a tradeoff between solar panel array size and battery capacity so other feasible systems can be realized by increasing one to make up for reductions in the other variable. Additionally, limiting system operation to March through October can reduce the size of the required panel array (to between 33% to 67% of the original size) or energy storage capacity (to between 40% to 67% of the original capacity) for a given daily energy load. The size of the solar battery system could be further decreased by adding an emergency generator that uses no more than 50 kg of propane annually. Battery capacities could be reduced by 17% to 38% compared to the original system, and the solar panel arrays could be reduced by 13% to 15% compared to the original. Specific reduction amounts depended on the specific configuration of the original system and the daily load level that had to be supported. A system that only operated from March through October with a backup generator could also support the 12 and 18 kWh daily energy demands, which could not be supported by the original system. Even with limiting operation to March through October and using an emergency generator, the minimal battery capacity was at least 2.5 times larger than the daily energy demand, and the solar panel array had to be large enough that the nominal output would provide the daily energy demand in 3 hours of full sun. However, because of the trade-off relationship, these minimums cannot be attained together. While continuous in-field robotic deployments show promise for improving agriculture, it will be critical to design with energy efficiency in mind and the off-grid power supply for wide-spread use across agricultural lands. Keywords: Agriculture robot, Autonomous, Batteries, Computer modeling, Energy, Power, Simulation, Sizing, Solar, System.

YOLOC-tiny: a generalized lightweight real-time detection model for multiripeness fruits of large non-green-ripe citrus in unstructured environments.

This study addresses the challenges of low detection precision and limited generalization across various ripeness levels and varieties for large non-green-ripe citrus fruits in complex scenarios. We present a high-precision and lightweight model, YOLOC-tiny, built upon YOLOv7, which utilizes EfficientNet-B0 as the feature extraction backbone network. To augment sensing capabilities and improve detection accuracy, we embed a spatial and channel composite attention mechanism, the convolutional block attention module (CBAM), into the head's efficient aggregation network. Additionally, we introduce an adaptive and complete intersection over union regression loss function, designed by integrating the phenotypic features of large non-green-ripe citrus, to mitigate the impact of data noise and efficiently calculate detection loss. Finally, a layer-based adaptive magnitude pruning strategy is employed to further eliminate redundant connections and parameters in the model. Targeting three types of citrus widely planted in Sichuan Province-navel orange, Ehime Jelly orange, and Harumi tangerine-YOLOC-tiny achieves an impressive mean average precision (mAP) of 83.0%, surpassing most other state-of-the-art (SOTA) detectors in the same class. Compared with YOLOv7 and YOLOv8x, its mAP improved by 1.7% and 1.9%, respectively, with a parameter count of only 4.2M. In picking robot deployment applications, YOLOC-tiny attains an accuracy of 92.8% at a rate of 59 frames per second. This study provides a theoretical foundation and technical reference for upgrading and optimizing low-computing-power ground-based robots, such as those used for fruit picking and orchard inspection.

A matter of consequences

Abstract On reviewing the literature regarding acceptance and trust in human-robot interaction (HRI), there are a number of open questions that needed to be addressed in order to establish effective collaborations between humans and robots in real-world applications. In particular, we identified four principal open areas that should be investigated to create guidelines for the successful deployment of robots in the wild. These areas are focused on: (1) the robot’s abilities and limitations; in particular when it makes errors with different severity of consequences, (2) individual differences, (3) the dynamics of human-robot trust, and (4) the interaction between humans and robots over time. In this paper, we present two very similar studies, one with a virtual robot with human-like abilities, and one with a Care-O-bot 4 robot. In the first study, we create an immersive narrative using an interactive storyboard to collect responses of 154 participants. In the second study, 6 participants had repeated interactions over three weeks with a physical robot. We summarise and discuss the findings of our investigations of the effects of robots’ errors on people’s trust in robots for designing mechanisms that allow robots to recover from a breach of trust. In particular, we observed that robots’ errors had greater impact on people’s trust in the robot when the errors were made at the beginning of the interaction and had severe consequences. Our results also provided insights on how these errors vary according to the individuals’ personalities, expectations and previous experiences.

Robot, let us pray! Can and should robots have religious functions? An ethical exploration of religious robots

AbstractConsiderable progress is being made in robotics, with robots being developed for many different areas of life: there are service robots, industrial robots, transport robots, medical robots, household robots, sex robots, exploration robots, military robots, and many more. As robot development advances, an intriguing question arises: should robots also encompass religious functions? Religious robots could be used in religious practices, education, discussions, and ceremonies within religious buildings. This article delves into two pivotal questions, combining perspectives from philosophy and religious studies: can and should robots have religious functions? Section 2 initiates the discourse by introducing and discussing the relationship between robots and religion. The core of the article (developed in Sects. 3 and 4) scrutinizes the fundamental questions: can robots possess religious functions, and should they? After an exhaustive discussion of the arguments, benefits, and potential objections regarding religious robots, Sect. 5 addresses the lingering ethical challenges that demand attention. Section 6 presents a discussion of the findings, outlines the limitations of this study, and ultimately responds to the dual research question. Based on the study’s results, brief criteria for the development and deployment of religious robots are proposed, serving as guidelines for future research. Section 7 concludes by offering insights into the future development of religious robots and potential avenues for further research.

Bio-Inspired Untethered Robot-Sensor Platform for Minimally Invasive Biomedical Sensing.

Conventional catheter- or probe-based in vivo biomedical sensing is uncomfortable, inconvenient, and sometimes infeasible for long-term monitoring. Existing implantable sensors often require an invasive procedure for sensor placement. Untethered soft robots with the capability to deliver the sensor to the desired monitoring point hold great promise for minimally invasive biomedical sensing. Inspired by the locomotion modes of snakes, we present here a soft kirigami robot for sensor deployment and real-time wireless sensing. The locomotion mechanism of the soft robot is achieved by kirigami patterns that offer asymmetric tribological properties that mimic the skin of the snake. The robot exhibits good deployability, excellent load capacity (up to 150 times its own weight), high-speed locomotion (0.25 body length per step), and wide environmental adaptability with multimodal movements (obstacle crossing, locomotion in wet and dry conditions, climbing, and inverted crawling). When integrated with passive sensors, the versatile soft robot can locomote inside the human body, deliver the passive sensor to the desired location, and hold the sensor in place for real-time monitoring in a minimally invasive manner. The proof-of-concept prototype demonstrates that the platform can perform real-time impedance monitoring for the diagnosis of gastroesophageal reflux disease.

Lessons learned: Symbiotic autonomous robot ecosystem for nuclear environments

AbstractNuclear 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.

Should we be afraid of dental robots?

As it seems likely that France, at the forefront in the use of surgical robotic platforms, will authorize the deployment of dental robots in the short term, the purpose of this article is to question what is at stake in this technological revolution, for dental professionals, regulators but also (and above all) for patients: what awareness-raising will the intervention of machines bring? How does robotics reshuffle the cards of the care relationship? What are the ethical and public health issues? After having defined dental robots as non-humanoid, automated surgical devices with exclusive restorative vocation, we will see how the field of dental robotics currently oscillates between hopes and illusions. We will describe the new responsibilities and ethical requirements related to the introduction of these AI-powered technical objects, as well as the necessary safeguards that have to be implemented in order to avoid any moral buffer and to protect patients from any robotization (literally or metaphorically in the sense of ‘zombification’). Finally, we will show that ultimately the question of robot-dentists is the natural evolution of unreasonable application of industrial processes to the rationalization of health and ectopic, neo-liberal practices shifting healthcare into a commercial commodity. Patients can participate in reversing this trend, by recalling that the centrality of the human person is the cornerstone of health professions.

Developing Industry 5.0 To Effectively Harness Production Capacities

Abstract The dynamic evolution of the economy and the evolving demands necessitating adaptability are primary catalysts for the emergence of Industry 5.0 which, building on the foundations of Industry 4.0, is oriented towards meeting human needs and expectations while ensuring the responsible functioning of the workforce. This is particularly salient in sectors experiencing rapid advancements in manufacturing concepts, characterized by the integration of advanced IT, information and data utilization, automation, and the deployment of collaborative robots, often referred to as “cobots”. The study outlines the key prerequisites concerning the development and operation of Industry 5.0, as specified in European Union guidelines, and evident in research literature. Three critical facets pertinent to the effective implementation of Industry 5.0 are extensively addressed, encompassing the imperative to effectively cater to human-related requisites and highlighting the potential benefits therein. The paper offers an overview of openly accessible, previously published materials on the subject. The author’s examination encompasses studies regarded as milestones, which build on the foundations of Industry 4.0 to advance the conceptual framework of Industry 5.0. Rather than centering on statistical analysis, this research attempts to explore relationships that warrant further in-depth investigation to ascertain existing correlations. The paper seeks to outline factors to be considered in the course of implementing Industry 5.0. Its conclusions may be of use in supporting people responsible for organizational development and specifically in helping them to identify potential issues so as to give their organization an edge in the global competitive environment.

Sim2Real Neural Controllers for Physics-Based Robotic Deployment of Deformable Linear Objects

Deformable linear objects (DLOs), such as rods, cables, and ropes, play important roles in daily life. However, manipulation of DLOs is challenging as large geometrically nonlinear deformations may occur during the manipulation process. This problem is made even more difficult as the different deformation modes (e.g., stretching, bending, and twisting) may result in elastic instabilities during manipulation. In this paper, we formulate a physics-guided data-driven method to solve a challenging manipulation task—accurately deploying a DLO (an elastic rod) onto a rigid substrate along various prescribed patterns. Our framework combines machine learning, scaling analysis, and physical simulations to develop a physics-based neural controller for deployment. We explore the complex interplay between the gravitational and elastic energies of the manipulated DLO and obtain a control method for DLO deployment that is robust against friction and material properties. Out of the numerous geometrical and material properties of the rod and substrate, we show that only three non-dimensional parameters are needed to describe the deployment process with physical analysis. Therefore, the essence of the controlling law for the manipulation task can be constructed with a low-dimensional model, drastically increasing the computation speed. The effectiveness of our optimal control scheme is shown through a comprehensive robotic case study comparing against a heuristic control method for deploying rods for a wide variety of patterns. In addition to this, we also showcase the practicality of our control scheme by having a robot accomplish challenging high-level tasks such as mimicking human handwriting, cable placement, and tying knots.

Design And Analysis of a Pipe Inspection Caterpillar Robot Using Different Materials

The development of a pipe inspection robot employing caterpillar locomotion and constructed with PLA and ABS materials entails a comprehensive process. Beginning with the delineation of specific requirements, such as size and inspection capabilities, the conceptual design is conceived, outlining the robot's features. The subsequent phase involves meticulous 3D modelling using software like SolidWorks, followed by ANSYS analysis to simulate and optimize the robot's structural integrity, stress distribution, and overall performance. PLA and ABS are chosen as 3D printing materials, with PLA offering ease of printing and biodegradability, while ABS contributes durability and temperature resistance. The prototype is then constructed, incorporating motors, sensors, and the caterpillar locomotion system. ANSYS analysis is employed to assess the robot's behaviour under varying conditions, ensuring robustness and reliability. The iterative process of testing and refining the design leads to the optimization of the robot's performance. The final steps involve thorough documentation, regulatory compliance checks, and potential scalability for diverse applications, culminating in the deployment of the caterpillar robot for effective pipe inspection tasks.&#x0D; This paper delves into the design and static structural analysis of a caterpillar robot model utilizing ANSYS, with a specific emphasis on assessing two distinct materials: Acrylonitrile Butadiene Styrene (ABS) and Polylactic acid (PLA). The study involves a comprehensive comparison of various parameters between the two materials, aiming to provide insights into their respective performance and suitability for the caterpillar robot's structural components.

Best practice for safety management – case of major oil processing country in the Middle East

PurposeDeployment of health and safety standards in extremely hazardous work environments such as oil and gas sector, is essential to minimise accidents leaving employees permanently or temporarily incapacitated. The purpose of this research, is to understand why there are frequent accidents in case country's oil and gas sector, with a view to recommend solutions to mitigate problems.Design/methodology/approachResearch methodology involved extensive review of the literature to appreciate background, and current research on typical accidents and safety measures taken at oil and gas construction sites to minimise accidents in a middle east country. Interviews, questionnaires and case stidies were deployed to acquire data which highlighted major reasons for accidents occurrence at oil and gas construction sites, and safety tools and techniques that could reduce accident rate if adopted by companies. FindingsFindings, showed that oil and gas construction projects in case country were prone to health and safety related risks, challenges and accidents due to failure to comply with standards and legislation. Construction site teams and shop floor staff were rarely involved in development of safety policies, and some had no understanding of requirements and procedures underpinning safety during operations. Research recommended rapid application/adoption of international standards underpinned by ISO 45000 series and staff training at all levels. Deployment of robots and use of machine learning technology were suggested to implement risky tasks in the sector.Originality/valueResearch was based on rampant accidents occurring in hazardous oil and gas sector in country studied. Enforcement of health and safety standards, and use of modern tools and techniques were recommended to minimise accident rate.

Development of a Neuroergonomic Assessment for the Evaluation of Mental Workload in an Industrial Human–Robot Interaction Assembly Task: A Comparative Case Study

The disruptive deployment of collaborative robots, named cobots, in Industry 5.0 has brought attention to the safety and ergonomic aspects of industrial human–robot interaction (HRI) tasks. In particular, the study of the operator’s mental workload in HRI activities has been the research object of a new branch of ergonomics, called neuroergonomics, to improve the operator’s wellbeing and the efficiency of the system. This study shows the development of a combinative assessment for the evaluation of mental workload in a comparative analysis of two assembly task scenarios, without and with robot interaction. The evaluation of mental workload is achieved through a combination of subjective (NASA TLX) and real-time objective measurements. This latter measurement is found using an innovative electroencephalogram (EEG) device and the characterization of the cognitive workload through the brainwave power ratio β/α, defined after the pre-processing phase of EEG data. Finally, observational analyses are considered regarding the task performance of the two scenarios. The statistical analyses show how significantly the mental workload diminution and a higher level of performance, as the number of components assembled correctly by the participants, are achieved in the scenario with the robot.

Security Automation in Application Development Using Robotic Process Automation (RPA)

In the contemporary digital landscape, the rapid advancement of application development necessitates a parallel evolution in security measures. Traditional security practices often struggle to keep pace with the dynamic nature of modern software development. To address these challenges, Robotic Process Automation (RPA) emerges as a transformative tool, offering substantial potential to automate security tasks within the application development lifecycle. This paper explores the integration of RPA into security automation, focusing on its capabilities to enhance security protocols, minimize human error, and streamline processes across various stages of development. Security automation through RPA involves the deployment of software robots to perform repetitive, rule-based tasks that are essential to maintaining and enforcing security standards. These tasks include automated code scanning, vulnerability assessments, compliance checks, and incident response, all of which are crucial to the development and deployment of secure applications. By automating these processes, organizations can significantly reduce the time and resources required to secure applications, ensuring that security measures are not compromised by the speed and agility demanded by modern development practices.

스마트도시의 안전성 향상을 위한 스마트기술 적용방안에 관한 연구

In order to propose ways to apply smart technology to improve the safety of smart cities, this study aims to propose smart technologies that are highly related to safety management in the areas of transportation, medical care, environment, nature, and crime prevention in smart cities, such as AR, BIM, big data, and cloud. , IoT, VR, robot automation, drones, and 3D printing were analyzed. For the study, the applicability of smart technology in each field was investigated by safety management experts using a matrix evaluation method, and the results can be summarized as follows. In the transportation field, big data technology for collecting and processing traffic information through GPS, GIS and smart phone apps, drone technology that monitors real-time traffic information and utilizes sensors such as vehicles, roads and signal devices, and various collected information The safety of smart cities can be secured by applying IoT technology that builds prediction models based on traffic patterns. In the medical field, robot automation technology that sets up predictive models for telemedicine, health management, drug processing and infectious diseases, drone technology that utilizes the movement of patients and medical staff, attachment of monitoring devices, and securing public health response data, etc. It is predicted that it will be possible to secure the safety of smart cities through 3D printing technology that can manufacture medical instruments, auxiliary devices, and various artificial bodies, as well as various medical models and educational tools. In the environmental field, drone technology enables real-time monitoring of water quality, air, and various environmental factors, robotic automation technology that uses robots in places to collect environmental data within the city, and environmental infrastructure that installs and collects buildings, vehicles, and various infrastructure facilities. IoT technology can be applied to improve the safety of smart cities. In the natural field, drone technology is capable of collecting and utilizing various data by attaching video, thermal images, LiDAR scans, radars and sensors, and integrated management linked to a central system focused on the installation of houses, buildings and various infrastructure facilities and environmental changes. The safety of smart cities can be improved by utilizing possible IoT technology, BIM technology that can establish response plans for storm and flood damage, cold waves, heatstroke, and earthquakes through 3D models, and big data technology that collects, integrates, and builds platforms for safety-related data. It can be promoted. In the crime prevention field, drone technology that can detect the occurrence of abnormal signals such as sound, heat, and optical sensors and link with the local police station, IoT technology that can collect and analyze voice recognition technology, and AI that strengthens facial and vehicle recognition in intelligent CCTV. The safety of smart cities can be secured through robot automation technology that combines the deployment of basic robots and collaboration with drones and IoT technologies.

"Would I Feel More Secure With a Robot?": Understanding Perceptions of Security Robots in Public Spaces

Robots are increasingly being deployed as security agents helping law enforcement in spaces such as streets, parks, or shopping malls. Unfortunately, the deployment of security robots is not without problems and controversies. For example, the New York Police Department canceled its contract with Boston Dynamics in response to backlash from their use of Digidog, an autonomous robotic dog, which sparked fears in the public. However, it is unclear to what extent affected communities have been involved in the design and deployment process of robots. This is problematic because, without input from community members in the processes of design and deployment, security robots are likely to not satisfy the concerns or safety needs of real communities. To gain deeper insight into people's perceptions of security robots - including both potential benefits and concerns - we conducted 17 semi-structured interviews addressing the following research questions: RQ1. What characteristics do people ascribe to security robots? RQ2. What expectations do people have about the function and role of security robots? RQ3. What are people's attitudes toward the use of security robots? Our study offers several contributions to the existing literature on security robots.

A framework for FAIR robotic datasets

It is essential to publish and make available environmental data gathered by emerging robotic platforms to contribute to the Global Ocean Observing System (GOOS), supported by the United Nations - Decade of Ocean Science for Sustainable Development (2021–2030). The transparency of these unique observational datasets needs to be supported by the corresponding robotic records. The data describing the observational platform behaviour and its performance are necessary to validate the environmental data and repeat consistently the in-situ robotic deployment. The Free and Open Source Software (FOSS), proposed in this manuscript, describes how, using the established approach in Earth Sciences, the data characterising marine robotic missions can be formatted and shared following the FAIR (Findable, Accessible, Interoperable, Reusable) principles. The manuscript is a step-by-step guide to render marine robotic telemetry FAIR and publishable. State-of-the-art protocols for metadata and data formatting are proposed, applied and integrated automatically using Jupyter Notebooks to maximise visibility and ease of use. The method outlined here aims to be a first fundamental step towards FAIR interdisciplinary observational science.