ABSTRACT
The COVID-19 pandemic has led to a surge of interest in research work involving the development of robotic systems that reduce human-to-human interaction, as such a technology can greatly benefit healthcare industries in preventing the spread of highly infectious diseases. An indoor service robot is built and equipped with wheel odometry and a 2D LiDAR. However, the presence of the systematic odometry errors is evident during field testing. Hence, the possibility of minimizing systematic odometry errors is inspected using various methods of calculation, namely: UMBmark, Lee's and Jung's. The methods all use the Bidirectional Square Path test, performed together with ROS. It is found that Jung's method is the most appropriate method showing a 20.4% improvement compared to the uncalibrated dead reckoning accuracy. Moreover, it is found that the presence of slippage, a nonsystematic error, greatly affects the return position errors of the robot. Consequently, it is recommended to improve the design of the wheelbase to minimize the effects of nonsystematic errors. © 2022 IEEE.
ABSTRACT
Research on consumers' trust toward interaction with Artificially Intelligent (AI) social robots in service delivery has gained much more interest due to the outbreak of COVID-19 pandemic. However, this topic has not been widely invesgiated in China. To provide a psychometrically sound instrument in diverse cultural contexts, this study was to validate a scale of Social Service Robot Interaction Trust (SSRIT) that measures consumers' trust toward interaction with AI social robots in service delivery in a Chinese sample of adults. The results showed that the Chinese version of the SSRIT was validated with reliability and validity, suggesting that the Chinese version of the SSRIT could be used as an effective tool to assess trust in AI social robots in service delivery within the Chinese context. The implications of the findings were also discussed. [ FROM AUTHOR] Copyright of International Journal of Human-Computer Interaction is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)
ABSTRACT
Social distancing is an effective way to reduce infection risk during pandemics, such as COVID-19. It is important for the tourism industry to understand the effect of social distancing on tourist behavior to better adapt to this emerging environment. This study investigates the role of social distancing in tourists' preferences for anthropomorphism. Based on three experimental studies, this study found that tourists tend to prefer anthropomorphism more under conditions of social distancing (vs. nonsocial distancing). This effect was induced by the higher perceived warmth of anthropomorphism when one had to practice social distancing. Such effects are only significant among tourists with higher levels of interdependent self-construal. This study makes significant theoretical contributions and provides important practical implications for tourism marketing and service design during pandemic and epidemic crises.
ABSTRACT
There has been an increase of interest and demand in the usage of logistic indoor service robots that are designed to minimize interactions between humans due to the occurrence of the COVID-19 outbreak. The application of the rising technology in the medical sector has great benefits in the industry, such as the prevention of the spread of highly infectious diseases using distance as a factor. Rooting from the purpose of the said robot, the main focus should be the ease of navigation through achieving the desired trajectory, in order to maximize the functionality, prevent collision, reduce user maneuvering difficulties, and such. Hence, this paper is focused on improving the trajectory errors on the robot navigation performance based on different control system designs specifically, a physical joystick controller and a mobile-based Bluetooth application controller. The design of the joystick is based on a pivot as its base which is directed to all angles and the design of the Bluetooth app is based on fourdirectional buttons that will operate upon clicking, and switching to other buttons to change commands. With this, the researchers conducted linear path and rotational tests using both remote control modes that are based on five varying speed values of 0.75 m/s, 0.5m/s, 0.35m/s, 0.25m/s, and 0.15 m/s. Based on the data analysis, the yielded results showed that using the Bluetooth app lowers the robot's trajectory error by 50% to 60% compared to using ajoystick to navigate to the desired point. Thus, the researchers concluded that the design of a control system greatly affects the robot navigation in achieving the desired trajectory. Considering the nonsystematic errors, a calibration based on the hardware structure design specifically on the caster wheel is recommended. © 2023 IEEE.
ABSTRACT
Virtual Reality (VR) and simulation continue po-sitioning as suitable tools for fine-tuning processes otherwise impossible in real life. Such is the case of Aether, a mobile service robot for elderly care developed during the COVID-19 pandemic. Aether's development was negatively impacted due to restrictions placed on accessing long-term care facilities that impeded testing object tracking, elderly tracking, fall detection, and human-robot interactions. Our efforts to maximize Aether's development led us to create a digital twin where the core functionality is replicated to train the machine learning modules to optimize the robot's responses before real-world deployment. However, the digital twin creation requires significant authoring to ensure the virtual environment matches the real one by employing 3D technical artistry skills, which demands a professional knowledgeable in this domain. This paper presents a sandbox prototype for scene customization that allows importing, positioning, scaling, and saving changes for mobile robot simulation. Our preliminary testing of the sandbox has focused on usability to understand how the setting up of the environment is perceived. Preliminary results indicate that the sandbox is usable with improvements pertaining to improving the manipulation of the objects. © 2022 IEEE.
ABSTRACT
The COVID-19 pandemic has had a massive impact on the global aviation industry. As a result, the airline industry has been forced to embrace new technologies and procedures in order to provide a more secure and bio-safe travel. Currently, the role of smart technology in airport systems has expanded significantly as a result of the contemporary Industry 4.0 context. The article presents a novel construction of an intelligent mobile robot system to guide passengers to take the plane at the departure terminals at busy airports. The robot provides instructions to the customer through the interaction between the robot and the customer utilizing voice communications. The usage of the Google Cloud Speech-to-Text API combined with technical machine learning to analyze and understand the customer's requirements are deployed. In addition, we use a face detection technique based on Multi-task Cascaded Convolutional Networks (MTCNN) to predict the distance between the robot and passengers to perform the function. The robot can guide passengers to desired areas in the terminal. The results and evaluation of the implementation process are also mentioned in the article and show promise.
ABSTRACT
The COVID-19 pandemic has accelerated the use of contactless service robots in hospitality industries. However, the key drivers of consumer behaviors against service robots have been ill-understood. This study examines the interactive relationships between the physical (visual features) and psychological (service autonomy) dimensions of service-robot anthropomorphism and their impacts on consumer acceptance of service robots. Adopting an experimental vignette method (EVM) with 402 participants, the study reveals that the impacts of visual features on consumers' intention are affected by the level of service robots' autonomy;particularly, consumers showed the highest intention when the robots have medium visual features and high autonomy while their intention became lower for the same level of visual features with low autonomy. Interestingly, consumers showed the lowest intention with high level visual features, regardless of the levels of autonomy. Our results also show that human identity threats and consumer resistance play a significant counterproductive mechanism between service robot anthropomorphism and consumers' intention. © 2022 Elsevier Ltd
ABSTRACT
Purpose>This study aims to compare the effect of barista type (human vs robot) on perceived safety and examine the role of two moderators (mask-wearing and coronavirus vaccination) on the effects of barista type on perceived safety and visit intention.Design/methodology/approach>The research design consists of three studies. Three experiments were sequentially designed and conducted to address research questions.Findings>Study 1 found that perceived safety mediates the effect of barista type on customers' visit intention. Study 2 revealed that the mask-wearing of human and robot baristas differently influences perceived safety. Study 3 showed that customers, especially where robot baristas are used, perceive the effect of mask-wearing differently depending on their coronavirus vaccination status.Research limitations/implications>Given that the levels of restrictions vary worldwide, together with the extent of countries' vaccination rollouts, caution is required when generalising the research findings.Practical implications>The findings have practical implications for the hospitality industry, where the roles of face masks and coronavirus vaccines in shaping consumer psychology and behaviour have been underexplored.Originality/value>Coronavirus vaccination is considered one of the most important driving forces for the recovery of hospitality businesses. As a heuristic-systematic model postulated, this study identified that vaccination status (fully vaccinated vs not vaccinated) changes the level of involvement when customers assess the level of risk in service environments. By pinpointing the function of service robots in safeguarding customers from the potential spread of the disease, this study broadens the scope of human–robot interaction research in hospitality.
ABSTRACT
In recent years, due to the impact of COVID-19 around the world, there has been a serious shortage of medical resources. In order to supplement the manpower and fear that medical staff's contact with patients will cause a breach in the epidemic, reduce the workload of nurses, and help nurses perform repetitive tasks so that nurses can concentrate more on the patient's condition. Therefore, this paper proposes M-Robot, which is a friendly interface service robot based on the Android system and can be controlled by voice, touch, and remote control in the medical care field. The system is mainly divided into two parts. One is the web server. The web server is divided into two parts: front-end and back-end. The front-end is responsible for friendly user interface management, and the back-end is for accessing the SQLite database, as well as processing speech recognition and semantic understanding in voice services. In the other part, we use TEMI robot to develop and complete the desired service. Its service content includes environment introduction, delivery service, questionnaire survey, broadcast car, scheduling reminder, follow-up record, and patient instruction video. In the voice control mode, the user can say the wake-up word to the robot and say the required service content, and the robot will execute after receiving the message;in the remote control mode, we provide a friendly web interface for remote control. As well as the information needed to manage various services. © 2022 IEEE.
ABSTRACT
In recent years, business environments are undergoing disruptive changes across sectors [1]. Globalization and technological advances, such as artificial intelligence and the internet of things, have completely redesigned business activities, bringing to light an ever-increasing interest and attention towards the customer [2], especially in healthcare sector. In this context, researchers is paying more and more attention to the introduction of new technologies capable of meeting the patients' needs [3, 4] and the Covid-19 pandemic has contributed and still contributes to accelerate this phenomenon [5]. Therefore, emerging technologies (i.e., AI-enabled solutions, service robots, conversational agents) are proving to be effective partners in improving medical care and quality of life [6]. Conversational agents, often identified in other ways as 'chatbots', are AI-enabled service robots based on the use of text [7] and capable of interpreting natural language and ensuring automation of responses by emulating human behavior [8, 9, 10]. Their introduction is linked to help institutions and doctors in the management of their patients [11, 12], at the same time maintaining the negligible incremental costs thanks to their virtual aspect [13-14]. However, while the utilization of these tools has significantly increased during the pandemic [15, 16, 17], it is unclear what benefits they bring to service delivery. In order to identify their contributions, there is a need to find out which activities can be supported by conversational agents.This paper takes a grounded approach [18] to achieve contextual understanding design and to effectively interpret the context and meanings related to conversational agents in healthcare interactions. The study context concerns six chatbots adopted in the healthcare sector through semi-structured interviews conducted in the health ecosystem. Secondary data relating to these tools under consideration are also used to complete the picture on them. Observation, interviewing and archival documents [19] could be used in qualitative research to make comparisons and obtain enriched results due to the opportunity to bridge the weaknesses of one source by compensating it with the strengths of others. Conversational agents automate customer interactions with smart meaningful interactions powered by Artificial Intelligence, making support, information provision and contextual understanding scalable. They help doctors to conduct the conversations that matter with their patients. In this context, conversational agents play a critical role in making relevant healthcare information accessible to the right stakeholders at the right time, defining an ever-present accessible solution for patients' needs. In summary, conversational agents cannot replace the role of doctors but help them to manage patients. By conveying constant presence and fast information, they help doctors to build close relationships and trust with patients. © 2022 IEEE.
ABSTRACT
The COVID-19 pandemic has had a massive impact on the global aviation industry. As a result, the airline industry has been forced to embrace new technologies and procedures in order to provide a more secure and bio-safe travel. Currently, the role of smart technology in airport systems has expanded significantly as a result of the contemporary Industry 4.0 context. The article presents a novel construction of an intelligent mobile robot system to guide passengers to take the plane at the departure terminals at busy airports. The robot provides instructions to the customer through the interaction between the robot and the customer utilizing voice communications. The usage of the Google Cloud Speech-to-Text API combined with technical machine learning to analyze and understand the customer's requirements are deployed. In addition, we use a face detection technique based on Multi-task Cascaded Convolutional Networks (MTCNN) to predict the distance between the robot and passengers to perform the function. The robot can guide passengers to desired areas in the terminal. The results and evaluation of the implementation process are also mentioned in the article and show promise.
ABSTRACT
The rapid robotization of the hotel industry faces reluctance from frontline employees. This study aims to explore frontline employees’ intentions to use service robots in the hotel workplace. Combining technology affordance theory and socio-material perspective, the study conducted four experiments pre-pandemic, amid-pandemic, and post-pandemic to test the proposed framework. The results reveal that hotel employees, especially those with low collectivism (vs. high), prefer a room service robot with physical affordance to a concierge robot with cognitive affordance because the former offers more relative advantages and higher trust. This main effect remained the same both pre- and amid-pandemic. During the pandemic, the COVID-19 compliance of guests showed a significant interaction effect on the employees’ intentions to use service robots in the workplace. The study findings provide meaningful implications for hoteliers selecting the correct type of robot for adoption and encouraging employees to use service robots.
ABSTRACT
The COVID-19 pandemic has accelerated the use of contactless service robots in hospitality industries. However, the key drivers of consumer behaviors against service robots have been ill-understood. This study examines the interactive relationships between the physical (visual features) and psychological (service autonomy) dimensions of service-robot anthropomorphism and their impacts on consumer acceptance of service robots. Adopting an experimental vignette method (EVM) with 402 participants, the study reveals that the impacts of visual features on consumers’ intention are affected by the level of service robots’ autonomy;particularly, consumers showed the highest intention when the robots have medium visual features and high autonomy while their intention became lower for the same level of visual features with low autonomy. Interestingly, consumers showed the lowest intention with high level visual features, regardless of the levels of autonomy. Our results also show that human identity threats and consumer resistance play a significant counterproductive mechanism between service robot anthropomorphism and consumers’ intention.
ABSTRACT
Deep learning based models on the edge devices have received considerable attention as a promising means to handle a variety of AI applications. However, deploying the deep learning models in the production environment with efficient inference on the edge devices is still a challenging task due to computation and memory constraints. This paper proposes a framework for the service robot named GuardBot powered by Jetson Xavier NX and presents a real-world case study of deploying the optimized face mask recognition application with real-time inference on the edge device. It assists the robot to detect whether people are wearing a mask to guard against COVID-19 and gives a polite voice reminder to wear the mask. Our framework contains dual-stage architecture based on convolutional neural networks with three main modules that employ (1) MTCNN for face detection, (2) our proposed CNN model and seven transfer learning based custom models which are Inception-v3, VGG16, denseNet121, resNet50, NASNetMobile, XceptionNet, MobileNet-v2 for face mask classification, (3) TensorRT for optimization of all the models to speedup inference on the Jetson Xavier NX. Our study carries out several analysis based on the models' performance in terms of their frames per second, execution time and images per second. It also evaluates the accuracy, precision, recall & F1-score and makes the comparison of all models before and after optimization with a main focus on high throughput and low latency. Finally, the framework is deployed on a mobile robot to perform experiments in both outdoor and multi-floor indoor environments with patrolling and non-patrolling modes. Compared to other state-of-the-art models, our proposed CNN model for face mask recognition based on the classification obtains 94.5%, 95.9% and 94.28% accuracy on training, validation and testing datasets respectively which is better than MobileNet-v2, Xception and InceptionNet-v3 while it achieves highest throughput and lowest latency than all other models after optimization at different precision levels.
ABSTRACT
Purpose>There has been a dramatic increase in the adoption of service robots in hotels, potentially replacing the human workforce. Drawing on Social Amplification of Risk Framework, this study aims to examine the moderating effect of transformational leadership on the indirect relationships between Gen Z employees’ tech-savviness and social skills on industry turnover intention via service robot risk awareness (SRRA).Design/methodology/approach>This study collected two-wave time-lagged multilevel data of 281 frontline Gen Z hotel employees from 54 departments in China. Participants were asked to rate their tech-savviness, social skills and SRRA in the first survey. They rated their supervisor’s transformational leadership and industry turnover intention one week later.Findings>Multilevel path analysis results showed SRRA mediates the negative indirect relationship of Gen Z employee’s tech-savviness and social skills on industry turnover intention. Transformational leadership weakened the positive effect of SRRA on industry turnover intention.Originality/value>This study contributes to the growing literature on service robots by investigating the antecedents and outcomes of employees’ SRRA. To the best of the authors’ knowledge, it is one of the first empirical studies investigating the role of leadership to mitigate the negative consequences of employee’s SRRA. Managers can use the results of this study to implement training programs and ensure that employees and service robots successfully coexist in the workplace.
ABSTRACT
One of the important requirements for service robots is attracting people by attractiveness and to be able to exchange messages with people. With reference to the traditional Japanese puppet show, Ningyo Joruri, we have independently developed OSONO, which is a physical robot, with high-quality choreography. In this paper, we report questionnaire evaluations on OSONO targeting a puppeteer expert group and compare with existing questionnaire results targeting the ordinary person. This shows that the method of creating OSONO and its choreography is effective in widely general. Additionally, we develop a remote evaluations system so that we can conduct questionnaires of OSONO in a short time for more evaluators. We also verify the effectiveness of this remote evaluation system. This system can be expected as a substitute for the conventional face-to-face evaluation, which is becoming difficult to conduct it due to the pandemic of COVID-19. © 2021 IEEE.
ABSTRACT
Purpose - The purpose of this study is to explore the acceptance of robots as social distancing agents and to understand how guests may respond to the application of service robots in a hospitality setting as a way to achieve a zero-COVID-19 travel experience. This study contributes to the current knowledge in the area of service robot application by providing a better insight of, and guests response to, service robot operation in hotels. Design/Methodology/Approach - To obtain information from participants, the semi-structured interview method was used. articipants were hotel guests who had stayed in hotels where robots performed human tasks. Data were analysed using thematic analysis. Findings - The findings suggest that robots are perceived as effective social distancing agents even though the participants experienced instances of robot incompetency during their stay at a hotel with robotics-based services. Participants also believe that with improved smart robot services, hotels can resume operations and guests can stay in hotels during the pandemic period without unnecessary worries. Originality - In light of the findings, some future research directions are suggested for researchers to further understand and explore the wider application of robotics in social distancing.
ABSTRACT
Purpose - This study aims to simultaneously examine the influence of demographic. psychographic and situational factors on consumers' willingness to pay a price premium (WTPp) for robotic restaurants and to profile market segments based on consumers WTPp levels (positive, neutral and negative). Design/methodology/approach - Using an online survey, the data were gathered from a sample of 897 Thai consumers who had dined at a robotic restaurant in the past 12 months. Structural equation modeling, chi-square tests and the one-way analysis of variance were used for data analysis. Findings - Demographic (gender, age, income and marital status), psychographic (perceived advantages/disadvantages, personal innovativens and personality traits) and situational factors (perceived health risk and self-protection behavior) significantly influence consumers' WTPp for robotic restaurants. The positive price premium group differs significantly from the neutral and negative price premium groups in terms of demographic, psychographic and situational profiles. Practical implications - The findings of this study help restaurateurs target the correct customers and set up appropriate price fences to safeguard profits and maximize return on investment. Originality/value - This study contributes to the literature on technology-based services and hospitality by heeding the calls made by lvanov and Webster (2021) and providing much-nwded empirical evidence of possible changes in consumers' WTPp for robot-delivered services in restaurants due to COVID-19.
ABSTRACT
Medical robotics is an interdisciplinary domain devoted to the development of electromechanical machines for therapeutic purposes. It has the history of 34 years in enabling the new medical treatments by giving physicians additional powers or by assisting them. The pandemic COVID-19 has changed the world wherein humans are hiding in the masks and doctors are highly pressurized to save the lives of humans. On this note, this paper is concerned with the service robots in a hospital ward to offer medication and monitor patients to help the doctors. The purpose of this paper is to compare two well-known grid-based algorithms BFS & DFS and conclude with the optimal path finding algorithm for the medical robots in a virtualized ward of 20x20 grid plan. The results are simulated in MATLAB and it is found that BFS is more worthy in terms of finding an optimal path for service robot in a hospital ward. © 2021 IEEE.