Service Robots Research Articles

Next generation employability and career sustainability in the hospitality industry 5.0

PurposeWith an industry 5.0 revolution taking place in the hospitality industry, a shift from manual to cognitive labor is anticipated, characterized by greater sustainability, resilience and a human-centric approach. In this regard, hospitality educators' ability and willingness to teach novel topics such as automation at work, upskilling of employees, man-machine interaction and service robots have become more important than ever. This study aims to interpret the perspectives of hospitality educators about bridging the gap in the employability skills of (next-gen) hospitality graduates and the concerns relating to career sustainability in times of transition.Design/methodology/approachA case study method was used given the novelty of the topic in a developing country like India. A qualitative survey with open-ended questions, is employed to understand the viewpoints of Indian hospitality educators, including those with more than 15 years of teaching experience. In-depth interviews were conducted with 23 hospitality educators to reach the theoretical saturation point. MAXQDA software was used to analyze the qualitative data collected in the study.FindingsThe findings reveal the challenges and motivations of hospitality educators in adapting to frequently changing business environments. In doing so, it sheds light on the methods employed to create a generation of hospitality graduates aligned with the changing dynamics of the industry.Originality/valueThe paper presents the viewpoints of hospitality educators in India in relation to a futuristic approach to next-gen employability and career sustainability. Whilst numerous studies have focused on the role of robots and artificial intelligence in replacing the human component of the service environment, the concept of people working alongside advanced technologies is fairly new and needs to be fully explored.

Understanding and Improving Consumer Reactions to Service Bots

Abstract Many firms are beginning to replace customer service employees with bots, from humanoid service robots to digital chatbots. Using real human–bot interactions in lab and field settings, we study consumers’ evaluations of bot-provided service. We find that service evaluations are more negative when the service provider is a bot versus a human—even when the provided service is identical. This effect is explained by consumers’ belief that service automation is motivated by firm benefits (i.e., cutting costs) at the expense of customer benefits (such as service quality). The effect is eliminated when firms share the economic surplus derived from automation with consumers through price discounts. The effect is reversed when service bots provide unambiguously superior service to human employees—a scenario that may soon become reality. Consumers’ default reactions to service bots are therefore largely negative but can be equal to or better than reactions to human service providers if firms can demonstrate how automation benefits consumers.

How Can Hospitality Industry Improve Customer Satisfaction by Determining the Relevant Degree of Robot Staff Implementation?

This study tackled the issues with service robots in hospitality industry and how to leverage robotic technology effectively. The implementation of robots in the hospitality industry was pushed to the boundary; however, customer satisfaction did not improve much as people still complain about the lengthy check-in process, the confusing communication, and other common complaints; there are more associated issues, such as underwhelming welcome, robots do not understand a specific accent, robots run out of battery, robots struggle to understand guests’ requests. Another issue was that some guests were uncomfortable with being served by service robots. The study aimed to determine the relevant degree of robotic staff implementation in the hospitality industry to improve customer satisfaction. For the empirical analysis, the data are coming from the given-out survey, including people who work at the hotel and people who do not work at the hotel, to gather comprehensive opinions from people with diversified occupancy. The study conducted a multiple-choice survey and a short answer question to leave them blank for people who want to express their feelings for service robots.

Mission Specification Patterns for Mobile Robots: Providing Support for Quantitative Properties

With many applications across domains as diverse as logistics, healthcare, and agriculture, service robots are in increasingly high demand. Nevertheless, the designers of these robots often struggle with specifying their tasks in a way that is both human-understandable and sufficiently precise to enable automated verification and planning of robotic missions. Recent research has addressed this problem for the functional aspects of robotic missions through the use of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">mission specification patterns</i> . These patterns support the definition of robotic missions involving, for instance, the patrolling of a perimeter, the avoidance of unsafe locations within an area, or reacting to specific events. Our paper introduces a catalog of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">QUantitAtive RoboTic mission spEcificaTion patterns</i> (QUARTET) that tackles the complementary and equally important challenge of specifying the reliability, performance, resource usage, and other key quantitative properties of robotic missions. Identified using a methodology that included the analysis of 73 research papers published in 17 leading software engineering and robotics venues between 2014–2021, our 22 QUARTET patterns are defined in a tool-supported domain-specific language. As such, QUARTET enables: (i) the precise definition of quantitative robotic-mission requirements and (ii) the translation of these requirements into probabilistic reward computation tree logic (PRCTL), supporting their formal verification and automated planning of robotic missions. We demonstrate the applicability of QUARTET by showing that it supports the specification of over 95% of the quantitative robotic mission requirements from a systematically selected set of recent research papers, of which 75% can be automatically translated into PRCTL for the purposes of verification through model checking and mission planning.

Artificial Intelligence: The Significance of Tesla Bot

The rising humanoid robot industry has brought significant interest from the public and treasure seekers who see this industry as the future. Tesla, a leading motor manufacturer and artificial intelligence company, has also joined the competition of humanoid robots by introducing “Optimus.” The service robotics industry, which “Optimus” focuses on, is fast-growing, with approximately 119% growth over the last four years. Compared with its competitors, Tesla has a few advantages. First of all, it has abundant experience in the field as it has accumulated countless data on human behavior through the beta that its Tesla cars have provided over the years. In addition, Tesla has more capital than its competitors. As of September 10th, 2021, Tesla has a market capital of 939 billion dollars. However, Tesla is also known for its overconfidence in its products, as it usually exaggerates the delivery date and the capability of its products. Another obstacle that Tesla faces comes from the industry itself. Admittedly, it is not a mature industry without much experience that Tesla can learn from. As a result, like many of its competitors, Tesla has to develop all of its technologies in-house, leading to the high prices of humanoid robots on the market. However, suppose Tesla is able to develop a complete version of “Optimus” faster and better than its competitors. In that case, it is very likely for Tesla to become the leader in the humanoid robot industry.

Socially Aware Robot Navigation Framework: Where and How to Approach People in Dynamic Social Environments

This paper proposes a human approaching robot navigation framework that enables a mobile service robot to (i) estimate a socially optimal approaching pose, and (ii) navigate safely and socially to the estimated approaching pose. In the first stage, the robot estimates potential approaching poses of a human or a human group, which the robot can safely and socially approach, using the dynamic social zone model. In the second stage, the proposed framework selects a socially optimal approaching pose, then estimate a socially optimal trajectory of the robot using the proposed goal-oriented timed elastic band (GTEB) model. The developed GTEB model takes into account the current robot’s states, robot dynamics, dynamic social zone, regular obstacles and potential approaching poses to generate the socially optimal robot trajectory from the robot’s current pose to the selected optimal approaching pose. The motion control command extracted from the socially optimal trajectory is then utilized to drive the mobile robot to approach the individual humans or human groups, while safely and socially avoiding regular obstacles, human and human groups during the navigation process. The proposed approaching human framework is verified in the both simulation and real robots. The results illustrate that, the mobile robot equipped with our developed GTEB model is able to safely and socially approach and avoid individual humans and human groups, while guaranteeing the comfortable safety for the humans and socially acceptable behaviors for the robot. Note to Practitioners—Although our proposed GTEB model is capable of estimating a socially optimal approaching pose and social robot trajectory, driving the robot to approach a human and a human group, and providing the safety and comfort for humans and socially acceptable behaviors of the robot, there exists a few drawbacks if we wish to apply the proposed approaching human framework in dynamic social environments. First, the optimizer for the GTEB model should be improved in terms of computational time and accuracy to avoid generating unpredictable robot trajectories, especially in dynamic social environments. Second, the socio-spatio-temporal characteristics of the humans including human position, motion and orientation, and human group and human–object interactions play an important role in the proposed GTEB model. However, the existing techniques are only suitable in quasi-dynamic social environments. Hence, highly accurate, robust and real-time algorithms for human detection and tracking, and social interaction detection are necessary. Third, social interactive intentions such as human–robot, human–human and human–object interactive intentions should be predicted and incorporated into the proposed framework to improve the performance of the developed framework in the dynamic social environments. Last but not least, human and human group identification algorithms should be proposed to enable the robot to identify the humans, whose the mobile robot is requested to approach. In the future, the social interactive intentions, the human’s future states and its trajectories and will be predicted using deep learning algorithms and incorporated into the approaching human framework to improve the performance of the proposed framework.

Design of Mopping Robot

With the development of Internet of Things and intelligence, cleaning mobile robots have gradually replaced the traditional manual cleaning work, and intelligence has become the main research direction of sweeping robots. As a new product in the field of service robot, mopping robot has made some achievements, but there are still many key technical problems to be solved or improved. In order to improve the safety of products in public places such as shopping malls, a robot with multiple cleaning functions such as vacuuming, mopping and drying is proposed. The mechanical mechanisms mainly include a frame, a three wheel walking mechanism, a sweeping mechanism, a mopping mechanism, a drying mechanism, etc. It can realize wifi control and mobile phone APP control, and solve the problems of the existing mopping robot such as single function, low cleaning efficiency, and the danger of slippery ground.

Human-Aware Path Planning With Improved Virtual Doppler Method in Highly Dynamic Environments

Human-aware path planner is essential for achieving harmonious coexistence between humans and robots in highly dynamic environments. In this paper, we propose an integrated framework to find the optimal path in the complex environment with considering collision risk, social norms, and crowded areas. In the proposed framework, a general dynamic group model (g-space) based on the Gaussian Mixed Model (GMM) is proposed as the social norms of dynamic groups, which not only considers the factors of humans (e.g., pose, quantity, distribution, psychology) but also establishes the proximity and human interacting constraints of dynamic groups. An integrated Collision Risk and Human Space (CR&HS) model is applied to achieve human-acceptable behaviors, in which both collision avoidance, human comfort, and interference-free constraints have been involved. Moreover, an Improved Virtual Doppler Method (IVDM) has been used to realize safety navigation to avoid the robot falling into the crowded area. Finally, the proposed framework has been utilized with the sampling-based rapidly-exploring random tree. Experimental results demonstrate that the proposed method can generate the optimal human-aware collision-free path in complex environments. Note to Practitioners—This paper aims to plan an optimal trajectory for the robot in highly dynamic environments. In this field, it is still a challenging task to plan a trajectory with collision-free, human-aware, and crowd-aware. To do that, we present an integrated framework to generate the optimal trajectory by involving the collision risk, social norms, and human density. First, the g-space model is adopted as interference-free constraints of dynamic groups. The integrated knowledge fusion model (CR&HS) then penalizes the manners which have higher collision risk and adverse effects on human interaction or human comfortable. Besides, human motion and density are provided to a robot by IVDM. The proposed framework is utilized in the sampling-based rapidly-exploring random tree as the evaluation module. Finally, the feasibility and reliability of the proposed method have been verified by experiments in different simulated environments. The proposed framework can be applied in most mobile service robots to achieve human-friendly manners.

Scheduling of decentralized robot services in cloud manufacturing with deep reinforcement learning

• Both Deep Q-Networks (DQN) and Dueling Deep Q-Networks (DDQN)-based algorithms for scheduling of decentralized robot services are proposed. • Performance of different algorithms, including DQN, DDQN, and other three benchmark algorithms, indicates that DDQN performs the best with respect to each indicator. • Effects of different combinations of weight coefficients and influencing degrees of different indicators on the overall scheduling objective are analyzed. Cloud manufacturing is a service-oriented manufacturing model that offers manufacturing resources as cloud services. Robots are an important type of manufacturing resources. In cloud manufacturng, large-scale distributed robots are encapsulated into cloud services and provided to consumers in an on-demand manner. How to effectively and efficiently manage and schedule decentralized robot services in cloud manufacturing to achieve on-demand provisioning is a challenging issue. During the past few years, Deep Reinforcement Learning (DRL) has become very popular and successfully been applied to many different areas such as games, robotics, and manufacturing. DRL also holds tremendous potential for solving scheduling issues in cloud manufacturing. To this end, this paper is devoted to exploring effective approaches for scheduling of decentralized robot manufacturing services in cloud manufacturing with DRL. Specifically, both Deep Q-Networks (DQN) and Dueling Deep Q-Networks (DDQN)-based scheduling algorithms are proposed. Performance of different algorithms, including DQN, DDQN, and other three benchmark algorithms, indicates that DDQN performs the best with respect to each indicator. Effects of different combinations of weight coefficients and influencing degrees of different indicators on the overall scheduling objective are analyzed. Results indicate that the DDQN-based scheduling algorithm is able to generate scheduling solutions efficiently.

Robust Task Scheduling for Heterogeneous Robot Teams Under Capability Uncertainty

This article develops a stochastic programming framework for multiagent systems, where task decomposition, assignment, and scheduling problems are simultaneously optimized. The framework can be applied to heterogeneous mobile robot teams with distributed subtasks. Examples include pandemic robotic service coordination, explore and rescue, and delivery systems with heterogeneous vehicles. Owing to their inherent flexibility and robustness, multiagent systems are applied in a growing range of real-world problems that involve heterogeneous tasks and uncertain information. Most previous works assume one fixed way to decompose a task into roles that can later be assigned to the agents. This assumption is not valid for a complex task where the roles can vary and multiple decomposition structures exist. Meanwhile, it is unclear how uncertainties in task requirements and agent capabilities can be systematically quantified and optimized under a multiagent system setting. A representation for complex tasks is proposed: agent capabilities are represented as a vector of random distributions, and task requirements are verified by a generalizable binary function. The conditional value at risk is chosen as a metric in the objective function to generate robust plans. An efficient algorithm is described to solve the model, and the whole framework is evaluated in two different practical test cases: capture-the-flag and robotic service coordination during a pandemic (e.g., COVID-19). Results demonstrate that the framework is generalizable, is scalable up to 140 agents and 40 tasks for the example test cases, and provides low-cost plans that ensure a high probability of success.

서비스 로봇의 사용자 수용 의도에 영향을 미치는 요인에 관한 연구

The purpose of this study is to investigate the factors that affect the acceptance intention of service robots in the service counter situation, targeting general consumers who have experience using robots. Based on the UTAUT2 model, we investigated the effects of robot-related characteristics (perceived anthropomorphism, perceived interaction) and user characteristics (perceived risk, self-efficacy) on acceptance intentions. We collected data from 308 individuals and used factor analysis and structural equations for hypothesis testing. Analysis showed that perceived anthropomorphism, self-efficacy, performance expectations, effort expectations, and hedonic motivations influenced use intentions. Furthermore, it was confirmed that perceived anthropomorphism influences performance expectations and pleasure motivations, and that perceived risk influences effort expectations. Perceived interaction and self-efficacy affected both UTAUT2 variables. In order to provide a better customer-centric service environment, we explored the factors that must be prepared before considering technology. We hope that we can provide academic significance and practical suggestions for future research.

사용 목적에 따른 서비스 로봇 인터랙션 디자인 사례 연구

Recently, the development of service robots that can assist or replace human work is accelerating by utilizing technological advances such as artificial intelligence and autonomous driving. Through this, the size of the global service robot market is increasing, and it is expected that the contact point between humans and robots will increase in real life according to these changes. Therefore, research on human-robot interaction (HRI) for smooth communication is a meaningful research topic. This study examined various interaction cases of foreign and domestic service robots. The human-likeness of the robot was derived as an evaluation factor, and the service robot was classified into functional and relational types according to the role of the robot, and it was analyzed by comparing the utilization of interaction feedback. As a result, relational service robots have higher utilization of interaction feedback in visual appearance, touch, movement, interaction behavior, and non-verbal communication than functional service robots. In the case of sound interaction, it was highly utilized regardless of relationship type and functional type, and the interaction between verbal communication and social behavior was highly utilized for functional service robots. Through this study, it is expected that a meaningful direction can be presented by helping to supplement and improve the interaction of many service robots to be developed in the future.

호텔 서비스 로봇의 의인화 수준이 로봇 서비스품질 기대에 미치는 영향

With the advent of artificial intelligence, more service robots are used to provide various services. As the use of service robots increases, anthropomorphic service robots, rather than simple machines, have been introduced to the hotel industry, and the social interactiveness of such anthropomorphic robots can improve consumers' perception of service quality. Therefore, this study aims to examine how perceived robot service quality differs based on the levels of hotel service robots ’anthropomorphism and customers' gadget-loving tendencies. According to the study results, first, the higher the anthropomorphism level of service robots, the higher the perceived robot service quality, namely in tenets like Automation, Personalisation and Efficiency. Second, looking at how gadget-loving tendency moderated the relationship between the levels of service robots’ anthropomorphism and the expected robot service quality, this study confirmed a significant interaction effect of Automation, Efficiency, and Precision. This study expands the scope of service robot research by categorizing hotel service robots by their anthropomorphism level and verifying differences in consumer perceptions; improves the understanding of consumer behavior by segmenting consumers by their gadget-loving tendencies; and provides a basis for designing hotel service robots to increase consumer satisfaction.

AI 서비스 로봇의 개발 방향 및 제언

The service robot market is globally driven by the advancement in robotics, increase in robot automation demand, and rise in living standards. This study proposes the development direction of AI service robots by classifying and organizing them according to their application field and scope and then examining the market trends and prospects of domestic and foreign AI service robots. AI service robots are those whose applications have been expanded beyond manufacturing and into domestic, medical, national defense, firefighting, and agriculture, as distinguished from industrial robots. The global market of service robots reached $35.24 billion in 2021 and is expected to grow up to $140.94 billion by 2027, with an average annual growth rate of 21.9%. The short-term and long-term strategies of AI service robots were presented by reflecting technical feasibility and social requirements.

Adoption of service robots: exploring the emerging trends through the lens of bibliometric analysis

PurposeThe aim of this study is to examine the existing literature on service robots in order to identify prominent themes, assess the present state of service robotics research and highlight the contributions of seminal publications in the business, management and hospitality domain.Design/methodology/approachThis study analysed 332 Scopus papers from 1985 to 2022 using bibliometric techniques like citation and co-citation analysis.FindingsThe study findings highlighted that there has been a consistent rise in publications related to service robots. The paper identifies three significant themes in the service robot literature: adoption of service robots in the context of customer service, anthropomorphism and integration of artificial intelligence in robotic service. Furthermore, this study highlights prominent authors, journals, institutions and countries associated with research on service robots and discusses the future research opportunities in this domain.Originality/valueThis study contributes to the service robots’ literature in the hospitality context by compilation of various reference materials using a comprehensive bibliometric analysis. Previous studies do not point out crucial themes in this area, nor do they provide an overview of prominent journals, institutions, authors and trends in this field. Therefore, this study attempts to fill the lacunae.

The Psychological and Behavioral Impact of the Use of Service Robots on Customers based on the S-O-R Theory Perspective

This paper focuses on the impact of service robots on customer psychology and behavior, systematically reviews the current service marketing research literature that focuses on service robots. This paper first compares the characteristics of service robots with those of human employees, and then presents the salient features of service robots in the provision of services. Finally, the paper discusses what can be further researched in the field of service robotics in the context of future artificial intelligence, complementing the existing research framework and suggesting new ideas for the study of artificial intelligence services.

Evolution of Socially-Aware Robot Navigation

In recent years, commercial and research interest in service robots working in everyday environments has grown. These devices are expected to move autonomously in crowded environments, maximizing not only movement efficiency and safety parameters, but also social acceptability. Extending traditional path planning modules with socially aware criteria, while maintaining fast algorithms capable of reacting to human behavior without causing discomfort, can be a complex challenge. Solving this challenge has involved the development of proactive systems that take into account cooperation (and not only interaction) with the people around them, the determined incorporation of approaches based on Deep Learning, or the recent fusion with skills coming from the field of human–robot interaction (speech, touch). This review analyzes approaches to socially aware navigation and classifies them according to the strategies followed by the robot to manage interaction (or cooperation) with humans.

Антропоморфные роботы в медицине: варианты технологий и перспективы.

Introduction. Anthropomorphic robots may eventually occupy a special niche in medicine, performing physically demanding, as well as emotionally monotonous work. As artificial intelligence is introduced, ARs will become more complex and their functions will expand. Materials and methods. For a review on anthropomorphic robots, foreign and Russian scientific publications were studied. It should be noted that the Russian segment of scientific publications for the keywords «anthropomorphic robots», «humanoid robots», «social robots» is extremely small and does not reflect the current world practice. 20 articles were selected from foreign sources. 2 own classifications have been developed - medical robots and anthropomorphic medical robots. Results. In recent years, there has been an explosive growth in the development of robotics. Along with industrial, military, service robots, the development of medical robots is going its own way. A universal anthropomorphic robot is a complex combination of a musculoskeletal system, mechanical limbs, a voice recognition system, space and neural networks capable of processing and understanding the environment and voice commands. The hallmark of AR at the time of writing: lack of cognitive ability and physical autonomy. The use of AR in certain medical fields is described, an attempt is made to determine the social aspects of AR. Conclusions. The greatest demand for anthropomorphic (humanoid) robots in medicine will be for caring for the elderly and for patients with limited motor functions, during pandemics or other potentially life-threatening situations. In the future, at the stage of introducing artificial intelligence, a thorough study of the legislation and the security system of such robots is required. Currently, the cost of AR is quite high, but in the future, cost reduction will be associated with the absence of costs for changing the environment for using such robots, which may be economically justified. The most important aspect of the use of AR is emotional interaction with a person and the safety of the latter.

Developing a Machine Learning Algorithm for Service Robots in Industrial Applications

Robots, which have mostly been effective in areas such as industrial, agricultural, and production facilities, have started to take a place in the service sector, as their technologies have become lower in cost and more easily accessible. This situation has attracted the attention of companies and researchers and has accelerated studies on this subject. In this study, an algorithm was developed for the autonomous mobile robot to serve in industrial areas. In line with this study, it was ensured that the autonomous mobile robot mapped the working environment, determined the working station in this environment, and then carried out transport operations between these working stations in accordance with a given work order. After the mobile robot fulfilled the work order, it went into a waiting state until a new work order was received. For the mobile robot to save energy, it was ensured that it waited close to the point where the work order came in the most, by means of machine learning in the waiting position. The developed algorithms were designed using the NI LabVIEW environment and then simulated in the RobotinoSIM environment and physically tested using the Robotino autonomous mobile robot platform. The experimental results showed that mapping and location reporting using an RGB camera, odometry, and a QR code eliminated permanent location errors, and the robot completed 50 work orders with 100% accuracy.

A Scalable Tree Based Path Planning for a Service Robot

Path planning plays a vital role in a mobile robot navigation system. It essentially generates a shortest traversable path between two given points. There are many path planning algorithms proposed by researchers all over the world, however, there are very little work focussing on path planning for a service environment. The general assumption are either the environment is fully known or unknown. Both the cases will not be suitable for a service environment. Afully known environment will restrict further expansion in terms of number of navigation points and unknown environment would give an inefficient path. Unlike other environments, service environments have certain factors to be considered, like user friendliness, repeatability, scalability and portability which are very essential for a service robot. In this paper a simple, efficient, robust and environment independent path planning algorithm for an indoor mobile servicerobot is presented. Initially the robot is trained to navigate to all the possible destinations sequentially with minimal user interface which will ensure the robot knows partial paths in the environment. With the trained data the path planning algorithm maps all the logical paths between all the destinations, which helps in autonomous navigation. The algorithm is implemented and tested using 2D simulator Player/Stage. The proposed system is tested with two differentservice environment layouts and proved to have features like scalability, trainability, accuracy and repeatability. The algorithm is compared with various classical path planning algorithms and the results shows that proposed path planning algorithm is in par with the other algorithms in terms of accuracy and efficient path generation.