Search results

To ensure the motion attitude and stable contact force of massage robot working on unknown human tissue environment, this study aims to propose a robotic system for autonomous massage path planning and stable interaction control.

First, back region extraction and acupoint recognition based on deep learning is proposed, which provides a basis for determining the working area and path points of the robot. Second, to realize the standard approach and movement trajectory of the expert massage, 3D reconstruction and path planning of the massage area are performed, and normal vectors are calculated to control the normal orientation of robot-end. Finally, to cope with the soft and hard changes of human tissue state and body movement, an adaptive force tracking control strategy is presented to compensate the uncertainty of environmental position and tissue hardness online.

Improved network model can accomplish the acupoint recognition task with a large accuracy and integrate the point cloud to generate massage trajectories adapted to the shape of the human body. Experimental results show that the adaptive force tracking control can obtain a relatively smooth force, and the error is basically within ± 0.2 N during the online experiment.

This paper incorporates deep learning, 3D reconstruction and impedance control, the robot can understand the shape features of the massage area and adapt its planning massage path to carry out a stable and safe force tracking control during dynamic robot–human contact.

The purpose of this study is to evaluate service robots as an alternative service provider that can reduce customers’ social discomfort in hospitality service encounters. Specifically, the authors discuss when and in what scenarios service robots can alleviate such social discomfort and explain this effect from the perspective of dehumanization.

Following a social constructivist paradigm, the authors adopt a qualitative research design, gathering data through 21 semistructured interviews to explore why the presence of service employees causes customers’ social discomfort in hospitality service encounters and how service robots alleviate such discomfort.

This study’s results suggest that both the active and passive engagement of service employees are sources of customers’ social discomfort in hospitality service encounters; thus, adopting service robots can help reduce such discomfort in some scenarios. Customers’ differentiating behaviors, a downstream effect of social discomfort, are also addressed.

Service robots can reduce customers’ social discomfort in certain scenarios and influence their consumption behaviors. This finding offers actionable insights regarding the adoption of service robots in hospitality service encounters.

This research enhances the understanding of social discomfort in hospitality service encounters and expands the research on service robots. To the best of the authors’ knowledge, it is the first attempt to reveal the bright side of robots in service encounters from a dehumanization perspective.

Maintaining the safety of the human is a major concern in factories where humans co-exist with robots and other physical tools. Typically, the area around the robots is monitored using lasers. However, lasers cannot distinguish between human and non-human objects in the robot’s path. Stopping or slowing down the robot when non-human objects approach is unproductive. This research contribution addresses that inefficiency by showing how computer-vision techniques can be used instead of lasers which improve up-time of the robot.

A computer-vision safety system is presented. Image segmentation, 3D point clouds, face recognition, hand gesture recognition, speed and trajectory tracking and a digital twin are used. Using speed and separation, the robot’s speed is controlled based on the nearest location of humans accurate to their body shape. The computer-vision safety system is compared to a traditional laser measure. The system is evaluated in a controlled test, and in the field.

Computer-vision and lasers are shown to be equivalent by a measure of relationship and measure of agreement. R² is given as 0.999983. The two methods are systematically producing similar results, as the bias is close to zero, at 0.060 mm. Using Bland–Altman analysis, 95% of the differences lie within the limits of maximum acceptable differences.

In this paper an original model for future computer-vision safety systems is described which is equivalent to existing laser systems, identifies and adapts to particular humans and reduces the need to slow and stop systems thereby improving efficiency. The implication is that computer-vision can be used to substitute lasers and permit adaptive robotic control in human–robot collaboration systems.

This study aims to propose a research model integrating technology acceptance model 3 (TAM3) constructs and human aspects of humanoid service robots (HSRs), measured by the Godspeed questionnaire series and tested across two hotel properties in Japan and the USA.

Potential participants were approached randomly by email invitation. A final sample size of 395 across two hotels, one in Japan and the other in the USA, was obtained, and the data were analysed using structural equation modelling.

The results confirm that perceived usefulness, driven by subjective norms and output quality, and perceived ease of use, driven by perceived enjoyment and absence of anxiety, are the immediate direct determinants of users’ re-patronage intentions for HSRs. Results also showed that users prefer anthropomorphism, perceived intelligence and the safety of an HSR for reusing it.

The findings have practical implications for the hospitality industry, suggesting multiple attributes of an HSRs that managers need to consider before deploying them in their properties.

The current study proposes an integrated model determining factors that affect the re-patronage of HSRs in hotels.

This study aims to conceptualize the characteristics of artificial intelligence (AI) dogs while exploring their applications in tourism and hospitality settings.

The total of 30 in-depth interviews were conducted, and data were analyzed through thematic analysis.

This study proposed differences between AI dogs and real dogs and human-like robots, core characteristics of AI dogs’ functions, a matrix of appearance and expectation regarding intelligence for AI dogs and human-like robots, the relationship between ethical barriers and task complexity, adoptions of AI dogs in different user segments and practical applications in hospitality and tourism settings, such as restaurants, city tour guides, extended-stay resorts and event organizations.

This research advances the field of tourism and hospitality studies by introducing the new concept of AI dogs and their practical applications. This present study adds new insights into the opportunities and contexts of human–robot interaction in the field of tourism and hospitality.

To the best of the authors’ knowledge, this research is one of the first studies of AI dogs in tourism and hospitality.

Applications of artificial intelligence (AI), such as virtual and physical service robots, generative AI, large language models and decision support systems, alter the nature of services. Most service research centers on the division between human and AI resources. Less attention has been paid to analyzing the entangled resource relations and interactions between humans and AI entities. Thus, the purpose of this paper is to extend our metatheoretical understanding of resource integration and value cocreation by analyzing different human–AI resource relations in service ecosystems.

The conceptual paper adapts a novel framework from postphenomenology, specifically cyborg intentionality. This framework is used to analyze what kinds of human–AI resource relations enable resource integration and value cocreation in service ecosystems.

We conceptualize seven different human–AI resource relations, namely background, embodiment, hermeneutic, alterity, cyborg, immersion and composite relation. The sociotechnical entangled perspective on human–AI resource relations challenges and reframes our understanding of interactions between humans and nonhumans in resource integration and value cocreation and the distinction between operant and operand resources in service research.

Our primary contribution to researchers and service providers is dissolving the distinction between operant and operand resources. We present two foundational propositions. 1. Humans and AI become entangled value cocreating resources in inherently sociotechnical service ecosystems; and 2. Human and AI entanglements in value cocreation manifest through seven resource relations in inherently sociotechnical service ecosystems. Understanding the combinatorial potential of different human–AI resource relations enables service providers to make informed choices in service ecosystems.

The present paper aims to address challenges associated with path planning and obstacle avoidance in mobile robotics. It introduces a pioneering solution called the Bi-directional Adaptive Enhanced A* (BAEA*) algorithm, which uses a new bidirectional search strategy. This approach facilitates simultaneous exploration from both the starting and target nodes and improves the efficiency and effectiveness of the algorithm in navigation environments. By using the heuristic knowledge A*, the algorithm avoids unproductive blind exploration, helps to obtain more efficient data for identifying optimal solutions. The simulation results demonstrate the superior performance of the BAEA* algorithm in achieving rapid convergence towards an optimal action strategy compared to existing methods.

The paper adopts a careful design focusing on the development and evaluation of the BAEA* for mobile robot path planning, based on the reference [18]. The algorithm has remarkable adaptability to dynamically changing environments and ensures robust navigation in the context of environmental changes. Its scale further enhances its applicability in large and complex environments, which means it has flexibility for various practical applications. The rigorous evaluation of our proposed BAEA* algorithm with the Bidirectional adaptive A* (BAA*) algorithm [18] in five different environments demonstrates the superiority of the BAEA* algorithm. The BAEA* algorithm consistently outperforms BAA*, demonstrating its ability to plan shorter and more stable paths and achieve higher success rates in all environments.

The paper adopts a careful design focusing on the development and evaluation of the BAEA* for mobile robot path planning, based on the reference [18]. The algorithm has remarkable adaptability to dynamically changing environments and ensures robust navigation in the context of environmental changes. Its scale further enhances its applicability in large and complex environments, which means it has flexibility for various practical applications. The rigorous evaluation of our proposed BAEA* algorithm with the Bi-directional adaptive A* (BAA*) algorithm [18] in five different environments demonstrates the superiority of the BAEA* algorithm.

The rigorous evaluation of our proposed BAEA* algorithm with the BAA* algorithm [18] in five different environments demonstrates the superiority of the BAEA* algorithm. The BAEA* algorithm consistently outperforms BAA*, demonstrating its ability to plan shorter and more stable paths and achieve higher success rates in all environments.

The originality of this paper lies in the introduction of the bidirectional adaptive enhancing A* algorithm (BAEA*) as a novel solution for path planning for mobile robots. This algorithm is characterized by its unique characteristics that distinguish it from others in this field. First, BAEA* uses a unique bidirectional search strategy, allowing to explore the same path from both the initial node and the target node. This approach significantly improves efficiency by quickly converging to the best paths and using A* heuristic knowledge. In particular, the algorithm shows remarkable capabilities to quickly recognize shorter and more stable paths while ensuring higher success rates, which is an important feature for time-sensitive applications. In addition, BAEA* shows adaptability and robustness in dynamically changing environments, not only avoiding obstacles but also respecting various constraints, ensuring safe path selection. Its scale further increases its versatility by seamlessly applying it to extensive and complex environments, making it a versatile solution for a wide range of practical applications. The rigorous assessment against established algorithms such as BAA* consistently shows the superior performance of BAEA* in planning shorter paths, achieving higher success rates in different environments and cementing its importance in complex and challenging environments. This originality marks BAEA* as a pioneering contribution, increasing the efficiency, adaptability and applicability of mobile robot path planning methods.

The purpose of this paper is to evaluate the barriers militating against the adoption of robotics in the construction industry.

Robotics implementation barriers were obtained from the previous studies and then through questionnaire survey construction stakeholders in Nigeria evaluate these barriers. Consequently, these barriers were examined via the exploratory factor analysis (EFA) technique. Furthermore, a model of these barriers was implemented by means of a partial least square structural equation modeling (PLS-SEM).

The EFA results showed that these barriers could be categorized into two: cost and technology. Results obtained from the proposed model showed that platform tools were crucial tools for implementing cloud computing.

The novelty of this research work will be provided a solid foundation for critically assessing and appreciating the different barriers affecting the adoption of robotics.

The study seeks to understand the possible opportunities that Industry 5.0 might offer for various aspects of inclusive sustainability. The study aims to discuss existing perspectives on the classification of Industry 5.0 technologies and their underlying role in materializing the sustainability values of this agenda.

The study systematically reviewed Industry 5.0 literature based on the PRISMA protocol. The study further employed a detailed content-centric review of eligible documents and conducted evidence mapping to fulfill the research objectives.

The advancement of Industry 5.0 is currently underway, with noteworthy initial contributions enriching its knowledge base. Although a unanimous definition remains lacking, diverse viewpoints emerge concerning the recognition of fundamental technologies and the potential for yielding sustainable outcomes. The expected contribution of Industry 5.0 to sustainability varies significantly depending on the context and the nature of underlying technologies.

Industry 5.0 holds the potential for advancing sustainability at both the firm and supply chain levels. It is envisioned to contribute proportionately to the three sustainability dimensions. However, the current discourse primarily dwells in theoretical and conceptual domains, lacking empirical exploration of its practical implications.

This study comprehensively explores diverse perspectives on Industry 5.0 technologies and their potential contributions to economic, environmental and social sustainability. Despite its promise, the practical evidence supporting the effectiveness of Industry 5.0 remains limited. Certain conditions are necessary to realize the benefits of Industry 5.0 fully, yet the mechanisms behind these conditions require further investigation. In this regard, the study suggests several potential areas for future research.

In an era where complex technological advances increasingly govern service delivery, it is incumbent on service firms to pioneer innovative strategies to sustain customer engagement and cultivate loyalty. This conceptual paper examines the transformative potential of artificial intelligence (AI) in the realm of online customer communities, with a particular focus on its creation, management and enhancement facets. The authors explore how AI can revolutionize the dynamics of customer interaction, feedback mechanisms and overall engagement within the service industry.

This conceptual paper draws from marketing and management literature focusing on customer communities and AI in service and customer engagement contexts with a robust future research agenda.

A classification of online customer community engagement is provided along with a conceptual framework to guide our understanding of the integration of AI into online customer communities.

This exploration underscores the imperative for service firms to embrace AI-driven approaches to online customer community management, not only as a means to optimize their operations but as a vital strategy to stay competitive in the ever-evolving digital landscape. This paper examines the novel combination of AI with online customer communities and provides the framework in the form of an input-process-output (IPO) model for future research into this integration.