Search Results (178)

Industry 5.0 involves the integration of advanced technologies, collaboration between humans and intelligent machines, resilience and sustainability, all of which are essential for the advancement of the food services industry. This analysis reviews the scientific literature on Industries 4.0 and 5.0 technologies, whether experimental or implemented, focused on producing large meals in food service. The review has been conducted through a systematic search, covering aspects from consumer ordering and the cooking process to distribution while considering management, quality control, and sustainability. A total of thirty-one articles, published between 2006 and 2025, were selected, with the majority focusing on Industry 5.0 (71%) and a significant proportion on testing phases (77.4%). In the context of Food Service Perspectives, the emphasis has been placed on customer service (32.3%), highlighting the use of Artificial Intelligence (AI)-powered robots for serving customers and AI for service personalization. Sustainability has also received attention (29%), focusing on AI and machine learning (ML) applications aimed at waste reduction. In management (22.6%), AI has been applied to optimize production schedules, enhance menu engineering, and improve overall management. Big Data (BD) and ML were utilized for sales analysis, while Blockchain technology was employed for traceability. Cooking innovations (9.7%) centered on automation, particularly the use of collaborative robots (cobots). For Quality Control (6.4%), AI, along with the Internet of Things (IoT) and Cloud Computing, has been used to monitor the physical aspects of food. The study underscores the importance of strategic investments in technology to optimize processes and resources, personalize services, and ensure food quality, thereby promoting balance and sustainability. Full article

Social robots in public cultural venues, such as science museums, must engage diverse visitors through brief, one-off encounters where long-term user modeling is infeasible. This research examines immediately interpretable behavioral cues of a robot that can evoke a sense of personalization without storing or profiling individual users. First, a video-based observational study of expert and novice museum docents identified service strategies that enable socially adaptive communication. Building on these insights, three controlled laboratory studies investigated how specific cues from robots influence user perception. A video-based controlled study examined how recognition accuracy shapes users’ social impressions of the robot’s intelligence. Additional studies based on the Wizard-of-Oz (WoZ) method tested whether explanatory content aligned with participants’ background knowledge and whether explicit preference inquiry and memory-based continuity strengthened perceptions of personalization. Results showed that recognition accuracy improved social impressions, whereas knowledge alignment, explicit preference inquiry, and memory-based continuity cues increased perceived personalization. These findings demonstrate that micro-level personalization cues, interpretable within a short-term encounter, can support user-centered interaction design for social robots in public environments. Full article

In recent years, an increasing interest in artificial intelligence applications in a widespread spectrum of fields which include, among others, robotics, communications, artistic creations, security and protection technologies, etc., has been observed. Of the latter categories, one field which has largely benefitted is surveillance and security technologies. This fact is combined with an increase in omnipresent automatic surveillance system installations which pave the way to new technologies. Technologies that are being promoted are the ones offering uninterrupted, robust, efficient and reliable operation. In this work, we examine the ability of thermal automatic detection systems to fulfill their role as an essential part of such a mechanism. The primary advantage of thermal detection is the potential to provide a 24-h uninterrupted detection service exploiting its innate robustness against environmental or weather changes and shifts in illumination conditions. For providing a reliable security mechanism, a second requirement is considered sine qua non: the efficiency of the system in order to provide timely alerts for potential threats and incidents. In this work, we evaluate various efficient object detection models operating solely in the thermal/infrared spectrum to examine their role as potential backbone detectors in surveillance systems. Full article

As grids decarbonize and end-use sectors electrify, the rapid penetration of artificial intelligence (AI) and hyperscale data centers reshapes the electrical load profile and power quality requirements. This leads not only to higher consumption but also coincident demand in constrained urban nodes, steeper ramps and tighter power quality constraints. The article investigates to what extent a compute-additionality covenant can reduce resource inadequacy (LOLE) at an acceptable $/kW-yr under realistic grid constraints, tying interconnection/capacity releases to auditable contributions (ELCC-accredited firm-clean MW in-zone or verified PCC-level services such as FFR/VAR/black-start). Using two worked cases (mature market and EMDE context) the way in which tranche-gated interconnection, ELCC accreditation and PCC-level services can hold LOLE at the planning target while delivering auditable FFR/VAR/ride-through performance at acceptable normalized costs is illustrated. Enforcement relies on standards-based telemetry and cybersecurity (IEC 61850/62351/62443) and PCC compliance (e.g., IEEE/IEC). Supply and network-side options are screened with stage-gates and indicative ELCC/PCC contributions. In a representative mature case, adequacy at 0.1 day·yr⁻¹ is maintained at ≈$200 per compute-kW-yr. A covenant term sheet (tranche sizing, benefit–risk sharing, compliance workflow) is developed along an integration roadmap. Taken together, this perspective outlines a governance mechanism that aligns rapid compute growth with system adequacy and decarbonization. Full article

To address persistent challenges in tennis—such as inefficient ball retrieval, the high cost of serving equipment, and difficulties in scheduling matches—this study proposes the design of a shared tennis service robot aimed at improving user experience and validating design feasibility. Grounded in user experience theory, user requirements were collected through questionnaires and structured interviews. The Analytic Hierarchy Process (AHP) was adopted to construct a hierarchical model of requirements. Weighted calculations were then applied to quantify and rank user needs. Design solutions were then derived based on these rankings. To evaluate the solutions, the Fuzzy Comprehensive Evaluation (FCE) method was utilized for multidimensional assessment. The results show that AHP identified three core requirements: intelligent ball retrieval, intelligent serving, and personalized serving parameter customization. Guided by these priorities, the proposed design integrates a shared rental model with multisensory interactive feedback. The final evaluation yielded an FCE score of 87.83, confirming the effectiveness of the solution. The combined AHP-FCE method provides a systematic framework for quantifying user needs and objectively evaluating design alternatives. It also offers a methodological foundation for the development of sports service robots. The shared tennis robot effectively reduces labor and operational costs while enhancing the overall user experience. Full article

The hospitality industry’s rapid adoption of AI service robots has revealed significant variability in consumer acceptance, highlighting the need for personality-based implementation strategies rather than one-size-fits-all approaches. This study extended the AIDUA (Artificial Intelligence Device Use Acceptance) model by integrating Big Five personality traits and robot design characteristics to understand AI service robot acceptance among South African hospitality consumers. A convergent mixed-methods design combined structural equation modeling of survey data (n = 301) with natural language processing analysis of qualitative responses to examine personality-acceptance pathways and consumer concern themes. Results demonstrated that neuroticism negatively influenced performance expectancy (β = −0.284, p < 0.001), while openness enhanced hedonic motivation and preference for humanoid robots (β = 0.347, p < 0.001). Privacy concerns partially mediated the neuroticism-rejection relationship, while transparency interventions significantly improved acceptance among high-neuroticism consumers (effect size d = 0.98). Four distinct consumer segments emerged: Tech Innovators (23.1%), Pragmatic Adopters (31.7%), Cautious Sceptics (28.4%), and Social Moderates (16.8%), each requiring tailored robot deployment strategies. The extended AIDUA framework explained 68.4% of variance in acceptance intentions, providing hospitality operators with empirically validated guidelines for matching robot types to guest personality profiles, optimizing guest satisfaction while minimizing resistance through culturally sensitive implementation strategies. Full article

With the rapid advancement of artificial intelligence, the deployment of social robots has significantly broadened, extending into diverse fields such as education, medical services, and business. Despite this expansive growth, there remains a notable scarcity of empirical research addressing the underlying psychological mechanisms that influence human–robot interactions. To address this critical research gap, the present study proposes and empirically tests a theoretical model designed to elucidate how users’ multi-dimensional perceived values of social robots influence their attitudinal responses and outcomes. Based on questionnaire data from 569 social robot users, the study reveals that users’ perceived utilitarian value, emotional value, and hedonic value all exert significant positive effects on active involvement, thereby fostering their identification and reinforcing attitudinal loyalty. Among these dimensions, emotional value emerged as the strongest predictor, underscoring the pivotal role of emotional orientation in cultivating lasting human–robot relationships. Furthermore, the findings highlight the critical mediating function of active involvement in linking perceived value to users’ psychological sense of belonging, thereby elucidating the mechanism through which perceived value enhances engagement and promotes sustained long-term interaction. These findings extend the conceptual boundaries of human–machine interaction, offer a theoretical foundation for future explorations of user psychological mechanisms, and inform strategic design approaches centered on emotional interaction and user-oriented experiences, providing practical guidance for optimizing social robot design in applications. Full article

The field of haircutting robots is poised for a significant transformation, driven by advancements in artificial intelligence, mechatronics, and humanoid robotics. This perspective paper examines the emerging market for haircutting robots, propelled by decreasing hardware costs and a growing demand for automated grooming services. We review foundational technologies, including advanced hair modeling, real-time motion planning, and haptic feedback, and analyze their application in both teleoperated and fully autonomous systems. Key technical requirements and challenges in safety certification are discussed in detail. Furthermore, we explore how cutting-edge technologies like direct-drive systems, large language models, virtual reality, and big data collection can empower these robots to offer a human-like, personalized, and efficient experience. We propose a business model centered on supervised autonomy, which enables early commercialization and sets a path toward future scalability. This perspective paper provides a theoretical and technical framework for the future deployment and commercialization of haircutting robots, highlighting their potential to create a new sector in the automation industry. Full article

Intelligent robotic systems frequently operate under stringent energy limitations, especially in complex and dynamic environments. To enhance both adaptability and reliability, this study introduces a semantic planning framework that integrates ontology-driven reasoning with energy awareness. The framework estimates energy consumption based on the platform-specific behavior of sensing, actuation, and computational modules while continuously updating place-level semantic representations using real-time execution data. These representations encode not only spatial and contextual semantics but also energy characteristics acquired from prior operational history. By embedding historical energy usage profiles into hierarchical semantic maps, this framework enables more efficient route planning and context-aware task assignment. A shared semantic layer facilitates coordinated planning for both single-robot and multi-robot systems, with the decisions informed by energy-centric knowledge. This approach remains hardware-independent and can be applied across diverse platforms, such as indoor service robots and ground-based autonomous vehicles. Experimental validation using a differential-drive mobile platform in a structured indoor setting demonstrates improvements in energy efficiency, the robustness of planning, and the quality of the task distribution. This framework effectively connects high-level symbolic reasoning with low-level energy behavior, providing a unified mechanism for energy-informed semantic decision-making. Full article

Since the first successful on-orbit repair mission in 1984 to the Solar Maximum Mission (SMM) satellite, considerable progress has been made in the field of On-orbit Servicing, Assembly, and Manufacturing (OSAM) of spacecraft using either human-guided or autonomous robots. This article aims to provide a review of state-of-the-art efforts in this field and the necessary technologies to achieve the ultimate objective of autonomous spacecraft repairs while in orbit. The article covers the literature relevant to OSAM, including a brief overview of OSAM, inspection technologies, manufacturing and repair technologies, state-of-the-art robotic technologies capable of performing the required tasks, and intelligent path planning of robots. The article also highlights the research’s location, timeframe, and public versus private sector efforts, and outlines future directions in this field. This article aims to foster a community of researchers and public-private partnerships working towards the common objective of autonomous robotic inspection and repair of on-orbit spacecraft. Full article

Speaker recognition has become a critical component of human–robot interaction (HRI), enabling personalized services based on user identity, as the demand for home service robots increases. In contrast to conventional speech recognition tasks, recognition in home service robot environments is affected by varying speaker–robot distances and background noises, which can significantly reduce accuracy. Traditional approaches rely on hand-crafted features, which may lose essential speaker-specific information during extraction like mel-frequency cepstral coefficients (MFCCs). To address this, we propose a novel speaker recognition technique for intelligent robots that combines SincNet-based raw waveform processing with an adaptive neuro-fuzzy inference system (ANFIS). SincNet extracts relevant frequency features by learning low- and high-cutoff frequencies in its convolutional filters, reducing parameter complexity while retaining discriminative power. To improve interpretability and handle non-linearity, ANFIS is used as the classifier, leveraging fuzzy rules generated by fuzzy c-means (FCM) clustering. The model is evaluated on a custom dataset collected in a realistic home environment with background noise, including TV sounds and mechanical noise from robot motion. Our results show that the proposed model outperforms existing CNN, CNN-ANFIS, and SincNet models in terms of accuracy. This approach offers robust performance and enhanced model transparency, making it well-suited for intelligent home robot systems. Full article

Recently, the market for miniaturized Unmanned Agricultural Robots has experienced rapid development worldwide, driven by advances in robotics, artificial intelligence and precision agriculture. These technologies are no longer confined to highly industrialized countries but are increasingly penetrating emerging economies, including Romania, where they hold significant potential for transforming farming practices and entrepreneurial competitiveness. The purpose of the present paper is to present strategies for enhancing the competitive advantage of agricultural entrepreneurs in Romania’s Central Region. This is achieved by leveraging competitive advantage through value creation, specifically by deepening strategies for the rapid integration of new miniaturized robotic products. The research employed a mixed-methods approach, combining qualitative and quantitative techniques to investigate the ability of key stakeholders—agricultural entrepreneurs, precision agriculture product/service providers, institutional representatives, and investors—to dynamically adapt to evolving market conditions. The study’s findings reveal a strong interest and readiness among precision agriculture stakeholders to adopt advanced technologies, supported by robust operational knowledge management practices including external knowledge acquisition, strategic partnerships and data protection. Although agricultural entrepreneurs exhibit considerable adaptive and absorptive capacities—evidenced by their openness to innovation and collaboration—persistent barriers such as high equipment costs and limited financing access continue to impede the broad adoption of miniaturized robotic solutions. The study concludes by emphasizing the need for supportive policies and collaborative financing models and it suggests future research on adoption dynamics, cross-country comparisons and the role of education in accelerating agricultural robotics. Full article

Automation has been widely applied and has greatly affected quality management in the catering industry. Intelligent restaurants refer to those in which smart devices and artificial intelligence (AI) technologies (such as robots and self-service technologies) are embedded in the restaurant environment. However, the existing research on intelligent restaurants has mostly focused on the technological development of equipment. Hence, this interdisciplinary study, integrating insights from hospitality management and human–computer interaction, examines how human-provided and automated-provided services interactively influence customers’ dining experience quality in intelligent restaurants, and how they affect customers’ perceived value and their social media sharing generation. This study develops a measurement scale of dining experience quality in intelligent restaurants that contains human-provided experience and automated-provided experience through in-depth interviews with 15 customers (Study1), and a model was proposed and verified using partial least-squares structural equation modelling (PLS-SEM) analysis on a sample of 493 customers dining in intelligent restaurants (Study 2), which shows that the quality of dining experience has a positive effect on customer perceived value, overall satisfaction in intelligent restaurants, and social media sharing generation. Specifically, automated-provided services offer functional value, while human employees mainly provide perceived emotional value. Perceived functional value has a greater impact on overall satisfaction with intelligent restaurants. The originality of this research is that it integrates services provided by humans and services provided by automated devices and clarifies the different roles of functional and emotional value in shaping customers’ perceived value. These findings provide a new research perspective for intelligent restaurants and insight into the optimization of service quality and automation systems in intelligent restaurants, thereby promoting sustainable business practices in the industry. Full article

During the last decade, artificial intelligence (AI) has enabled key technological innovations within the modern dementia and frailty healthcare and prevention landscape. This has boosted the impact of technology in the clinical setting, enabling earlier diagnosis with improved specificity and sensitivity, leading to accurate and time-efficient support that has driven the development of preventative interventions minimizing the risk and rate of progression. Background/Objectives: The rapid ageing of the European population places a substantial strain on the current healthcare system and imposes several challenges. COMFORTage is the joint effort of medical experts (i.e., neurologists, psychiatrists, neuropsychologists, nurses, and memory clinics), social scientists and humanists, technical experts (i.e., data scientists, AI experts, and robotic experts), digital innovation hubs (DIHs), and living labs (LLs) to establish a pan-European framework for community-based, integrated, and people-centric prevention, monitoring, and progression-managing solutions for dementia and frailty. Its main goal is to introduce an integrated and digitally enabled framework that will facilitate the provision of personalized and integrated care prevention and intervention strategies on dementia and frailty, by piloting novel technologies and producing quantified evidence on the impact to individuals’ wellbeing and quality of life. Methods: A robust and comprehensive design approach adopted through this framework provides the guidelines, tools, and methodologies necessary to empower stakeholders by enhancing their health and digital literacy. The integration of the initial information from 13 pilots across 8 European countries demonstrates the scalability and adaptability of this approach across diverse healthcare systems. Through a systematic analysis, it aims to streamline healthcare processes, reduce health inequalities in modern communities, and foster healthy and active ageing by leveraging evidence-based insights and real-world implementations across multiple regions. Results: Emerging technologies are integrated with societal and clinical innovations, as well as with advanced and evidence-based care models, toward the introduction of a comprehensive global coordination framework that: (a) improves individuals’ adherence to risk mitigation and prevention strategies; (b) delivers targeted and personalized recommendations; (c) supports societal, lifestyle, and behavioral changes; (d) empowers individuals toward their health and digital literacy; and (e) fosters inclusiveness and promotes equality of access to health and care services. Conclusions: The proposed framework is designed to enable earlier diagnosis and improved prognosis coupled with personalized prevention interventions. It capitalizes on the integration of technical, clinical, and social innovations and is deployed in 13 real-world pilots to empirically assess its potential impact, ensuring robust validation across diverse healthcare settings. Full article

This work introduces a comprehensive vision-based framework for autonomous space debris removal using robotic manipulators. A real-time debris detection module is built upon the YOLOv8 architecture, ensuring reliable target localization under varying illumination and occlusion conditions. Following detection, object motion states are estimated through a calibrated binocular vision system coupled with a physics-based collision model. Smooth interception trajectories are generated via a particle swarm optimization strategy integrated with a 5–5–5 polynomial interpolation scheme, enabling continuous and time-optimal end-effector motions. To anticipate future arm movements, a Transformer-based sequence predictor is enhanced by replacing conventional multilayer perceptrons with Kolmogorov–Arnold networks (KANs), improving both parameter efficiency and interpretability. In practice, the Transformer+KAN model compensates the manipulator’s trajectory planner to adapt to more complex scenarios. Each component is then evaluated separately in simulation, demonstrating stable tracking performance, precise trajectory execution, and robust motion prediction for intelligent on-orbit servicing. Full article