Search Results (886)

The rapid development of large model-driven agent applications, such as digital assistants and robots, requires 6G radio access networks (RAN) to deliver enhanced flexibility, adaptability, and low-latency capabilities. However, the existing RAN user plane (UP) architecture suffers from coarse decoupling granularity and significant cross-layer functional redundancy. These limitations severely hinder the on-demand orchestration and dynamic reconfiguration required by heterogeneous agent services. To address these challenges, this paper proposes a ComBERT-driven service-based RAN UP decoupling method, specifically targeting the functional coupling and redundancy between the PDCP and RLC sublayers. First, we develop a domain-specific language model, ComBERT, by pre-training a BERT model on a 3GPP protocol corpus and fine-tuning it on text-matching tasks to deeply comprehend protocol semantics. Subsequently, ComBERT is utilized to extract semantic features from UP functional components, employing a sliding window mechanism to overcome truncation in lengthy protocol texts and using cosine similarity to measure functional relevance. Finally, a threshold-based fusion algorithm is designed to identify and merge cross-layer redundant functions, thereby forming independent service units with distinct responsibilities. These fused units serve as the basic building blocks for scenario-specific orchestration. Simulation results demonstrate that the proposed method reduces the number of UP components by 12.5%, 18.7%, and 18.2% in eMBB, URLLC, and mMTC scenarios, respectively. Simultaneously, it decreases average processing delays by 7.9%, 10.2%, and 11.0% across these respective scenarios. Ultimately, this approach effectively improves the lightweight deployment, processing efficiency, and reconfiguration capabilities of the service-based UP, providing a crucial foundation for on-demand service orchestration in 6G networks tailored to agent services. Full article

Reviews of artificial intelligence (AI) for mobile robots usually cover one competence—perception, SLAM, path planning, control, or reinforcement learning—and rarely show how these combine into a working system. We take the opposite view and treat autonomy as one pipeline: sensing, perception, localization and mapping, prediction, planning, visual servoing and control, high-level decision-making, and continual learning. We survey how AI has reshaped each stage for industrial and service robots across Industry 4.0, 5.0, and the emerging Industry 6.0. Using a structured, PRISMA-informed protocol with explicit search strings, inclusion criteria, and cross-embodiment transfer rules, we screen the literature, analyze a corpus drawn mainly from the last five years, and position it against prior surveys with a coverage matrix that exposes their single-block focus. Four findings stand out. Perception and localization approach engineering maturity through multimodal fusion and foundation vision models. Planning and control stay effective but computationally demanding. Decision-making, now driven by large language and vision–language–action models, is powerful yet unverifiable and fails under safety constraints. Lifelong learning is almost absent from deployed systems. The decisive weaknesses sit at the interfaces: at the perception–planning, planning–control, and control–decision handoffs the sim-to-real gap, limited on-robot compute, and scarce industrial data compound. We compare AI families by technology readiness, catalog datasets and benchmarks, examine the safety-certification barrier, and consolidate cross-cutting gaps. We close with a staged roadmap toward Industry 6.0 and a next-generation architecture coupling a foundation perception backbone, a world model and digital twin, a continual-learning memory, and a reasoning core wrapped by a safety monitor. The aim is to move from cataloging algorithms to engineering integrated autonomy. Full article

Background/Objectives: Rehabilitation robotics is increasingly used in neurorehabilitation, yet evidence on how robotic services are organized, maintained and scaled in routine care remains fragmented. This mapping review examined service-delivery models, implementation determinants, strategies, outcomes, workflows and resource signals for robotic rehabilitation in service-linked clinical care. Methods: Six search sources were queried from inception to 21 February 2026. Eligible reports were studies addressing rehabilitation robotics within clinical pathways and reporting an implementation-relevant element. Data from 55 studies were systematically charted and mapped to CFIR determinants, ERIC strategies and Proctor implementation outcomes. Design-appropriate appraisal was applied once per included report to contextualize methodological confidence. Results: Staffing and supervision were reported in 50 studies (90.9%), workflow and scheduling in 43 (78.2%), safety governance in 32 (58.2%) and training and competency in 29 (52.7%). Feasibility was mapped in 40 studies (72.7%), acceptability in 31 (56.4%) and adoption in 23 (41.8%), whereas sustainability, penetration, technical support, documentation infrastructure and implementation cost were less consistently reported. Two randomized trials were appraised with RoB 2 and four non-randomized comparative studies with ROBINS-I; the remaining 49 reports received design-appropriate methodological or reporting appraisal. Conclusions: Routine-care rehabilitation robotics is best understood as a service configuration rather than a device alone. Transferable implementation requires explicit reporting of workforce, workflow, safety, documentation, technical support, infrastructure, cost, equity and sustainment. Frequencies indicate reporting presence, not practical importance or causal influence. The proposed workflow, logic model and minimum reporting set are evidence-informed, inductively derived proposals requiring prospective validation. Full article

Reliable navigation in GPS-denied environments remains a critical challenge for autonomous vehicles (AVs), particularly in complex indoor and urban settings. GPS-based localization systems often fail under these conditions, highlighting the need for resilient multimodal solutions. In this article, we present a radar-assisted tracking system that integrates LiDAR and inertial measurements within a sensor-fusion architecture to achieve robust navigation. The principal methodological contribution is a unified tracking and prediction framework that combines Bayesian state estimation with learning-based temporal prediction, enabling accurate tracking while continuously forecasting the slave robot’s short-term future state from mapping observations generated by the master robot, with a typical end-to-end perception-to-action latency of 20–60 ms. The communication and prediction forecasting module operates with an update interval below 35 ms, enabling real-time cooperative robotic operation. Sensor data are fused through a pipeline incorporating Gaussian Mixture Models (GMMs) for post-processing, which helps mitigate the limitations associated with individual sensors during edge processing. Moreover, Kalman filtering is employed to mitigate sensor noise and drift, thereby improving state estimation accuracy through trajectory smoothing. The fused spatiotemporal information is subsequently exploited by a Convolutional Recurrent Neural Network (CRNN) coupled with a Nonlinear Autoregressive model with eXogenous Inputs (NARX) to model the robot’s motion dynamics and provide short-horizon state prediction. Through simulations and real-world indoor experiments conducted in GPS-denied environments, we validate the system’s ability to provide accurate and continuous pose estimation with low localization errors. Experimental results show that the proposed framework achieves root-mean-square errors of 0.12 m, 0.15 m, and 0.28 m along the X, Y, and Z axes, respectively, while maintaining sub-meter maximum position deviations throughout the evaluated trajectories. These results confirm that the proposed framework provides reliable localization and predictive state estimation for cooperative robotic navigation in indoor GPS-denied environments. Future work will investigate outdoor validation and extend the framework to additional data-driven decision-making models for future robotic services. Full article

Autonomous mobile robots require robust perception and high-level reasoning to operate in complex indoor environments. While the Robot Operating System (ROS) provides a modular framework for mapping and navigation, classical pipelines lack semantic understanding and natural-language interaction. This paper presents a semantic-aware autonomous navigation framework implemented on a ROS 1 (Melodic) mobile robot equipped with a two-dimensional light detection and ranging (LiDAR) sensor and an RGB-D camera. The system integrates LiDAR-based simultaneous localization and mapping (SLAM), the ROS navigation stack (move_base), and a lightweight You Only Look Once (YOLO) object detector for real-time on-board perception, and it anchors detections into the metric map to build a semantic map. A large language model (LLM) interprets natural-language instructions and converts them into structured navigation goals. Perception and control run entirely on-board the Jetson Nano, whereas the LLM is invoked episodically as a cloud service, yielding a hybrid embedded/cloud architecture. In indoor trials over a semantic map of two object classes, the system grounded all ten multilingual commands to the correct objects at a reasoning cost of about one second, and safely rejected a command referring to an unmapped object. We present this as an in-depth single-platform case study: owing to the 4 GB memory budget, the on-board detector and the full navigation stack are time-multiplexed rather than run continuously in parallel; nonetheless, a single degraded end-to-end trial confirmed that perception, online semantic anchoring, language grounding, and navigation compose within one continuous session. The framework offers a low-cost, extensible basis for language-guided robots in smart environments. Full article

Service robots often receive natural language instructions in changing workspaces where multiple visible objects may match one description. Relying on detector confidence, random selection, or direct vision–language model (VLM) prediction can lead to a wrong action. This paper presents ActivAsk, a zero-shot framework for resolving referential ambiguity before robotic grasping. ActivAsk constructs open-vocabulary candidates from red-green-blue-depth (RGB-D) input, asks candidate-grounded yes/no questions when needed, updates the candidate state from the user’s answer, and grasps after target resolution. It selects among VLM-proposed candidate partitions using an expected free energy (EFE) criterion motivated by active inference; with neutral response preferences, this reduces to information gain over candidate partitions. Offline experiments showed that interactive clarification improved target accuracy from about 53–54% for noninteractive baselines to about 90–92%. ActivAsk matched the best interactive accuracy (92.13%) while asking 15.47–19.71% fewer questions on asked trials and 21.43–23.88% fewer for ambiguous instructions. In online real robot experiments, ActivAsk achieved 92.98% target selection accuracy and 87.72% full correct object grasp success; unresolved or wrong targets were not physically executed after operator-controlled verification and were counted as task failures. Full article

On-site digital human guide systems, which integrate intelligent interactive technologies with guidance, interpretation, and information services, are emerging as an important form of intelligent on-site service. However, existing knowledge remains largely fragmented across technological configurations, interaction modalities, application contexts, and user experience objectives, lacking an integrated cross-dimensional analytical perspective that explains how these systems create and deliver value. To address this gap, this paper aims to adopt a Product–Service Systems (PSS) perspective to systematically examine the development of on-site digital human guide systems. Specifically, it explores their product–service configurations, service delivery modes and user participation, and artificial intelligence (AI) capability integration pathways, while identifying the value creation opportunities and challenges associated with system development and continuous optimization. The PSS paradigm offers such an integrative lens, which has gained renewed relevance as the Fourth Industrial Revolution (Industry 4.0) accelerates the servitization and digital transformation of traditional services. This paper conducts a scoping review employing content analysis based on a structured cross-database search across Google Scholar, Scopus, and Web of Science, supplemented by snowball sampling, covering 40 studies, including 34 deployed projects. The findings reveal that: (1) on-site digital human guide systems can be categorized into four product–service configurations: fixed terminal (stationary dialogue); mobile terminal (location-aware guidance); Head-Mounted Display/Mixed Reality (immersive experience); and robot (full-process mobile service). The selection of these configurations is shaped by spatial characteristics, target user group, and institutional operational conditions; (2) a user behavior taxonomy of nine active and five passive types was developed, demonstrating that user participation patterns are jointly shaped by control allocation in service delivery and the social behavior design of digital humans, with users showing a preference for controllable and interruptible engagement; (3) AI integration has evolved from rule-and-script-driven approaches to modular AI integration, and subsequently to large language model (LLM)/Retrieval-Augmented Generation (RAG)-driven architectures, with corresponding design implications proposed; and (4) challenges were classified into four major categories and 26 subcategories across three digital transformation stages. The contribution of this paper lies in the development of an integrated PSS analytical framework for on-site digital human guide systems, spanning product, service, and AI integration layers. The framework provides a multi-layer analytical lens for understanding system configurations, user participation, technological evolution, and implementation challenges, while offering structured guidance for configuration and service selection, system design, and continuous optimization across diverse deployment contexts. These findings provide practical implications for researchers and practitioners seeking to design, deploy, and optimize on-site digital human guide systems across diverse service environments. Full article

With the growing global fleet of motor vehicles and rising demand for unmanned services, enhancing the efficiency and intelligence of refueling operations at gas stations has become a critical industry priority. This review focuses on refueling robots as its core research subject, providing a systematic review of its developmental history and system architecture. Building upon this foundation, this review conducts an in-depth analysis and synthesis of three key enabling technologies: (1) the end effector—integrating multi-degree-of-freedom actuators and sensor modules to precisely control fuel tank lid actuation and fuel nozzle insertion/removal; (2) refueling interface identification—enabling vehicle-type classification, refueling interface location extraction, and recognition of refueling interface features; and (3) refueling interface localization—determining the 6 DoF pose of the refueling interface relative to the robot. Through this technical analysis, it is shown that refueling robots have attained an initial level of intelligence; however, significant challenges remain in achieving high precision and robust performance, ensuring safety and reliability, and establishing standardization and broad interoperability. Future research efforts should therefore prioritize improving environmental adaptability—particularly in complex, unstructured settings—advancing autonomous decision-making capabilities, and enhancing product universality, thereby accelerating the commercial deployment of refueling robots. Full article

This study presents a materials-oriented, code-based parametric optimization of the load-bearing steel tubular arch girder in a tied-arch footbridge inspired by the Father Bernatek Footbridge in Krakow. The objective was to reduce structural steel demand by minimizing the arch-girder weight under Eurocode load combinations with ultimate limit state (ULS) and serviceability limit state (SLS) constraints, while accounting for discrete tubular cross-section changes within a realistic finite element model. A semi-automated workflow linked Autodesk Dynamo, Python scripts, and Autodesk Robot Structural Analysis to generate bridge geometry, build the finite element method (FEM) model, apply code-based loads and combinations, and evaluate structural response using a discrete, non-gradient-based search. A preliminary sensitivity screening was performed for the full set of design parameters, while the final optimization was governed mainly by arch rise, hanger number, and ULS-controlled discrete arch cross-section changes. The optimization reduced the arch-girder weight by 10.7% relative to the reference configuration, from 360.3 × 10³ kg to 321.6 × 10³ kg, within the adopted design domain. The optimum solution corresponded to an arch rise of 23.4 m, 31 hangers, and a deck spacing of 6.0 m. Hanger arrangement strongly affected force redistribution in the arch girder, while the final optimum was controlled by code-based utilization thresholds. The results show that an application programming interface (API)-driven parametric workflow can support early-stage optimization of tied-arch footbridges under code-based design constraints. The scientific contribution of the study lies not in automating Eurocode verification alone, but in identifying the structural mechanisms that govern the minimum-weight solution, including the interaction between arch rise, hanger arrangement, force redistribution, ULS utilization, and discrete tubular cross-section changes. Full article

Real-time speaker localization and speech enhancement remain challenging for embedded acoustic systems operating in noisy and reverberant environments. This paper presents a multichannel beamforming framework based on Generalized Cross-Correlation with Phase Transform (GCC-PHAT) for Direction of Arrival (DOA) estimation and Delay-and-Sum (DAS) beamforming using a compact four-element circular MEMS microphone array. The proposed approach incorporates DOA smoothing and verification to improve localization stability before beamforming. Experimental evaluation in a controlled indoor environment demonstrated localization errors below 1°, while the beamforming stage achieved Signal-to-Noise Ratio (SNR) values predominantly between 25 dB and 40 dB, with peaks approaching 45 dB, and Root Mean Square Error (RMSE) values below 0.05 for most processing windows with an average processing time per frame of 3.473 ms and a memory consumption of 67.11 KB. Comparative results show that the proposed system provides competitive localization accuracy with a computationally simple processing pipeline, making it suitable for real-time embedded applications such as intelligent voice interfaces, videoconferencing systems, and service robotics. Full article

Recently, flexible electronics have attracted widespread attention in personalized health monitoring, soft robotics, and smart human-machine interactions due to intrinsic high stretchability. Among them, constructing buckled structures in flexible devices is one of the most effective strategies to achieve flexibility and stretchability. Flexible electronic devices based on buckled structure (FEDB) have gained significant research progress, owing to their outstanding advantages such as simple fabrication processes, excellent structural stability, and broad applicability. Furthermore, its application areas are expanding to emerging scenarios, including the human body, underwater environments, the oceans, and space. However, there are few systematic reviews concerning their progresses, although researchers show increasing interest in the emerging applications of FEDB. This review summarizes recent research progress in FEDB. First, this review explains the buckled instability mechanism, listing the common conductive and substrate materials. The polymeric substrates discussed mainly include PDMS, TPU, SBS, PC, and hydrogel, which provide the flexibility and deformability required for FEDB. In addition, this review summarizes several methods for constructing buckled structures, including prestretch-release, solvent swelling, thermal, mold, and 3D printing as well as techniques for controlling morphology. Second, this review summarizes the applications of FEDB, such as flexible electrodes, strain and pressure sensors, and energy devices. Particularly, it provides a detailed introduction to the expansion of emerging scenarios, involving underwater monitoring, in vitro and in vivo physiological signal detection, human-machine interactions, and portable capsule devices. Finally, this review points out the current challenges of FEDB, including long-term service stability, adaptability to extreme environments, conformal attachment to complex curved surfaces, and large-scale manufacturing. Full article

Large language models (LLMs) exhibit strong general conversational capabilities, yet their deployment in domain-specific service environments such as hospitality remains limited by the absence of emotionally grounded datasets and validated end-to-end system architectures. This paper presents HOSPIT-LLM, an EU-funded euROBIN Technology Exchange Program pilot, as a complete, integrated pilot pipeline for human-centered conversational AI in hotel reception scenarios. We deploy a multimodal hotel-terminal assistant in a real hotel reception, capturing synchronized dual-camera video and audio to collect authentic guest–staff interactions. Speech is transcribed using Whisper, and emotion is extracted from the corresponding video segments via DeepFace, producing 582 real Greek guest–receptionist exchange examples. The resulting data are classified into eight Standard Operating Procedure (SOP) categories. To address data scarcity, we augment the corpus with 1269 synthetic dialogues generated by eight diverse LLMs through the OpenRouter API, yielding a total of 1851 dialogue records with explicit emotion-token annotation. We fine-tune Qwen3.5-35B-A3B using Low-Rank Adaptation (LoRA) through a two-stage process: supervised fine-tuning (SFT) on an 888-example conversation pool and Simple Preference Optimization (SimPO) on a 1899-pair preference pool, each split 80/10/10 into training, validation, and test. The resulting model is integrated into an interactive hotel-terminal system combining YOLO-based person detection, face-recognition-driven guest personalization, Kokoro neural text-to-speech (TTS), and a multi-service orchestration layer connected to the hotel Property Management System (PMS). Evaluation combines standard text metrics, emotion-aware scoring, and a large-model judge. The results indicate targeted improvements in the rule-based contextual emotion-policy match and staff-emotion policy compliance compared to the base model, while general response-quality gains remain more modest. In particular, the rule-based contextual policy-match score improves from 0.614 to 0.901, while forbidden staff-emotion outputs decrease from 0.142 to 0.018. The deployed pilot demonstrates the practical integration of a personalized, emotion-aware LLM assistant in an interactive hotel-terminal setting; end-to-end latency and fully hotel-side edge deployment were not evaluated and are left for future work. HOSPIT-LLM provides a reproducible framework for multimodal dataset creation, preference-based fine-tuning, and deployment of human-centered AI systems. A mobile robotic embodiment is planned as future work. Full article

This study examined how educational robotics and machine learning activities contributed to the understanding of artificial intelligence (AI) among elementary school students, pre-service teachers, and practicing teachers. The intervention involved 1207 participants, including 1009 fifth-grade students, 93 practicing elementary school teachers, and 105 pre-service teachers. Participants engaged in a series of structured activities focused on educational robotics, visual block-based programming, and machine learning concepts. A pre-experimental design was employed to assess changes in understanding and to explore participants’ perceptions of the learning experience. Statistical analyses included Student’s t-tests, the Kruskal–Wallis H test, and Bonferroni-corrected post hoc comparisons. Results indicated pre–post gains in elementary school students’ understanding of machine learning, AI modeling processes, and fundamental computational concepts following participation in the intervention. Across the three groups, participants reported positive perceptions of the activities and recognized their value for learning about AI. Elementary school students reported higher motivation and stronger engagement in coding-related tasks, likely reflecting their previous experience with Scratch in school settings. Practicing teachers expressed a more interdisciplinary perspective on the educational applications of AI, whereas pre-service teachers demonstrated stronger conceptual understanding of the underlying principles. Overall, the findings suggest that educational robotics and machine learning activities are associated with positive learning experiences and observed gains in AI-related understanding. However, given the pre-experimental design without a control group or random assignment, the results should be interpreted as evidence of observed changes and participant perceptions rather than demonstrated effectiveness. Full article

Effective task allocation is fundamental to the success of heterogeneous multi-robot cooperative missions in smart manufacturing workshops, yet real-world operational uncertainties pose severe challenges to solution feasibility and mission robustness. Addressing these challenges, this paper focuses on the inspection and maintenance task allocation problem for heterogeneous mobile robot teams operating under fluctuating equipment maintenance time windows, variable task execution durations, and uncertain robot travel speeds caused by workshop congestion and payload variations. First, the aforementioned uncertain parameters are characterized using triangular fuzzy numbers, upon which a fuzzy chance-constrained programming model is constructed with the objective of minimizing total operational cost while ensuring constraint satisfaction under uncertainty. The proposed model simultaneously handles two types of critical constraints: the service time window constraint, which requires each task to be completed before its latest allowable service deadline, and the time sequence constraint, which enforces that each equipment inspection task must be completed prior to the corresponding maintenance task. Then, to tackle the inherent NP-hardness of this problem, a multi-strategy hybrid Harris Hawks Optimization algorithm incorporating differential evolution, termed MSHHODE, is proposed. In detail, three targeted enhancement mechanisms are introduced: a hunting enthusiasm factor that governs the dynamic balance between global exploration and local exploitation throughout the search process; an elite-assisted guidance strategy that stabilizes convergence by leveraging high-quality solutions to direct population evolution; and an adaptive differential evolution mechanism that reinforces global search diversity and mitigates premature convergence to local optima. Finally, simulation experiments conducted across multiple workshop-scale scenarios demonstrate that MSHHODE consistently outperforms benchmark algorithms across different key performance metrics under varied uncertain conditions, which validates the effectiveness and robustness of the proposed approach in solving complex, constrained allocation problems, offering a practical and reliable framework for real-world heterogeneous multi-robot task planning in smart manufacturing environments. Full article

With contemporary advancements in additive manufacturing (AM), it has become possible to obtain hull structures for spacecraft made of relatively cheap materials. The possibility of substituting super-austenitic stainless steel Avesta SMO 254 X1NiCrMoCuN20-18-7 (EN 10088) for that already used in the Starship SpaceX 304 L-Modified is focused on achieving better thermal stability and durability in extreme conditions. The Directed Energy Deposition Arc (DED-Arc) method for AM has enabled the production of high-strength-to-weight ratios. The aim is to engage low-cost material with treatment optimization to provide greater corrosion resistance and high yield and tensile strength. For the DED-Arc, a filler wire was selected for the welding source, Fronius TPS 400i. A simulation via the RoboDK Robot Development Kit for the FANUC ARC Mate 100ID10L is provided. Additional shot pining/vibration treatment is proposed for the finished structure, which can be a substitute for the cold-worked initial metal. A comparison is made for stainless steel that has already been tested for space travel. Regimes for the manufacturing process are proposed, with representative samples of Avesta SMO 254 obtained and tested using microhardness measurements, microcracking detection, porosity measurements, interface zone assessment, and microstructural analysis. The DED-Arc process can be applied to large-space shell manufacturing. A comparison is made with a focus on the mechanical and corrosion advantages. For Avesta SMO 254, microhardness measurements ranged from 235 to 246 HV1 and increased after treatment. The controlled parameters provided a maximum heat input of 0.7 KJ/mm, no defects, and a fine microstructure. The successful use of stainless steel with AM increases the potential for multiple space missions. The advanced method shows high quality, allows cost savings and provides extended service life. Full article