Search Results (414)

This study presents a compact affective sensing system that converts physical touch into intuitive multimodal feedback through dynamic facial expressions and optional audio responses. The system was developed to support non-verbal, human-centered interaction in embedded human–machine interfaces, where tactile input can be directly associated with emotional perception. The hardware platform is based on the M5Stack CoreS3, integrating a force-sensitive resistor (FSR), an ESP32-S3 microcontroller, an embedded LCD, and a built-in speaker. Pressure signals are acquired using a simple voltage divider circuit and digitized through the built-in 12-bit analog-to-digital converter (ADC) of the ESP32-S3. To improve signal stability, a simple moving average (SMA) filter is applied for noise reduction. The normalized pressure signal is classified into multiple pressure regions and mapped to emotional states. Smooth facial transitions are generated by continuously interpolating geometric facial parameters, including mouth curvature, eye shape, and eyebrow angle, without relying on pre-rendered images. Experimental evaluation demonstrated stable pressure detection, low-latency response, and intuitive emotional feedback across a wide operating range. User evaluation results further indicated that the combination of visual and auditory feedback enhanced realism, anthropomorphic perception, and interaction quality. The proposed system demonstrates the potential of tactile affective sensing for applications in assistive communication, education, and empathetic human–robot interaction systems. Full article

In Industry 5.0 (I5.0), close-proximity human–robot collaboration demands communication beyond conventional alarms and speech. Nonverbal auditory communication offers a complementary modality, yet its role in I5.0 remains unmapped. This scoping review maps nonverbal auditory communication research in I5.0 Human–Robot Interaction (HRI) and compares it with general HRI literature to identify transfer potential and research gaps. Peer-reviewed English-language articles (2023–April 2026) addressing nonverbal sound in HRI contexts were included. Speech, emotion detection, haptic interfaces and non-HRI domains were excluded. A search with two syntaxes across Web of Science, Scopus, IEEE Xplore, ACM and MDPI, supplemented by citation searching, targeted I5.0-specific (Syntax S1) and general HRI auditory literature (Syntax S2). This created two article record sets, n1 and n2. Articles were organized following Arksey and O’Malley’s framework and PRISMA-ScR into four inductively derived clusters: Sonification, Multimodal Feedback Systems, Safety and Frameworks and Concepts. From 782 initial records, 16 (n1) and 32 (n2) articles were included. In I5.0, multimodal feedback dominates: intentionally designed nonverbal sounds improve situational awareness, reduce cognitive workload and increase perceived safety. Compared to n2, which is shaped by social robotics and emotion-driven sound design, five gaps emerge in I5.0: absent emotion-related sound perception research, missing field studies, missing industry-specific sound design frameworks, underutilized sonification for spatial awareness and safety and no unimodal auditory studies under realistic industrial conditions. A dedicated sound design framework operationalizing I5.0 communicative requirements into designable sound parameters is needed, alongside empirical validation under realistic industrial noise conditions. Full article

This work addresses the design and implementation of an automated system for the handling and transportation of parts, integrating speed sensors, an optimized PID controller, an HMI interface, and an industrial robotic system. The speed sensors, powered by 5 V DC, enable continuous measurement of the conveyor belt’s speed and direction of rotation, providing the feedback signal required for the control loop. The core element of the system is the implementation of a PID controller applied to a direct current motor responsible for driving the conveyor belt. This controller regulates the motor speed by analyzing the error between the reference speed and the measured speed, using proportional, integral, and derivative actions to improve system stability, reduce steady-state error, and minimize oscillations. The application of PID control makes it possible to achieve an appropriate dynamic response, ensuring accuracy and reliability in the transportation process. System monitoring and operation are carried out through a human–machine interface (HMI) developed in LOGO Web Editor, which communicates with the PLC (LOGO V8) to visualize and control the status of the conveyor belt, sensors, and control elements in real time. This interface facilitates interaction between the operator and the system, allowing both virtual and physical operation. In addition, RAPID programming is used to control the IRB 14000 industrial robot, enabling the reading of PLC signals and the execution of coordinated trajectories between both arms. The operating sequence includes picking up a part with the left arm, placing it on the conveyor belt, and, after detection by sensors and PLC control, subsequent manipulation by the right arm to a specific point. Finally, both arms return to their original position, ensuring synchronized and collision-free operation. Lastly, this work integrates scientific knowledge related to the modeling, analysis, and control of dynamic systems, particularly in the implementation of closed-loop PID control optimized using genetic algorithms. This control is applied directly to an embedded system through the use of an Arduino board as the processing and control platform. Likewise, technological knowledge associated with industrial automation, PLC programming, HMI development, and industrial robotics is incorporated. The convergence of these scientific and technological approaches results in a comprehensive and compelling project that demonstrates the practical application of theoretical concepts in a functional automated system representative of real industrial environments. Full article

Efficient task assignment for mobile robots is a crucial challenge in modern intralogistics. This paper presents an integrated cyber-physical framework combining predictive tree search on switching max-plus linear systems with a physical IoT-based dispatch interface. The scheduling problem is modelled as a discrete event system, where standard max-plus algebra captures robot synchronization, and a switching mechanism represents alternative resource assignments. To address real-world operational disturbances, the predictive model is enhanced with a fault-tolerant control (FTC) mechanism that dynamically estimates and adapts to non-stationary transport delays. The resulting decision space, which grows exponentially with the prediction horizon, is explored via a predictive tree search algorithm utilizing a quadratic cost function to penalize excessive and uneven transport times. The physical dispatch layer is realized using KIS.BOX IoT devices acting as operator-controlled stations, communicating with the central controller via a WebSocket/STOMP event stream and a lightweight REST API. Simulation results obtained in a Blender 3D environment demonstrate that the proposed FTC predictive strategy significantly reduces the variance of task completion times under fault conditions compared to a baseline First-In-First-Out approach. Furthermore, the IoT integration successfully simulates and validates the feasibility of human-in-the-loop task injection within a realistic, stochastic scenario. Full article

Autonomous robotic systems are being deployed in commercial, healthcare, logistics, and mixed-use built environments at a rate that significantly outpaces the adaptive capacity of existing building design and management paradigms. Buildings have historically been conceived exclusively for human occupants, and the resulting absence of a structured, scalable framework for evaluating or designing robot-ready facilities constitutes a critical gap in both research and professional practice. This article introduces the Robot-Friendly Buildings Level of Service (RFB-LOS) framework: a five-tier hierarchical classification system that characterises the degree to which a built environment supports autonomous robotic operations across six evaluative dimensions—building intelligence, active infrastructure, architectural planning, accessibility, observability, and safety. The framework spans a continuum from Robot Excluded (RFB-LOS-1), in which a building has no awareness of its robotic occupants, to Physical AI Robot Optimised (RFB-LOS-5), in which a Physical AI middleware layer assumes the highest command authority within a coordinated human–robot–building ecosystem. Drawing structural inspiration from the SAE J3016 Levels of Driving Automation, the EU Smart Readiness Indicator, HIMSS EMRAM, and BREEAM/LEED sustainability certification, the RFB-LOS framework is positioned as a foundational standard for the built environment and systems engineering community. Five real-world case studies spanning retail, hospitality, healthcare, and corporate sectors across four countries validate the framework’s tier assignments against observed operational outcomes. Full article

As robots and humans start to share common spaces and perform collaborative tasks, it has become critical to facilitate information exchange between them for communicating and interpreting each other’s intentions. By overlaying virtual objects on a view of the physical world, mixed reality (MR) technology offers a compelling approach for designing innovative models of human–robot interaction (HRI). For robot manipulators, mobile MR frameworks that allow a user to communicate a goal position for the robot’s end effector have been widely studied. However, HRI applications that may require other relevant information for the manipulator to complete more complex tasks remain unexplored. Thus, we propose an MR-enhanced HRI framework, deployed on a touchscreen tablet, that utilizes a virtual arrow object to communicate force intent (i.e., location, direction, and magnitude) to the manipulator and provide visual force feedback to the user. To evaluate the system performance and user experience, we conducted a user study with 25 participants who used a manipulator robot to complete four insertion subtasks, reporting a task success score of 96%, a usability overall mean score of 4.35 out of 5, and a low task load index of 21.49 out of 100. The results show that the MR-HRI framework is intuitive to operate, allowing users to successfully perform assigned tasks by effectively communicating their intentions through the virtual arrow. Full article

This paper presents a Digital Twin (DT)-based framework for the control, monitoring, and intelligent optimization of an Assembly/Disassembly/Repair Mechatronic Production Line (A/D/R MPL), developed as a laboratory platform aligned with Industry/Education 4.0/5.0 paradigms. The A/D/R MPL is assisted by two complementary cyber–physical robotic systems: an Assembly/Disassembly/Replacement Cyber–Physical Robotic System (A/D/R CPRS), and a Mobile Cyber–Physical Robotic System (MCPRS), enabling both fixed and mobile intelligent operations. The CPRS is equipped with an industrial robotic manipulator (IRM) responsible for A/D/R tasks, while the A/D Mechatronic Line (A/D ML) consists of seven interconnected workstations (WS1–WS7) dedicated to storage, transport, quality control, and final product handling. MCPRS includes a wheeled mobile robot (WMR), carrying a robotic manipulator (RM) and Mobile Visual Servoing System (MVSS). Each workstation is connected to a local slave programmable logic controller (PLC), which communicates via PROFIBUS with a master PLC located at the CPRS level. Additional communication infrastructures include LAN PROFINET and LAN Ethernet for local integration, and WAN Ethernet connectivity enabled through open platform Communication-Unified Architecture (OPC-UA), ensuring interoperability, scalability, and remote accessibility. Also, MODBUS TCP as serial industrial communication is used between the master PLC and the MCPRS. Virtual environment supports task planning through Augmented Reality (AR) and real-time monitoring through Virtual Reality (VR). The system behaviour is modelled with synchronized hybrid Petri Nets (SHPNs) which describe the discrete and hybrid dynamics of A/D/R processes. Artificial intelligence (AI) techniques are integrated into the DT framework for optimal task scheduling and adaptive decision-making. As a laboratory-scale implementation, the proposed system provides a comprehensive platform for experimentation, validation, and education. It supports Education 4.0/5.0 objectives by facilitating hands-on learning, human–machine interaction, and the integration of emerging technologies such as AI, Digital Twins, AR/VR, and cyber–physical systems. At the same time, it embodies Industry 4.0/5.0 principles, including interoperability, decentralization, sustainability, robustness, and human-centric design. Full article

The way robots represent the environment, make decisions, and express themselves can positively influence human–robot interaction if they clearly communicate their intentions and needs. To improve human–robot communication, biologically inspired models that mimic human communication skills, including task and scenario-specific contextual information, can facilitate mutual understanding and successful task execution. This paper presents a Context-Awareness and Biologically Inspired Behaviour system to generate a more natural human–robot interaction. The architecture combines sensory information processed by a Joint Attention System that prioritises stimuli based on internal processes with task-related motivations to generate context- and goal-adapted verbal and non-verbal interaction. We evaluate the system through a video-based user study that compares two robots with similar appearances but different behaviours, one using the proposed approach and the other not using the internal state and joint attention mechanisms, to make verbal and non-verbal responses. The results show that participants rated the robot endowed with the proposed system as significantly more sociable, agentic, and animated than the robot without it. Additionally, the robot not showing the responses developed in this work was perceived as more disturbing than the robot integrating the proposed system. Full article

This study investigates the user experience (UX) of game-based activities within a school-based social robot intervention for children with ASD and examines changes in task-related performance across robot-led and teacher-led structured communicative–linguistic activities. A multimodal methodology combines quantitative measures (accuracy, response time, and physiological signals) with qualitative teacher feedback. The results reveal limited significant differences in accuracy and other performance variables; however, response time decreased significantly across repetitions and was lower in teacher-led sessions. These findings indicate improved task-response efficiency and suggest a possible facilitation pattern in subsequent human-led interactions, although this effect cannot be disentangled from practice or order effects because of the sequential design. Rather than demonstrating broad linguistic gains, the study highlights the value of multimodal UX-oriented evaluation for identifying design limitations, refining robot-mediated educational activities, and supporting teacher involvement in ASD interventions. Full article

The COVID-19 pandemic highlighted the need for effective disinfection strategies in order to minimize human exposure and reduce the risk of contagion in indoor environments. Ultraviolet-C (UV-C) irradiation has proven to be an effective solution for inactivating a wide range of pathogens. However, traditional fixed UV-C systems suffer from limited coverage and lack operational flexibility. To address these limitations, this paper proposes an augmented reality (AR)-based teleoperation framework for an omnidirectional mobile robot equipped with a UV-C disinfection light. Unlike traditional toolchain integrations, our framework synergizes immersive spatial visualization of a reconstructed environment, operator-guided waypoint-based remote navigation, and real-time interaction with the disinfection payload in a single operational workflow. The system is implemented using a ROSMASTER X3 Plus robotic platform, which generates a three-dimensional representation of the environment through visual simultaneous localization and mapping using RTAB-Map. The result is a 3D map that is imported into the Unity game engine and deployed to a Meta Quest 3 head-mounted display, enabling immersive visualization and interaction. Communication between the AR interface and the robotic system is achieved via the ROS-TCP-Connection, allowing real-time data exchange and remote robot control. Through the AR interface, the operator can navigate the robot within the scanned environment and activate the UV-C light. Experimental validation conducted in a classroom demonstrates the feasibility of the proposed approach and shows measurable reductions in surface microbial load. These results indicate that our system-level integration of AR-assisted teleoperation with mobile UV-C robotics represents a feasible proof-of-concept for flexible, operator-guided disinfection of indoor spaces. Full article

Automatic modulation recognition (AMR) is increasingly relevant to communication-sensing front ends in robotic and human–robot collaborative systems, where reliable spectrum awareness and adaptive wireless reception are desired. However, existing methods often degrade sharply at low signal-to-noise ratios (SNRs), and large language models (LLMs) are not natively compatible with continuous I/Q signals due to the inherent modality gap. We propose BioLAMR, a GPT-2 adaptation framework for AMR inspired by the auditory system’s parallel time–frequency processing and cortical hierarchy. The framework combines bio-inspired dual-domain feature extraction with parameter-efficient LLM adaptation. BioLAMR includes three components. First, a lightweight dual-domain fusion (LDDF) module extracts complementary time- and frequency-domain features and fuses them through channel and spatial attention. Second, a convolutional embedding module converts continuous I/Q signals into GPT-2-compatible sequences without discrete tokenization. Third, a hierarchical fine-tuning strategy updates only 8.9% of parameters to preserve pretrained knowledge while adapting to modulation recognition. Experiments on the RadioML2016.10a and RadioML2016.10b benchmarks show that BioLAMR achieves overall accuracies of 64.99% and 67.43%, outperforming the strongest competing method by 2.60 and 2.47 percentage points, respectively. Under low-SNR conditions, it reaches 36.78% and 38.14%, the best results among the compared methods. Ablation studies verify the contribution of each component. These results demonstrate that combining dual-domain signal modeling with parameter-efficient GPT-2 adaptation is an effective route to robust AMR in challenging wireless environments. Full article

This study investigates the effective translation of complex biological principles into viable engineering solutions within the field of biomimetic design. A critical challenge in current research is the “fuzzy front end” bridging initial biological observations and practical engineering applications. This gap primarily stems from the lack of intermediary models capable of abstracting complex biomechanical data into manufacturable mechanical paradigms. To address this, we propose a systematic biomimetic translation framework that redefines traditional crafts as “Empirically Optimized Biological Analogues” (EOBAs), serving as a logical bridge between biological inspiration and engineering realization. This study contributes by integrating the Analytic Hierarchy Process (AHP) with the Fuzzy Comprehensive Evaluation (FCE) method to construct a quantitative assessment system. This system evaluates translation feasibility, engineering innovation potential, semantic interaction characteristics, and prototype manufacturability. Applying this framework to four intangible cultural heritages in Guangdong, combined with comprehensive expert and public evaluations, revealed that the Guangdong Lion Dance exhibits the highest biomimetic translation potential in terms of morphological clarity and dynamic behavioral characteristics. Consequently, we extracted the core principle of “embodied kinematics for communication” and developed a conceptual multi-segment biomimetic robotic prototype designated as “Kine-Lion”. Ultimately, this research provides a structured methodological reference for biomimetic robotic design, demonstrating that culturally abstracted biological behaviors can be systematically decoded into functional robotic structures. These findings indicate broad application prospects in the domains of human–robot interaction and biomimetic engineering. Full article

To address collision risks in teleoperated mobile robotic vehicles, this study proposes a Human–Machine Interaction-based Advanced Smooth Shared Control (ASSC) system aimed at enhancing obstacle avoidance and achieving smooth shared control between human operators and the automation system. The ASSC system integrates a novel approach using predictive vectors to represent the vehicle’s heading position, automatically adjusting the steering position upon obstacle detection to ensure smooth collision avoidance without changing the driver’s perception. Feedback forces applied to the steering wheel are calculated through an artificial potential field algorithm. Twenty participants were invited to operate the vehicle, providing feedback on the ASSC system’s performance relative to conventional obstacle avoidance methods. Performance metrics such as the effects of communication delays, Time to Complete the Task (TTC), ASSC effectiveness, performance of the delay impact on the ASSC system, and the Number of Obstacle Collisions (NOC) are analyzed. The results demonstrate that the ASSC system significantly outperforms traditional obstacle avoidance methods, providing more precise control in teleoperation. Statistical analysis indicates that the ASSC system improves safety, comfort and operational performance by 12.8%. This research highlights the ASSC system as a promising solution for enhancing automation, safety, and human–machine interaction in teleoperated mobile robotic vehicles. Full article

Widespread acceptance of collaborative robots in human-involved scenarios requires accessible and intuitive interfaces for lay workers and non-expert users. Existing interfaces often rely on users to plan and issue low-level commands, necessitating extensive knowledge of robot control. This study proposes a multimodal agentic AI framework integrating natural user interfaces (NUIs) to foster effortless human-like partnerships in human–robot collaboration (HRC), which enhance intuitiveness and operational efficiency. First, it allows users to instruct robots using plain language verbally, coupled with gaze, revealing objects precisely. Second, it offloads users’ workload for robot motion planning by understanding context and reasoning task decomposition. Third, coordinating with AI agents built on large language models (LLMs), the system interprets users’ requests effectively and provides feedback to establish transparent communication. This proof-of-concept study included experiments to demonstrate a practical implementation of the agentic AI framework on a mobile manipulation robot in the collaborative task of human–robot wood assembly. Seven participants were recruited to interact with this AI-integrated agentic robotic system. Task performance and user experience metrics were measured in terms of completion time, intervention rate, NASA TLX survey for workload, and valuable insights of practical applications were summarized through a qualitative analysis. This study highlights the potential of NUIs and agentic AI-embodied robots to overcome existing HRC barriers and contributes to improving HRC intuitiveness and efficiency. Full article

The accelerated integration of intelligent agents in user-centered digital environments has intensified research in the field of Human–Robot Interaction, especially regarding mechanisms for adaptive, intuitive, and cognitively aligned communication. The present study develops and empirically examines a structural model of BCI-inspired adaptive agents designed to support coordinated interaction in HRI contexts. The study analyzes users’ perceptions of standardized hypothetical interaction scenarios involving BCI-inspired adaptive digital agents, where BCI inspiration is conceptual and refers to adaptive architectures interpreting behavioral cues rather than direct neural signal acquisition. The proposed model integrates four main constructs—perceived technological innovation, user involvement, agent adaptivity, and digital synergy—and examines their associations with user satisfaction in digital collaborative environments. Data were collected through an anonymous questionnaire (N = 268) and analyzed using structural equation modeling with the PLS-SEM method. The structural model demonstrates substantial explanatory power, accounting for 66.8% of the variance in user satisfaction (R² = 0.668). The study contributes by empirically supporting a scenario-based structural evaluation framework suitable for early-stage adaptive HRI system design. The results highlight the role of digital synergy in aligning innovation, engagement, and adaptive behavior in BCI-inspired adaptive HRI systems, providing directions for the design of adaptive robotic agents oriented toward coordinated interaction, user-centered integration, and responsible use in collaborative digital ecosystems. Full article