Search Results (417)

In Industry 5.0, the transition from fixed traditional automation to flexible human–robot collaboration (HRC) needs interfaces that are both intuitive and efficient. This paper introduces a novel, multimodal control system for autonomous object handling, specifically designed to enhance natural user interaction in dynamic work environments. The system integrates a 6-Degrees of Freedom (DoF) collaborative robot (UR5e) with a hand-eye RGB-D vision system to achieve robust autonomy. The core technical contribution lies in a vision pipeline utilizing deep learning for object detection and point cloud processing for accurate 6D pose estimation, enabling advanced tasks such as human-aware object handover directly onto the operator’s hand. Crucially, an Automatic Speech Recognition (ASR) is incorporated, providing a Natural Language Understanding (NLU) layer that allows operators to issue real-time commands for task modification, error correction and object selection. Experimental results demonstrate that this multimodal approach offers a streamlined workflow aiming to improve operational flexibility compared to traditional HMIs, while enhancing the perceived naturalness of the collaborative task. The system establishes a framework for highly responsive and intuitive human–robot workspaces, advancing the state of the art in natural interaction for collaborative object manipulation. Full article

Background: Previous research suggests that resistance training in hypoxia can cause physiological and muscle adaptations. However, this method may not be efficient for individuals who are training to optimize maximal strength and power. Objective: This study aimed to investigate the effects of 8 weeks of high-intensity resistance circuit in normobaric hypoxic conditions on maximal and explosive measures of muscle strength in upper and lower limbs. Methods: A total of 28 subjects were randomly assigned to either hypoxia (fraction of inspired oxygen [F_IO₂] = 15%; HRC_hyp: n = 15; age: 24.6 ± 6.8 years; height: 177.4 ± 5.9 cm; weight: 74.9 ± 11.5 kg) or normoxia [F_IO₂] = 20.9%; HRC_norm: n = 13; age: 23.2 ± 5.2 years; height: 173.4 ± 6.2 cm; weight: 69.4 ± 7.4 kg) groups. Training sessions consisted of two blocks of three exercises and the training intensity was fixed performed at six repetition maximum. Participants exercised twice weekly for 8 weeks, and upper and lower body power tests were performed before and after the training program. The statistical analysis applied was a two-way analysis of variance with repeated measures and Bonferroni post hoc. Results: No significant differences were observed between groups. However, the hypoxia group showed higher intra-group differences in absolute (N) (F = 7.97; Δ7.3%; p < 0.05; ES = 0.49) and relative (N/Kg) (F = 8.34; Δ7.2%; p < 0.05; ES = 0.49) maximum push-up force after the training period. Conclusions: Hypoxic circuit training may improve a specific upper body performance outcome, but no clear advantage over normoxia was observed. Full article

In this paper, we propose a distributed admittance control framework for joint manipulation of objects by multiple robotic arms that addresses the challenges of human–robot interaction. The system is developed to control the joint transportation of an object by N Franka Emika Panda robots (validated with up to four in simulations) using external human force estimation in a distributed manner without relying on centralized computation or force sensors. We integrate a hybrid observer by combining a distributed force estimator with a nonlinear disturbance observer (NDOB) to achieve accurate human force estimation and minimize estimation errors in simulations. Adaptive radial basis function neural networks (RBFNNs) are employed to dynamically adjust the damping and inertia parameters, enhancing the system’s adaptability and stability. Event-based communication minimizes network bandwidth usage, while consensus protocols ensure synchronization of state estimates across robots. Unlike conventional methods, the proposed observer operates in a fully sensorless manner: no human-force measurements are required. The estimation relies solely on locally available robot states, maintaining high accuracy while reducing system complexity. The framework demonstrates scalability to multiple robots, enhancing robustness in distributed settings. Simulation results show superior performance in terms of path tracking, force estimation accuracy, and communication efficiency compared to centralized approaches. Specifically, the event-triggered strategy reduces communication messages by approximately 70% compared to always-connected mode while maintaining comparable RMSE in position $(9.97\times {10}^{-5}$ vs. $7.39\times {10}^{-5}$) and velocity $(2.52\times {10}^{-5}$ vs. $3.76\times {10}^{-5})$, outperforming periodic communication. Full article

With the transition from Industry 4.0 to Industry 5.0, human–robot collaborative assembly (HRCA) has progressed from physical copresence to cognitive integration and knowledge sharing. Digital twins (DTs) serve as enabling technologies that connect physical and virtual spaces. Support is provided for dynamic, safe, and human-centered collaboration. This study presents a systematic review of the research progress and practical applications of DT-enabled HRCA. First, conceptual boundaries between HRCA and general human–robot collaboration (HRC) in manufacturing are defined. Core elements of DT-driven state perception, task planning, and constraint modeling are described. Second, four task-allocation paradigms are classified and summarized, including optimization-based, constraint satisfaction-based, data-driven intelligent, and large language model (LLM)-assisted approaches. Applicable scenarios are identified. Third, the effects of collaboration modes and interaction modalities on planning logic are analyzed. Collaboration modes are categorized as parallel, sequential, and tightly coupled. Interaction modalities are grouped into AR-based explicit interaction, implicit intention perception, and multimodal fusion. Fourth, cross-domain application characteristics and engineering bottlenecks are summarized. Target domains include precision assembly, disassembly and remanufacturing, and construction on-site operations. Finally, four core challenges are distilled, including dynamic uncertainty, multi-objective conflicts, human factor adaptation, and system integration. Four future directions are outlined: LLM-enabled adaptive planning, safety–efficiency co-optimization, personalized collaboration, and standardized integration. The proposed technology–application–challenge–outlook framework is intended to provide a theoretical reference and practical guidance for transitioning HRCA from laboratory prototypes to large-scale industrial deployment. Full article

In human–robot collaboration (HRC) scenarios with direct physical contact, accurately estimating human intentions and adjusting robot behaviors based on multimodal information is the core factors that restrict the efficiency and precision of current HRC tasks. To enhance the performance of human–robot collaboration under physical contact conditions, we propose a joint network model named ACT_force_cooperative (AFC). This model leverages force sensing information as a representation of human intent to achieve human intent prediction during physical interaction, while simultaneously capturing visual information and robot state data, thereby enabling more efficient execution of human–robot collaborative tasks with multimodal information processing. Existing HRC methods often ignore humans’ collaborative experience in the environment and fail to recognize the uniqueness of interactive force in expressing human intentions. Focusing on the special role of interactive force among various types of data in physical interaction environments, the proposed model predicts humans’ future behavioral intentions and adopts a Transformer model to realize the fusion and representation of multimodal information, thus accomplishing more accurate and effective HRC tasks. Experimental results demonstrate that, through the processing of force sensing information and fusion of multimodal data, the proposed model reduces the motion error by 44.9% and increases the effective collaborative time ratio by 20.2% compared with the baseline Action Chunk Transformer (ACT) model. This not only improves the motion accuracy of the robot in collaborative tasks but also enhances the collaborative experience of human operators. Full article

Cold forging backward extrusion is mainly employed in the manufacturing of axisymmetric cup-like components used extensively in automotive and aerospace assemblies due to the process-induced strength that has a pivotal role in such applications. Although cold forging backward extrusion yields mechanically robust components, it demands high forces, subjecting tooling to immense stress, thereby restricting process capacity. The process encounters hindrances in gaining widespread industrial acceptance due to frequent failures of die elements, necessitating proper die design and control of major influencing factors for process viability and cost-effectiveness. The punches in backward extrusion are often susceptible to failures when processing steel billets. The punch service life is significantly affected by geometrical attributes, the type of steel undergoing deformation, and tool manufacturing aspects. Hence, the present study evaluates punch performance in cold forging backward extrusion using optimized geometrical attributes, manufactured through a design of an experimental approach comprising an L9 orthogonal array. The manufacturing factors considered are punch material, hardness, and advanced surface coating. Punches were designed for two industrial components using powder metallurgy (PM) steels—S600, S290, and S590, heat treated to 60–66 HRC, and coated via physical vapor deposition with TiN, AlTiN, and TiAlCN. Punch performance was analyzed against existing industry practices, and the strategy demonstrated improved productivity. Punch performance was determined based on the number of forgings produced before wear- and fatigue-induced failures. Significant improvements in punch performance were witnessed in both high-speed steel (HSS) and PM punches with optimized geometries. Fractographic investigations were carried out on fractured punches and analyzed, focusing on the coating’s effect on the thermal aspects of the punches. The proposed study will assist the cold-forging industry in determining appropriate variables to minimize forming responses, thereby enhancing tool life. The research also benefits industries by enhancing process robustness and improving process efficiency with respect to cost and time. Full article

Axial cracking in threaded joints of rotary steerable tools is a critical but under-investigated failure mode that can severely disrupt shale gas drilling operations. Understanding its root cause is essential for prevention. This study aims to determine the cause of an axial cracking failure in an S35150 austenitic stainless steel threaded joint from a field operation. A comprehensive analysis was conducted, integrating physicochemical characterization of the failed joint. The stress corrosion behavior of the threaded joint in a simulated corrosive environment was evaluated via four-point bend (FPB) and double cantilever beam (DCB) stress corrosion tests. The results showed that the material exhibited high susceptibility factors: a hardness of 38.5 HRC, a yield-to-tensile ratio near 1, and a P content exceeding the standard. Fracture surface analysis revealed an intergranular morphology with substantial chlorine (0.78%) and sulfur (0.93%) contents, indicative of stress corrosion cracking (SCC). The laboratory tests results demonstrated that the threaded joint had poor crack resistance: the fracture toughness value of the specimen measured by the DCB test was 24.14 MPa·m^0.5, and all specimens fractured during the FPB. Full article

This paper presents a comprehensive framework for evaluating the robustness and adaptability of human–robot collaboration (HRC) controllers under a spectrum of dynamic and unpredictable human intentions. Building upon variable admittance controller (VAC) frameworks augmented with Radial Basis Function Neural Network (RBFNN) online adaptation, we introduce two key innovations: (1) an intent-aware human force generator capable of simulating aggressive, hesitant, oscillatory, conflicting, and nominal behaviors, through the modulation of force gains and the introduction of stochastic noise, and (2) the extension of VAC to incorporate variable stiffness as an adaptive control parameter alongside damping and inertia. The adaptive parameters are jointly tuned online using a self-supervised learning (SSL) mechanism driven by motion error metrics and interaction dynamics. The framework is simulated in a dual-arm collaborative manipulation scenario involving two 7-DoF Franka Emika Panda robots transporting a shared object in a high-fidelity simulation environment. Simulation results demonstrate the system’s capability to maintain stable behavior and minimize tracking error despite abrupt changes in human intent. This work provides a novel and systematic tool for stress-testing adaptive controllers in HRC, with implications for the design of resilient, safe, and reliable robotic systems in real-world collaborative environments. Full article

Human–robot collaboration (HRC) offers a significant potential to improve productivity, safety, and performance in construction, yet its adoption remains constrained by interrelated barriers. The existing studies largely identify these barriers in isolation, with limited insight into their systemic interactions. This study addresses this gap by synthesising prior research using PRISMA and applying interpretive structural modelling (ISM) to examine the hierarchical and causal relationships among barriers to HRC in construction. Eight barrier categories are identified: financial, safety, communication, robot technology-related, organisational, legal/regulatory, education/training, and social and human factors. The ISM–MICMAC results reveal regulatory and communication barriers as key upstream drivers shaping downstream safety, training, organisational, and technological outcomes. By moving beyond descriptive listings, the study provides a systems-level framework that supports the strategic prioritisation of interventions and informed decision-making. The findings advance the theoretical understanding of HRC as a socio-technical system and offer an evidence-informed foundation for context-sensitive implementation strategies in construction. Full article

This paper deals with the control of two five-phase permanent magnet synchronous motors (PMSMs), which are connected in series and operating at different speeds and torques. The topology under study is intended for use in an electrical naval propulsion system. The backstepping control strategy, which uses the Lyapunov stability concept, is employed to control the speed of the two machines considering the series connection of the PMSM stator windings. A comparative study, with respect to classical Vector Control (VC) using PI regulators, is provided to demonstrate the robustness of the proposed control strategies in both healthy and faulty conditions. Typically, dual PMSMs in series cannot operate in the degraded mode in the event of faults. This study optimizes their operation by adapting to such modes, including faults caused by symmetrical parameter changes or by an asymmetrical High Resistance Connection (HRC) in the stator windings, thereby ensuring continuity of service. The HRC is investigated and verified in one stator phase, in two adjacent stator phases and in two non-adjacent stator phases, as well as in a symmetrical HRC fault across all phases. Matlab-based simulation results validate the control design to achieve the desired performance and prove the effectiveness and the asymptotic stability of backstepping control for two series-connected 5-Փ PMSMs, thereby providing redundancy for the naval electric propulsion system. Full article

This paper presents a breakthrough in activating the skin effect at conventional broaching speeds (1–8 m/min) by using laser defocus gradient modification to induce surface embrittlement in martensitic stainless steel Z10C13. Through controlled defocusing, a 50 μm gradient remelting layer was created, which features ultrafine grains (0.8 μm) and a high-density geometrically necessary dislocation (GND) zone (ρGND = 2.27 μm⁻³). The quasi-cleavage fracture was triggered via dislocation pinning by non-oriented low-angle grain boundaries (28.4% LAGBs). Multiscale characterization confirms that this microstructural transformation enhances surface hardness by 12.95% (reaching 31.4 HRC), reduces cutting force by 34.07%, and improves surface roughness by 63.74% (Sz = 28.80 μm). Simultaneously, a parallel crack-deflection mechanism restricts subsurface damage propagation, resulting in a crack-free subsurface zone. These results demonstrate the effectiveness of the embrittlement–toughening dichotomy for precision machining of difficult-to-cut materials under low-speed constraints. Full article

Optimizing heat treatment processes requires an understanding of the complex relationships between compositions, processing parameters, microstructures, and properties. Traditional experimental approaches are costly and time-consuming, whereas machine learning methods suffer from critical data scarcity. In this study, gradient boosting models were developed to predict microstructural phase fractions and mechanical properties using synthetic training data generated from an established metallurgical theory. A 400-sample dataset spanning eight AISI steel grades was created based on Koistinen–Marburger martensite kinetics, the Grossmann hardenability theory, and empirical property correlations from ASM handbooks. Following systematic hyperparameter optimization via 5-fold cross-validation, gradient boosting achieved R² = 0.955 for hardness (RMSE = 2.38 HRC), R² = 0.949 for tensile strength (RMSE = 87.6 MPa), and R² = 0.936 for yield strength, outperforming the Random Forest, Support Vector Regression, and Neural Networks by 7–13%. Feature importance analysis identified the tempering temperature (38.4%), carbon equivalent (15.4%), and carbon content (13.0%) as the dominant factors. Model predictions demonstrated physical consistency with the literature data (mean error of 1.8%) and satisfied the fundamental metallurgical relationships. This methodology provides a scalable and cost-effective approach for heat treatment optimization by reducing experimental requirements based on learning curve analysis while maintaining prediction accuracy within the measurement uncertainty. Full article

This comprehensive review synthesizes current advances and persistent challenges in integrating worker safety and food safety through human-robot collaboration (HRC) in poultry processing. Rapid industry expansion and rising consumer demand for ready-to-eat poultry products have heightened occupational risks and foodborne contamination concerns, necessitating holistic safety strategies. The review examines ergonomic, microbiological, and regulatory risks specific to poultry lines, and maps how state-of-the-art collaborative robots (“cobots”)—including power and force-limiting arms, adaptive soft grippers, machine vision, and biosensor integration—can support safer, more hygienic, and more productive operations. The authors analyze technical scientific literature (2018–2025) and real-world case studies, highlighting how automation (e.g., vision-guided deboning and intelligent sanitation) can reduce repetitive strain injuries, lower contamination rates, and improve production consistency. The review also addresses the psychological and sociocultural dimensions that affect workforce acceptance, as well as economic and regulatory barriers to adoption, particularly in small- and mid-sized plants. Key research gaps include gripper adaptability, validation of food safety outcomes in mixed human-cobot workflows, and the need for deeper workforce retraining and feedback mechanisms. The authors propose a multidisciplinary roadmap: harmonizing ergonomic, safety, and hygiene standards; developing adaptive food-grade robotic end-effectors; fostering explainable AI for process transparency; and advancing workforce education programs. Ultimately, successful HRC deployment in poultry processing will depend on continuous collaboration among industry, researchers, and regulatory authorities to ensure both safety and competitiveness in a rapidly evolving global food system. Full article

Interactions between alkaline solutions and the surface of pyrolytically coated graphite tubes (PCGTs) with/without a platform for determination of vanadium using high-resolution continuum source graphite furnace atomic absorption spectrometry (HR CS GFAAS) are discussed. Changes on the surface of tubes, lifetime of tubes, and formation of memory effect in the determination of vanadium in alkaline solutions (NaOH, Na₂CO₃, and real alkaline slag leachates) were investigated. Based on the results obtained, it is possible to state that HR CS GFAAS determination of vanadium content in alkaline solutions reveals that PCGTs with a platform are more susceptible than those without a platform to the formation of deposits and degradation of the platform surface, especially after the application of hydroxide environments. More marked and faster formation of deposits leads to shortening of the analytical lifetime of PCGTs with a platform (approx. 70 atomization/analytical cycles (ACs)) compared to PCGTs without a platform (approx. 290 ACs). The mechanical life of both types of tubes is comparable (approx. 500 ACs). Deposits formed on the internal surface of PCGTs can be removed in the presence of a carbonate environment and higher temperatures. Damage to the PCGT surface leads to the formation of scaled shapes and cavities, which can result in decreased absorbance due to losses of vanadium in the cavities (negative measurement error), or in increased absorbance by washing out of vanadium from the cavities (positive measurement error, and formation of memory effect). It was found that more frequent cleaning of PCGTs by performing ACs in an environment of 4 mol L⁻¹ HNO₃ can eliminate these unfavourable phenomena. Our results have shown that in the case of samples analysed with different sample environments (acidic vs. alkaline), the surface material of the tube/platform wears out more quickly, and therefore it is necessary to include a cleaning stage after changing the nature of the environment. Full article

Amid the dual-driven trends of Industry 5.0 and smart manufacturing integration, as well as the global imperative for manufacturing sustainability to address resource constraints, carbon neutrality goals, and circular economy demands, human–robot collaborative (HRC) manufacturing has emerged as a core direction for reshaping manufacturing production modes while aligning with sustainable development principles. This paper comprehensively reviews HRC manufacturing systems, summarizing their technical framework, practical applications, and development trends with a focus on the synergistic realization of operational efficiency and sustainability. Addressing the rigidity of traditional automated lines, inefficiency of manual production, and the unsustainable drawbacks of high energy consumption and resource waste in conventional manufacturing, HRC integrates humans’ flexible decision-making and environmental adaptability with robots’ high-precision and continuous operation, not only improving production efficiency, quality, and safety but also optimizing resource allocation, reducing energy consumption, and minimizing production waste to bolster manufacturing sustainability. Its core technologies include task allocation, multimodal perception, augmented interaction (AR/VR/MR), digital twin-driven integration, adaptive motion control, and real-time decision-making, all of which can be tailored to support sustainable production scenarios such as energy-efficient process scheduling and circular material utilization. These technologies have been applied in automotive, aeronautical, astronautical, and shipping industries, boosting high-end equipment manufacturing innovation while advancing the sector’s sustainability performance. Finally, challenges and future directions of HRC are discussed, emphasizing its pivotal role in driving manufacturing toward a balanced development of efficiency, intelligence, flexibility, and sustainability. Full article