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Human-robot collaboration (HRC), as an emerging production mode, has garnered significant attention in the context of the growing smart manufacturing. This study aims to investigate the application of human-robot collaboration in parallel assembly lines and explore its potential for improving productivity, resource utilisation and flexibility. To the best of our knowledge, this is one of the first attempts to consider the combination of parallel assembly line balancing problem (PALBP) and human-robot collaboration. In this study, the parallel assembly line balancing problem with human-robot collaboration (PALBP-HRC) is introduced and characterised. The collaboration mode allows human workers and robots to execute tasks both in parallel and in collaboration. A mixed-integer programming model (MIP) that minimises the cycle time is formulated and a lower bound is proposed. Due to the NP-hardness, we develop an adapted tabu search (TS) algorithm specifically tailored for problem characterisation. Experimental results indicate that the proposed TS algorithm achieves competing performance compared to the MIP and other algorithms, including genetic algorithm (GA) and simulated annealing (SA) algorithm. The comparison between PALBP and PALBP-HRC demonstrates that the integration of human-robot collaboration to parallel assembly line can achieve significant improvements in efficiency and flexibility. [ABSTRACT FROM AUTHOR]

Nowadays industrial robots have been increasingly widely deployed across various sectors and assembly tasks are seen as one of the dominant application fields. The assembly tasks, as the critical process in manufacturing, are still challenging for the robot because of the complex contact state between the robot and the environment (i.e. assembly components). In the assembly task, the robot should switch its controller from a non-contact mode to a contact-rich mode when the contact condition changes, where compliant behaviour is necessary for robustness to uncertain contact and safety of physical interaction. This paper proposes a deep reinforcement learning (DRL) method to achieve such compliance using variable stiffness compliant control. Concretely, a Cartesian compliant controller is built on a virtual dynamics model with a variable non-diagonal stiffness matrix to derive desired motion that reacts with the external force/torque. Upon that, a deep deterministic policy gradient (DDPG)-based agent is deployed to fine-tune such a non-diagonal stiffness matrix. After error-and-trial learning, robots can handle changes in contact state in assembly tasks without any pause and switching its controller mode, thus increasing task efficiency and compliance. The simulation and experiments show that our method allows robots to complete assembly tasks safely and efficiently under noisy observation. [ABSTRACT FROM AUTHOR]

Due to the growing demand for customised products, companies have faced increasing product and process complexity levels. To address this issue, manufacturing processes should become more flexible. One of the most promising technologies to achieve this goal is collaborative robotics (or 'cobots'). In collaborative assembly processes, human and robot combine their skills. However, the co-existence of humans and cobots in the same workspace may influence the operators' perception of assembly complexity. The analysis and control of assembly complexity are crucial to achieving better performances in terms of process quality and operators' well-being. Many qualitative methods have been proposed in the literature to provide a holistic assessment of assembly complexity. This paper proposes a novel method to define a quantitative scale of perceived assembly complexity, based on Thurstone Law of Comparative Judgements. This method was applied to an experimental case-study concerning the assembly of three different products in two modalities (i.e. manual and collaborative). Regression analysis showed that the perceived complexity may be related to the occurrence of process failures and to the perceived workload. The method also proved capable of identifying assembly processes where cobot assistance was helpful, providing process designers with a supporting tool to minimise perceived complexity. [ABSTRACT FROM AUTHOR]

The precise measurement of temperature is crucial in various fields such as biology, medicine, industrial automation, energy management, and daily life applications. While in most scenarios, sensors with a fixed thermal conductivity inevitably mismatch the analogous parameter of the medium being measured, thus causing the distortion and inaccurate detection of original temperature fields. Despite recent efforts on addressing the parameter-mismatch issue, all current solutions are constrained to a fixed working medium, whereas a more universal sensor should function in a variety of scenes. Here, we report a dynamic and reconfigurable thermal sensor capable of highly accurate measurements in diverse working environments. Remarkably, thanks to the highly tunable thermal conductivity of the expanded-plane structure, this sensor works effectively on background mediums with a wide range of conductivity. Such a development greatly enhances the robustness and adaptability of thermal sensors, setting a solid foundation for applications in multi-physical sensing scenarios. [ABSTRACT FROM AUTHOR]

Human–robot collaboration has been incorporated into production and assembly processes to promote system flexibility, changeability and adaptability. However, it poses new challenges to resource allocation and production scheduling due to its intrinsic uncertainty and also the increasing complexity of resources. This paper investigates a stochastic flow shop scheduling problem in the context of human–robot collaboration. The goal is to achieve efficient utilisation of flexible resources including human workers and cobots and take full advantage of human–robot collaboration in production scheduling. A stochastic Cobb–Douglas production function is utilised to evaluate the production efficiency of human–robot collaboration considering instabilities of human performance. A joint chance-constrained programming model is formulated to ensure that the required system performance can be achieved. A CVaR approximation-based approach is proposed to solve the formulated model with mixed-integer variables and a nonconvex constraint. The effectiveness of the formulated model and the efficiency of the proposed solution approach are evaluated via numerical experiments. Computational results show the superiority of our solution approach over three other approaches including Bonferroni approximation, scenario approach and individual chance-constrained programming. [ABSTRACT FROM AUTHOR]

High quality, safe, and reliable batteries are essential for widespread adoption of electric vehicles. Current Li-ion battery pack manufacturing processes rely on manual inspections to ensure electric vehicle battery quality. Such manual quality control is prone to errors, increasing the chances of defective batteries. This is likely to increase safety and reliability concerns in the public imagination, slowing down the adoption of electric vehicles. Furthermore, manual inspection is time-consuming and likely to become a bottleneck in scaling up electric vehicle battery production. A potential solution to address this need for fast and accurate inspection of batteries is the use of machine vision and robotics. In this study, we use digital twin design and simulation to develop a battery module inspection system that uses cobots and machine vision to inspect electric vehicle batteries for defects. Our proposed system can automate visual quality checks that are currently being done by human operators. The proposed cobotic system has been simulated and validated for a variety of battery defects to achieve fast and reliable detection. Since a digital twin of the cobotic inspection workcell has been used, the battery inspection system, as designed and validated, is ready for immediate implementation. [ABSTRACT FROM AUTHOR]

We document significant negative effects of exposure to increased automation at work on household wealth accumulation. Beyond the income and savings channels, we uncover a novel mechanism contributing to the negative wealth effects of automation that arises through the endogenous optimal portfolio decisions of households. We show that households rebalance their financial wealth away from the stock market in response to increased human capital risk induced by pervasive automation, thereby attaining lower wealth levels and relative positions in the wealth distribution. Our evidence suggests that the portfolio channel amplifies the inequality-enhancing effects of increased automation. Authors have furnished an Internet Appendix , which is available on the Oxford University Press Web site next to the link to the final published paper online. [ABSTRACT FROM AUTHOR]

As electric vehicle adoption accelerates and demand increases, the inability to produce batteries in sufficient quantities has emerged as a critical bottleneck in the electric vehicle supply chain. Given the impending climate change crisis, resolving this bottleneck is imperative to accelerate the transition to a zero-emission electric mobility future. One potential solution is the use of robotics for fast and cost-effective assembly of batteries at scale. This study proposes a three-stage digital twin design and analysis method to develop robotic workcells for fast and cost-effective assembly of electric vehicle battery modules. Using digital twin design and simulation, robotic assembly line configurations have been developed for battery module production at different scales. Digital twin analytics was used to evaluate and optimise the proposed robotic battery assembly system for speed and cost. Industrial automation experts were consulted to further improve robotic work cell layouts to minimise investment in robots. Because digital twins of robotic workcells have been used, the configurations of the battery assembly line, as designed and validated, are ready for immediate implementation. For practitioners, this study offers heuristic methods to determine the appropriate assembly line configuration, the required number of robots and humans, for a desired production volume. For researchers, this study outlines promising areas for future investigation. [ABSTRACT FROM AUTHOR]

This study consolidates the limited research on real-life customer experiences with hotels that have integrated service robots (SR) grounded in the Usability, Social Acceptance, User experience, and Societal Impact (USUS) Framework. Employing a systematic and thematic review across 42 articles, the findings reveal discrepancies in customer experience (CX) across dimensions like embodiment, emotions, human-oriented perceptions, security, and co-experience. Theoretically, the results suggest that long-term CX may be more grounded in pragmatic aspects rather than SR novelty. The review also cautions against indiscriminate SR integration, which can lead to unpredictable CX. The study contributes to academic discourse by identifying critical CX aspects, providing a future research agenda, and a foundation for developing robust theoretical frameworks. For practitioners, the insights offer guidance on effectively integrating AI technologies to enhance CX, such as focusing on SR functionality, optimal SR-human service offerings, and considering evolving CX for innovative technological integrations in hotels. [ABSTRACT FROM AUTHOR]

With increased application of service robots in the hospitality industry, many employees nowadays are required to work with service robots. Existing studies mainly focused on the impact of service robots on customers, but ignored the existence of employee-service robot (ESR) interaction. This study explores the relationship between ESR co-work experience and psychological empowerment. Data were collected from 318 hospitality employees in China and PLS-SEM was employed for a causal-predictive analysis. Our results show that ESR co-work experience can not only improve employee trust toward service robots, but also affect trust toward organizations, and these two types of trust further affect psychological empowerment (i.e. goal internalization, authority and capability). Some of these relationships are significantly affected by gender, with the proposed impacts being stronger among male employees. The theoretical contributions and managerial implications of our paper for hospitality robotics and human resource management are subsequently discussed. [ABSTRACT FROM AUTHOR]

The present study investigates the design analysis and development of a framed gantry robot (FGR) for 3-D concrete printing. Finite element simulations are performed for design validation and parameter estimation to conduct detailed analysis of the concrete printer. The kinematic and dynamic models of FGR are used for workspace analysis, motion control study, and motor sizing for printer development. The motion trajectory of the printer nozzle tip is evaluated using MATLAB simulations. An integrated programmable logic controller (PLC) with an in-built human-machine interface (HMI) through control algorithms has been implemented for interactive, user-friendly, and precise motion control. A separate control system is developed to monitor the pumping power and feed rate of mortar mix during printing. The simulated control scheme is validated experimentally using trajectory tracking for various input printing paths through HMI, with a maximum tracking error of 1 mm. Successful concrete printing trials have been conducted using various mix-design, following extensive evaluation of different printer and material parameters at the laboratory scale. The proposed design concept, mechanisms, interactive control interface, and tracking accuracy have been validated through simulation and experimental results, for ensuring quality concrete prints by the developed printer. Material testing parameters have been evaluated experimentally for modeling various mix-designs. To further evaluate printing performance, simulations of layer deformation in printed structures are conducted using finite element analysis, incorporating non-linear material models and various contact layer interactions. The simulated results of printed layers of different shapes of structures show a close resemblance with the experimental concrete printing trials. [ABSTRACT FROM AUTHOR]

This article introduces a novel six‐axis parallel manipulator co‐driven by motors with and without geared reducers. The design leverages the high stiffness provided by the geared motors and the high force‐sensing capability of the direct drives. When the geared motors move synchronously with their direct‐drive counterparts, the manipulator mimics a Delta robot's spatial translation, while the direct drives double as high‐performance force sensors. An asymmetric design of the manipulator is proposed to allocate a larger portion of torque to the geared motors, raising the overall stiffness of the mechanism. Relations between the external force to the driving torque are derived and the torque redistribution effect of the asymmetric design is illustrated numerically. Experimental results on prototypes of both the symmetric design and the asymmetric design show that direct drives consistently exhibit much higher linearity in the force‐to‐torque relationship compared to the geared drives, and also confirm that asymmetric design alleviates the torque burden on the direct drives. © 2024 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

Cooperative robots that perform safety functions as part of cooperative work with humans have been recently drawing attention. Proximity and tactile sensors that can be retrofitted to a robot's surface are important safety measures for existing robots that are not manufactured as cooperative robots to be used as cooperative robots. In this paper, a string‐like self‐capacitance‐based proximity and tactile sensor that can be easily wrapped around various types of existing robots is proposed. The novel and useful point in this paper is that it can be easily mounted on various robots by a flexible string‐like proximity and tactile sensor. The sensor module can be adjusted to the required length by connecting different numbers of modules depending on the mounting surface. As a demonstration, six prototype sensor modules were connected by each connector and wrapped around a robotic arm. The sensor modules on the robot's surface could detect an object within its proximity range and detect the rough pressure after contact. In addition, the robotic arm with the sensor modules could be controlled in real time using the data obtained from the sensor that are object detection data at proximity range. These results confirm that the proposed sensor module can be used in the proximity and tactile sensors of existing robots. © 2024 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

We analyse the effects of exposure to industrial robots on labour market adjustments, exploring longitudinal household data from China. We find that a one-standard-deviation increase in robot exposure led to a decline in labour force participation (−1%), employment (−7%) and hourly earnings (−8%) of Chinese workers. At the same time, among those who kept working, robot exposure increased the number of hours worked by 8%. These effects were concentrated among the less educated and larger among male, prime-age and older workers. We also find that more exposed workers increased their participation in technical training and were significantly more likely to retire earlier. [ABSTRACT FROM AUTHOR]

Objective: The purpose of this study is to identify the potential biomechanical and cognitive workload effects induced by human robot collaborative pollination task, how additional cues and reliability of the robot influence these effects and whether interacting with the robot influences the participant's anxiety and attitude towards robots. Background: Human–Robot Collaboration (HRC) could be used to alleviate pollinator shortages and robot performance issues. However, the effects of HRC for this setting have not been investigated. Methods: Sixteen participants were recruited. Four HRC modes, no cue, with cue, unreliable, and manual control were included. Three categories of dependent variables were measured: (1) spine kinematics (L5/S1, L1/T12, and T1/C7), (2) pupillary activation data, and (3) subjective measures such as perceived workload, robot-related anxiety, and negative attitudes towards robotics. Results: HRC reduced anxiety towards the cobot, decreased joint angles and angular velocity for the L5/S1 and L1/T12 joints, and reduced pupil dilation, with the "with cue" mode producing the lowest values. However, unreliability was detrimental to these gains. In addition, HRC resulted in a higher flexion angle for the neck (i.e., T1/C7). Conclusion: HRC reduced the physical and mental workload during the simulated pollination task. Benefits of the additional cue were minimal compared to no cues. The increased joint angle in the neck and unreliability affecting lower and mid back joint angles and workload requires further investigation. Application: These findings could be used to inform design decisions for HRC frameworks for agricultural applications that are cognizant of the different effects induced by HRC. [ABSTRACT FROM AUTHOR]

The increasing complexity and novel features of industrial robots have made their selection for specific applications a challenging task. Decision-makers are faced with the daunting task of navigating through various attributes and specifications, often under conditions of ambiguity and uncertainty. To assist in this complex decision-making process, this paper introduces a novel decision-making framework based on probabilistic uncertain linguistic q-rung orthopair fuzzy sets (PULq-ROFS). This framework effectively combines the strengths of probabilistic uncertain linguistic term set (PULTS) and q-rung orthopair fuzzy set (q-ROFS) to provide a more robust approach for handling ambiguity and uncertainty in the robot selection process. The proposed methodology integrates a multi-attribute group decision-making (MAGDM) approach. This approach utilizes the VIseKriterijumska Optimizacija I KOmpromisno Resenje (VIKOR) method in conjunction with the criterion importance via inter-criteria correlation (CRITIC) method. The CRITIC method determines attribute weights by analyzing both the differences and contrast intensity of criteria, thereby accounting for the relative strength and conflict among them. VIKOR is then employed to aggregate individual regret and group utility, resulting in a compromise solution that guides decision-makers toward the optimal robot selection. Proposed method provide the clarity and confidence to decision makers for choice of attributes and crediting this enhancement to the framework's transparency and its ability to incorporate a wide range of stakeholder perspectives. This framework facilitates a more inclusive decision-making process that acknowledges differing viewpoints and preferences. This proposed approach not only directs users toward optimal selections but also encourages collaboration among decision-makers, promoting a sense of shared ownership and responsibility in the selection process. This paper select the best robot by utilizing the benefits of both CRITIC and VIKOR method. The effectiveness of this integrated approach is validated through parameter and comparative analysis. The results demonstrate the potential applicability of the proposed methodology in real-world industrial robot selection scenarios, providing decision-makers with a powerful tool to navigate the complexities of modern robotic systems. [ABSTRACT FROM AUTHOR]

The convergence of data security and operational efficiency across various sectors, such as manufacturing, industry, logistics, agriculture, healthcare, and internet services, has been significantly enhanced using robotic-driven platforms and protocols. Notably, there has been a notable uptick in sophisticated cyberattacks targeting corporate and industrial robotic systems. These attacks are activated following the integration of the Internet of Things, the Internet, and organizational networks, as industrial units are interconnected. This study has formulated security-oriented criteria-based indicators for cyber-physical systems (CPS), encompassing industrial components and embedded sensors responsible for processing information logs and procedures. In this research, a robust security framework based on attack trees has been introduced, strategically focusing on addressing critical exploitable vulnerabilities rather than attempting to cover all CPS devices comprehensively. The systematic categorization of each physical device and its associated integrated sensors has been accomplished via data from logs and an information repository contained within a sensor index device library. [ABSTRACT FROM AUTHOR]

The U.S. Occupational Safety and Health Administration's (OSHA) most recent estimates show that between 35,000 and 62,000 injuries occur every year due to forklift-related accidents. According to the National Safety Council (NSC) data, approximately 78 fatalities are reported every year. Moreover, manual loading and unloading of heavy items is time-consuming and poses significant risks to workers in small and crowded warehouses. To address these safety and efficiency concerns cost-effectively, an automated robotic forklift prototype was developed. The key features of this industrial robot include full rotational mobility with a zero-degree turning radius, which reduces the time and space required to turn around corners. It can be operated remotely via a mobile phone using Bluetooth or wi-fi. The motion control system, based on the ESP-32 microcontroller, significantly enhances its operational efficiency compared to manual operation. This study evaluates the performance of the robotic forklift prototype, cost-effectiveness, with loading and unloading capabilities as effective solutions to the challenges faced by workers. Additionally, structural analysis using Ansys confirmed that the design can safely withstand forces 60% greater than the intended design load of 50 N. Furthermore, the maximum stress experienced by the fork is 67% below the material yield strength, further demonstrating robustness and reliability. The integration of advanced technology and Eco-friendly design positions this forklift as a viable and sustainable option for improving material handling in various industrial sectors. [ABSTRACT FROM AUTHOR]

This study investigates the potential of deploying a neural network model on an advanced programmable logic controller (PLC), specifically the Finder Opta™, for real-time inference within the predictive maintenance framework. In the context of Industry 4.0, edge computing aims to process data directly on local devices rather than relying on a cloud infrastructure. This approach minimizes latency, enhances data security, and reduces the bandwidth required for data transmission, making it ideal for industrial applications that demand immediate response times. Despite the limited memory and processing power inherent to many edge devices, this proof-of-concept demonstrates the suitability of the Finder Opta™ for such applications. Using acoustic data, a convolutional neural network (CNN) is deployed to infer the rotational speed of a mechanical test bench. The findings underscore the potential of the Finder Opta™ to support scalable and efficient predictive maintenance solutions, laying the groundwork for future research in real-time anomaly detection. By enabling machine learning capabilities on compact, resource-constrained hardware, this approach promises a cost-effective, adaptable solution for diverse industrial environments. [ABSTRACT FROM AUTHOR]

To address several challenges, including low efficiency, significant damage, and high costs, associated with the manual harvesting of Agaricus bisporus, in this study, a machine vision-based intelligent harvesting device was designed according to its agronomic characteristics and morphological features. This device mainly comprised a frame, camera, truss-type robotic arm, flexible manipulator, and control system. The FES-YOLOv5s deep learning target detection model was used to accurately identify and locate Agaricus bisporus. The harvesting control system, using a Jetson Orin Nano as the main controller, adopted an S-curve acceleration and deceleration motor control algorithm. This algorithm controlled the robotic arm and the flexible manipulator to harvest Agaricus bisporus based on the identification and positioning results. To confirm the impact of vibration on the harvesting process, a stepper motor drive test was conducted using both trapezoidal and S-curve acceleration and deceleration motor control algorithms. The test results showed that the S-curve acceleration and deceleration motor control algorithm exhibited excellent performance in vibration reduction and repeat positioning accuracy. The recognition efficiency and harvesting effectiveness of the intelligent harvesting device were tested using recognition accuracy, harvesting success rate, and damage rate as evaluation metrics. The results showed that the Agaricus bisporus recognition algorithm achieved an average recognition accuracy of 96.72%, with an average missed detection rate of 2.13% and a false detection rate of 1.72%. The harvesting success rate of the intelligent harvesting device was 94.95%, with an average damage rate of 2.67% and an average harvesting yield rate of 87.38%. These results meet the requirements for the intelligent harvesting of Agaricus bisporus and provide insight into the development of intelligent harvesting robots in the industrial production of Agaricus bisporus. [ABSTRACT FROM AUTHOR]

Industrial robotic arms are often subject to significant end-effector pose deviations from the target position due to the combined effects of nonlinear deformations such as link flexibility, joint compliance, and end-effector load. To address this issue, a study was conducted on the analysis and compensation of end-position errors in a six-degree-of-freedom robotic arm. The kinematic model of the robotic arm was established using the Denavit–Hartenberg (DH) parameter method, and a rigid–flexible coupled virtual prototype model was developed using ANSYS and ADAMS. Kinematic simulations were performed on the virtual prototype to analyze the variation in end-effector position errors under rigid–flexible coupling conditions. To achieve error compensation, an approach based on an Enhanced Crayfish Optimization Algorithm (ECOA) optimizing a BP neural network was proposed to compensate for position errors. An experimental platform was constructed for error measurement and validation. The experimental results demonstrated that the positioning accuracy after compensation improves by 75.77%, fully validating the effectiveness and reliability of the proposed method for compensating flexible errors. [ABSTRACT FROM AUTHOR]

Field implementations of fully underground sensor networks face many practical challenges that have limited their overall adoption. Power management is a commonly cited issue, as operators are required to either repeatedly excavate batteries for recharging or develop complex underground power infrastructures. Prior works have proposed wireless inductive power transfer (IPT) as a potential solution to these power management issues, but misalignment is a persistent issue in IPT systems, particularly in applications involving moving vehicles or obscured (e.g., underground) coils. This paper presents an automated methodology to sense misalignments and align IPT coils using robotic actuators and sequential Monte Carlo methods. The misalignment of a Class EF inverter-driven IPT system was modeled by tracking changes as its coils move apart laterally and distally. These models were integrated with particle filters to estimate the location of a hidden coil in 3D, given a sequence of sensor measurements. During laboratory tests on a Cartesian robot, these algorithms aligned the IPT system within 1 cm (0.025 coil diameters) of peak lateral alignment. On average, the alignment algorithms required less than four sensor measurements for localization. After laboratory testing, this approach was implemented with an agricultural sensor platform at the Utah Agricultural Experiment Station in Kaysville, Utah. In this implementation, a buried sensor platform was successfully charged using an aboveground, vehicle-mounted transmitter. Overall, this work contributes to the field of underground sensor networks by successfully integrating a self-aligning wireless power delivery system with existing agricultural infrastructure. Furthermore, the alignment strategy presented in this work accomplishes coil misalignment correction without the need for complex sensor or coil architectures. [ABSTRACT FROM AUTHOR]

Accurate dynamic model and response prediction of industrial robots (IRs) are prerequisites for production optimization before actual operation. In this study, a hybrid-driven dynamic position prediction (HDPP) approach integrating a parametric dynamic model (PDM) and learning-based residual error compensators (RECs) is developed to estimate the actual position of a robot end-effector based on the reference input trajectory. Firstly, a PDM consisting of a flexible dynamic model of the mechanical system and a servo system model is constructed as the primary predictor in HDPP. Meanwhile, a reinforcement learning (RL)-based parameter identification method is presented to obtain independent dynamic parameters, which integrates a CAD model, least squares estimation, and RL. Then, an REC based on the temporal convolutional network long short-term memory (TCN-LSTM) is proposed for each joint to compensate for the residual error after PDM prediction. A TCN is employed as the input of LSTM to extract and compress the discontinuous features, which can enhance the compensator's accuracy and stability. Additionally, a dynamics-integrated (DI) dataset construction scheme is developed for network training to boost the prediction accuracy. Finally, a series of experiments and comparative analysis are preformed to validate the performance of HDPP in terms of prediction accuracy and stability. [ABSTRACT FROM AUTHOR]

In robot grinding tasks, dual manipulators possess improved flexibility, which can cooperate to complete different tasks with higher efficiency and satisfactory effect. In collaborative robot grinding tasks, the critical issues lie in the motion trajectory planning of the two manipulators and trajectory tracking with satisfactory accuracy under the condition that the two manipulator ends apply force on each other. In order to accomplish the goals in a more concise and feasible way, a complete scheme for dual-arm robot grinding tasks is essential. To address this issue, taking the motion trajectory planning and impedance control into consideration, a novel scheme for dual manipulators to complete collaborative grinding tasks is presented in this paper. To this end, a dual-arm grinding system is first constructed, and the kinematic constraints in the cooperative motion are analyzed, based on which the motion trajectories of the dual manipulators are planned according to the grinding task objectives. Then, an impedance controller is designed to achieve accurate tracking of the motion trajectory in the grinding process. Finally, dual-arm collaborative simulations and grinding experiments are carried out, and the results show that the proposed method can achieve good motion results and better flexibility compared to the single-arm motion, which demonstrates the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Human–robot collaborative systems have been adopted by manufacturing organizations with the objective of releasing physical workload to the human factor. However, the roles and responsibilities of human operators in these semi-automated systems have not been properly analyzed. This might carry important consequences in the cognitive dimension of ergonomics, which then contradicts the main well-being goals of collaborative work. Therefore, we designed a series of collaborative scenarios where we shifted the assignment of work responsibilities between humans and robots while executing a quality inspection task. Variations in the state of cognitive ergonomics were estimated with subjective and objective techniques via workload tests and physiological responses respectively. Furthermore, we introduced a work design framework based on 50 state-of-the-art applications for a structured implementation of human–robot collaborative systems that contemplates the underlying organizational and technological components necessary to fulfill its basic functionalities. Human operators that possessed responsibility roles over collaborative robots presented better results in terms of cognitive workload and spare mental capacity alike. In this regard, mental demand is seen as a key workload variable to consider when designing collaborative work in current manufacturing settings. [ABSTRACT FROM AUTHOR]

This paper explores strategies for fostering efficient vocal communication and collaboration between human workers and collaborative robots (cobots) in assembly processes. Vocal communication enables the division of attention of the worker, as it frees their visual attention and the worker's hands, dedicated to the task at hand. Speech generation and speech recognition are pre-requisites for effective vocal communication. This study focuses on cobot assistive tasks, where the human is in charge of the work and performs the main tasks while the cobot assists the worker in various peripheral jobs, such as bringing tools, parts, or materials, and returning them or disposing of them, or screwing or packaging the products. A nuanced understanding is necessary for optimizing human–robot interactions and enhancing overall productivity and safety. Through a comprehensive review of the relevant literature and an illustrative example with worked scenarios, this manuscript identifies key factors influencing successful vocal communication and proposes practical strategies for implementation. [ABSTRACT FROM AUTHOR]

Automation via robotic systems is becoming widely adopted across many industries, but intelligent autonomy in dynamic environments is challenging to implement due to the difficulty of 3D vision. This paper proposes a novel method that utilizes in-situ 2D image processing to simplify 3D segmentation for robotic workspace detection in industrial applications. Using a TOF sensor mounted on a robotic arm, depth images of the workspace are collected. The algorithm identifies the contour of a table, filters extraneous data points, and converts only relevant data to a 3D pointcloud. This pointcloud is processed to identify the precise location of the workspace with regard to the robot. This method has been shown to be 10% more accurate and over 10,000% faster than a human analyzing the data in a GUI-based software using an octree region-based segmentation algorithm and provides consistent results, only limited by the resolution of the camera itself. [ABSTRACT FROM AUTHOR]

This study investigates the sector-specific economic impacts of robot density across countries with varying levels of technological adoption. The analysis focuses on three key sectors—manufacturing, industry (excluding construction), and construction—using panel data from 12 European Union countries between 2016 and 2022. To explore these relationships, the study employs the Method of Moments Quantile Regression (MMQR) methodology, which enables the assessment of the effects of robot density across different levels of sectoral performance while accounting for cross-country variations and heterogeneity. The findings reveal that robot density significantly enhances economic performance in the manufacturing sector, while its effects are smaller but still positive in the industrial and construction sectors. These results highlight the varying capacity of sectors and countries to integrate and benefit from automation technologies. The study concludes by emphasizing the importance of tailored automation strategies and policy interventions to maximize the economic benefits of robotics across diverse national and sectoral contexts. These insights contribute to understanding the role of automation in driving industrial transformation and economic growth. [ABSTRACT FROM AUTHOR]

This paper investigated the effect of automation processes in an industrial company engineering complex cyber-physical systems. The authors used an industry-as-laboratory approach as the research method, exploring an ongoing development project. The automation efforts focused on four areas: (1) test setup, (2) test execution, (3) test result analysis, and (4) documentation. All four areas showed promising results on increased effectiveness and/or efficiency. In particular, the automation of test result analysis will help the industrial company, KONGSBERG, reduce their main bottleneck in the test process, as well as reduce the risk of costly project delays. An automated system integration test process, facilitating iterative regression testing, will leverage the efficiency of the verification test process. [ABSTRACT FROM AUTHOR]

This study aims to provide a set of experimentally determined forces needed for gripping operations related to a robotically manipulated microliter manual pipette. The experiments are conducted within the scope of automated sample processing for polymerase chain reaction (PCR) analysis in small-sized to medium-sized laboratories where dedicated automated equipment is absent and where procedures are carried out manually. Automation is justified by the requirement for increased efficiency and to eliminate possible errors generated by lab technicians. The test system comprises an industrial robot; a dedicated custom gripper assembly necessary for the pipette; pipetting tips; and mechanical holders for tubes with chemical substances and genetic material. The selected approach is to measure forces using the robot's built-in force–torque sensor while controlling and limiting the pipette's gripping force and the robot's pushing force. Because the manipulation of different materials requires the attachment and discarding of tips to and from the pipette, the operator's perceived tip release force is also considered. [ABSTRACT FROM AUTHOR]

The trajectory planning of manipulators plays a crucial role in industrial applications. This importance is particularly pronounced when manipulators operate in environments filled with obstacles, where devising paths to navigate around obstacles becomes a pressing concern. This study focuses on the environment of frame obstacles in industrial scenes. At present, many obstacle avoidance trajectory planning algorithms struggle to strike a balance among trajectory length, generation time, and algorithm complexity. This study aims to generate path points for manipulators in an environment with obstacles, and the trajectory for these manipulators is planned. The search direction adaptive RRT*Connect (SDA-RRT*Connect) method is proposed to address this problem, which adaptively adjusts the search direction during the search process of RRT*Connect. In addition, we design a path process method to reduce the length of the path and increase its smoothness. As shown in experiments, the proposed method shows improved performances with respect to path length, algorithm complexity, and generation time, compared to traditional path planning methods. On average, the configuration space's path length and the time of generation are reduced by 38.7% and 57.4%, respectively. Furthermore, the polynomial curve trajectory of the manipulator was planned via a PSO algorithm, which optimized the running time of the manipulator. According to the experimental results, the proposed method costs less time during the manipulator's traveling process with respect to other comparative methods. The average reduction in running time is 45.2%. [ABSTRACT FROM AUTHOR]

A pseudo-spectral control algorithm based on adaptive Gauss collocation point reconstruction is proposed to efficiently solve the optimal dynamics control problem of industrial robots. A mathematical model for the kinematic relationship and dynamic optimization control of industrial robots has been established. On the basis of deriving the Legendre–Gauss collocation formula, a two-stage adaptive Gauss collocation strategy for industrial robot dynamics control variables was designed to improve the dynamics optimization control effect of industrial robot by improving the solution efficiency of constrained optimization problems. The results show that compared with the control variable parameterization method and the traditional Gaussian pseudo-spectral method, the proposed dynamic optimal control method based on an adaptive Gaussian point reconstruction algorithm can effectively improve the solving time and efficiency of constrained optimization problems, thereby further enhancing the dynamic optimization control and trajectory tracking effect of industrial robots. [ABSTRACT FROM AUTHOR]

In the safety technology of collaborative robots, standards differentiate between various collision types, the identification and differentiation of which are essential for ensuring safe operation. The objective of this paper is to develop and test a mechanism actuated by artificial muscle to examine the detection of these profiles in different collision scenarios. The ISO 15066 standard distinguishes between two types of collisions: quasi-static and transient. Using a simplified model, experiments were conducted to evaluate whether sensors could identify collision types accurately. The results demonstrate the feasibility of identifying collision types through sensor data. The findings have the potential to enhance the safety of collaborative robots. [ABSTRACT FROM AUTHOR]

Due to their precision, compact size, and high torque transfer, Rotate vector (RV) reducers are becoming more popular in industrial robots. However, repetitive operations and varying speed conditions mean that these components are prone to mechanical failure. Therefore, it is important to develop effective health monitoring (HM) strategies. Traditional approaches for HM, including those using vibration and acoustic emission sensors, encounter such challenges as noise interference, data inconsistency, and high computational costs. Deep learning-based techniques, which use current electrical data embedded within industrial robots, address these issues, offering a more efficient solution. This research provides transfer learning (TL) models for the HM of RV reducers, which eliminate the need to train models from scratch. Fine-tuning pre-trained architectures on operational data for the three different reducers of health conditions, which are healthy, faulty, and faulty aged, improves fault classification across different motion profiles and variable speed conditions. Four TL models, EfficientNet, MobileNet, GoogleNet, and ResNET50v2, are considered. The classification accuracy and generalization capabilities of the suggested models were assessed across diverse circumstances, including low speed, high speed, and speed fluctuations. Compared to the other models, the proposed EfficientNet model showed the most promising results, achieving a testing accuracy and an F1-score of 98.33% each, which makes it best suited for the HM of robotic reducers. [ABSTRACT FROM AUTHOR]

The efficient and sustainable design of robotic manufacturing cells is a critical aspect of modern industrial processes, for which energy and time efficiency play significant roles in achieving sustainability goals. In industrial practice, robotic cell design often involves methods such as predefined layout templates, empirical rules for positioning, and simulation-based validation. While these approaches provide a practical starting point, they may not fully account for the complex interdependencies between robot configuration, energy consumption, and operational efficiency. Consequently, opportunities for optimizing resource usage are frequently overlooked. This paper presents a novel methodology for optimizing the deployment of industrial robots and their peripherals, focusing on minimizing energy and time costs to enhance the sustainability of industrial processes. The proposed approach, grounded in experimental measurements and simulations, was validated through an experimental model of a welding robot station. The methodology integrates the analysis of the relationship between the robot base position, trajectory, and energy consumption. The results indicate that adjusting the relative positions of robots and work points can achieve energy savings of approximately six percent. Specifically, optimization reduced energy consumption by 1.6731 Wh per work cycle, translating to an annual savings of 0.8794 MWh for a 60 s clock cycle. These findings highlight the practical applicability of the proposed methodology, demonstrating its potential to significantly improve the energy and time efficiency of robotic workplaces. [ABSTRACT FROM AUTHOR]

Large-scale high-end equipment robotic precision operations have large workspaces and numerous uncertainties. They have an unevenly distributed error effect on the position in space. The current conventional stereotyping method ignores the uncertainty in the robotic system; the probabilistic or fuzzy method is often due to the lack of statistical samples in the project, and it is difficult to accurately define the probabilistic or fuzzy model because the probabilistic distribution pattern or fuzzy affiliation cannot be known in advance. In this paper, we propose a non-probabilistic reliability-based robot workspace partitioning method that only needs to know the upper and lower bounds of the values of the uncertain parameters and is adapted to realize accurate calibration of robots in small-sample or information-poor scenarios. The method considers the differences in non-probabilistic reliability of robot end positions in different workspace ranges and uses KLFCM clustering combined with a genetic algorithm to perform a two-stage hierarchical partitioning optimization. The experimental results show that compared with the global compensation, the average values of the upper and lower limits of the x, y, and z direction error intervals of the partitioned compensation are reduced by 31.17%, 7.26%, 14.30%, 34.91%, 2.48%, and 35.82%, respectively, verifying that the method in this study can more accurately realize the partitioned categorization calibration and compensation of the robot, and effectively improve the reliability and spatial adaptability of parameter calibration and compensation of the robot in the full workspace domain. The reliability and spatial adaptability of the parameter calibration and compensation are effectively improved in the entire workspace domain of the robot. [ABSTRACT FROM AUTHOR]

Current industrial robotic manipulators have made their lack of flexibility evident. The systems must know beforehand the piece and its position. To address this issue, contemporary approaches typically employ learning-based techniques, which rely on extensive amounts of data. To obtain vast data, an often sought tool is an extensive grasp dataset. This work introduces our Physics-Based Self-Supervised Grasp Pose Detection (PBSS-GPD) pipeline for model-based grasping point detection, which is useful for generating grasp pose datasets. Given a gripper-object pair, it samples grasping pose candidates using a modified version of GPD (implementing inner-grasps, CAD support...) and quantifies their quality using the MuJoCo physics engine and a grasp quality metric that takes into account the pose of the object over time. The system is optimized to run on CPU in headless-parallelized mode, with the option of running in a graphical interface or headless and storing videos of the process. The system has been validated obtaining grasping poses for a subset of Egad! objects using the Franka Panda two-finger gripper, compared with state-of-the-art grasp generation pipelines and tested in a real scenario. While our system achieves similar accuracy compared to a contemporary approach, 84% on the real-world validation, it has proven to be effective at generating grasps with good centering 18 times faster than the compared system. [ABSTRACT FROM AUTHOR]

This article presents the fundamental principles of robot accuracy. It characterizes a modular robot, describes the measurement setup, and outlines the methodology for evaluating positioning accuracy across different configurations of the modular robot (four, five, and six modules) under varying loads of 6, 10, and 16 kg. An analysis was conducted on the impact of load changes on four- and five-module configurations, as well as the effect of configuration changes on the robot's performance with 6 and 10 kg loads. The findings indicate that both the number of modules and the load affect positioning accuracy. This article highlights the importance of selecting the optimal configuration based on planned industrial tasks to ensure the highest precision and operational efficiency. [ABSTRACT FROM AUTHOR]

This research work presents an integrated method leveraging Convolutional Neural Networks and Recurrent Neural Networks (CNN-RNN) to enhance the accuracy of predictive maintenance and fault detection in DC motor drives of industrial robots. We propose a new hybrid deep learning framework that combines CNNs with RNNs to improve the accuracy of fault prediction that may occur on a DC motor drive during task processing. The CNN-RNN model determines the optimal maintenance strategy based on data collected from sensors, such as air temperature, process temperature, rotational speed, and so forth. The proposed AI model has the capacity to make highly accurate predictions and detect faults in DC motor drives, thus helping to ensure timely maintenance and reduce operational breakdowns. As a result, comparative analysis reveals that the proposed framework can achieve higher accuracy than the current existing method of combining CNN with Long Short-Term Memory networks (CNN-LSTM) as well as other CNNs, LSTMs, and traditional methods. The proposed CNN-RNN model can provide early fault detection for motor drives of industrial robots with a simpler architecture and lower complexity of the model compared to CNN-LSTM methods, which can enable the model to process faster than CNN-LSTM. It effectively extracts dynamic features and processes sequential data, achieving superior accuracy and precision in fault diagnosis, which can make it a practical and efficient solution for real-time fault detection in motor drive control systems of industrial robots. [ABSTRACT FROM AUTHOR]

Human hands have over 20 degrees of freedom, enabled by a complex system of bones, muscles, and joints. Hand differences can significantly impair dexterity and independence in daily activities. Accurate assessment of hand function, particularly digit movement, is vital for effective intervention and rehabilitation. However, current clinical methods rely on subjective observations and limited tests. Smart gloves with inertial measurement unit (IMU) sensors have emerged as tools for capturing digit movements, yet their sensor accuracy remains underexplored. This study developed and validated an IMU-based smart glove system for measuring finger joint movements in individuals with hand differences. The glove measured 3D digit rotations and was evaluated against an industrial robotic arm. Tests included rotations around three axes at 1°, 10°, and 90°, simulating extension/flexion, supination/pronation, and abduction/adduction. The IMU sensors demonstrated high accuracy and reliability, with minimal systematic bias and strong positive correlations (p > 0.95 across all tests). Agreement matrices revealed high agreement (<1°) in 24 trials, moderate (1–10°) in 12 trials, and low (>10°) in only 4 trials. The Root Mean Square Error (RMSE) ranged from 1.357 to 5.262 for the 90° tests, 0.094 to 0.538 for the 10° tests, and 0.129 to 0.36 for the 1° tests. Likewise, mean absolute error (MAE) ranged from 0.967 to 4.679 for the 90° tests, 0.073 to 0.386 for the 10° tests, and 0.102 to 0.309 for the 1° tests. The sensor provided precise measurements of digit angles across 0–90° in multiple directions, enabling reliable clinical assessment, remote monitoring, and improved diagnosis, treatment, and rehabilitation for individuals with hand differences. [ABSTRACT FROM AUTHOR]

In the domain of modern manufacturing digitalization, artificial intelligence tools are increasingly employed for condition monitoring and technical diagnostics. However, the majority of existing methodologies primarily concentrate on the technical diagnosis of rotating machines, with a noticeable lack of research addressing these issues in sequential machines. In this paper, we deal with the selection of suitable vibration signal characteristics for the detection of an industrial robot's release from its base during a handling operation. Statistical methods, including one-way ANOVA and t-tests, were used to identify the most significant features, which allowed us to isolate vibration metrics with significant predictive potential. These selected features were then used as inputs to various machine learning models to evaluate the hypothesis that these parameters can reliably indicate fastening releasing events. The results show that the optimized parameters significantly improve the detection accuracy, thus providing a reliable basis for future applications in predictive maintenance and monitoring. The findings represent an advance in robotic condition monitoring, providing a structured approach to feature selection that improves the reliability of disconnection detection in automated systems with potential applicability in various industrial environments. [ABSTRACT FROM AUTHOR]

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There is a growing interest in whether social robots, which are embodied and exhibit human-like behaviour, can be used for teaching and learning. Still, very few studies focus on the teacher's role. This study focuses on how a teacher acted in a learning-by-teaching activity with 20 children. In this small-scale field experiment, the teacher's interactions and teaching actions were observed when the teacher scaffolded a learning activity where children played a collaborative digital mathematics game to strengthen their mathematical reasoning and conceptual understanding of arithmetic. When playing, the children were acting as tutors for a tutee, according to the learning-by-teaching principle. In one scenario, the tutee was a younger child; in the other, the tutee was a social robot. Twenty 30-minute game-playing sessions are observed, video-recorded, and transcribed. The study explores the teacher's interactions and teaching actions in the two scenarios and discusses the results from the perspective of the teacher's role, social norms, and teacher digital competence. The interaction and thematic analyses show similarities and characteristic differences in the teacher's interaction patterns in the two scenarios. The teaching actions are similar on a structural level and differ regarding the types and distribution of teaching actions. In the child-child scenario, the teacher directs most teaching actions to both players, and the actions are didactic (mathematical) scaffolding. In contrast, in the child-robot scenario, the teacher only addresses the tutor, and the scaffolding is socially oriented. Implications for a teaching practice involving social robots as learning companions are discussed regarding teachers' presence and participation, types of social robot knowledge that go beyond digital competence, and new challenges introduced by using social robots as learning companions in the classroom. The study contributes new insights into the teacher's role and actions when teaching with a social robot in a collaborative learning situation, which is relevant for educational research and teaching practice. [ABSTRACT FROM AUTHOR]

Facing the system uncertainties caused by unmodeled dynamics and unpredictable external disturbances, the robot position control for meeting the high-performance control requirements on higher accuracy and faster beat is vital for many industrial applications, such as welding and laser cutting tasks. This work aims to cope with the problem of precise and fast position tracking for robot manipulators with an effective and safe control scheme. Specifically, a discrete-time super-twisting observer (STO) is integrated into the scheme to estimate the uncertain dynamics (e.g. unmodeled dynamics and external disturbances) in the feedforward compensation part of the dynamics. Subsequently, a discrete-time fast terminal sliding mode controller (FTSMC) dominates the robot control to guarantee fast convergence of the position tracking error. The significant improvement of the proposed method with respect to other discrete-time sliding mode control approaches lies in that it is capable of alleviating the chattering-like problem, achieving a fast convergence and improving the robustness of sliding mode control against uncertain dynamics. To illustrate the effectiveness of the presented control scheme, several experiments on a six degrees of freedom (6DoF) robot manipulator are provided. [ABSTRACT FROM AUTHOR]