Search

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]

Collaborative robots (cobots) are a type of Industry 4.0 technology designed to support manufacturing workers and create smart working environments (also called as Industry 5.0). However, little is known about how the use of cobots shapes workers' skills. We analyse this in four types of human-cobot interaction: coexistence, synchronism, cooperation, and collaboration. We examine the implementation of cobots by a leading global provider using a qualitative research based on: (i) analysis of reports regarding the implementation of 200 cobots in 138 companies, (ii) interviews with the team and customers, (iii) six-month follow-up of cobot implementation in a manufacturing plant, and (iv) interviews with two cobot competitors. Our findings demonstrate how each type of human-cobot interaction influences workers' skills in various manufacturing activities. We observe that most companies are in early stages of implementation, focusing on worker substitution. However, we identify a range of effects, including deskilling or reskilling, depending on the type of manufacturing activity analysed. The upskilling effect is particularly evident in the most advanced types of human-cobot interaction, regardless of the company's size. As a main contribution, this paper sheds light on how companies can enhance workers' skills through other levels of interaction between workers and cobots. [ABSTRACT FROM AUTHOR]

With the increased use of collaborative robots, a new production model of the human-robot collaborative hybrid assembly cell (HRCHAC) is becoming a new trend in customised production. Collaborative assembly between workers and robots in assembly cells can significantly increase productivity and improve the well-being of workers once the distribution of tasks and resources is optimised. This paper proposes a new integrated task allocation model to better utilise human-robot collaboration to increase productivity and improve worker well-being. The developed model enables the skills of both workers and robots to be fully utilised while ensuring economic efficiency and the effective protection of workers' physiological and psychological health. First, the product assembly process is decomposed into several assembly tasks, and the characteristics of each task are analysed. Second, a bi-objective mixed-integer planning model is developed with the objectives of minimising unit product assembly time and maximising total task matching. The ergonomics-related objectives are considered in terms of both the physiological and psychological fatigue of the worker, and relevant constraints are established. An improved NSGA-II algorithm is developed to determine the final task allocation scheme. Finally, the proposed method is applied to a real industrial case to verify the effectiveness of the approach. [ABSTRACT FROM AUTHOR]

Warehouse operations have been significantly improved because of the rapid advancement of cyber-physical system technologies, preparing for Work-of-the-Future. The challenge, however, is how to design a collaborative system to deliver optimal performance by multiple agents who are highly distributed but interconnected and operate with technologies that provide massive amounts of real-time data. To address the challenge, in this article, the Cyber Collaborative Protocol for Dual-Cycle Task in Future Warehouse is developed to minimise total operation cost and time. The problem is addressed in two phases; the Global and Local phases. The global phase has higher computational power, maintaining a mathematical model, while the local phase has limited computational power and time, utilising heuristics to deliver the outcome. Computer experiments are utilised for validating the designed protocol compared with other alternatives. The results show that, in all given scenarios, the newly designed protocol outperforms alternatives with statistical significance. The original contribution of this research is the design and control of Cyber Collaborative Warehouse operations with a new focus on collaborative multi-agent interactions. In addition, a major implication is that future warehouses can benefit competitively by operating with Task Administration Protocols such as the new Cyber Collaborative Protocol for Dual-Cycle Task in Future Warehouse. [ABSTRACT FROM AUTHOR]

Welding is the most critical operation in the shipbuilding process and has a significant influence on the production cost and quality of ships. Therefore, the welding operation must be optimised. This paper presents a real-world welding gantry robot scheduling problem at shipyards, in which three gantry robots function in parallel. Welding gantry robots cannot cross each other and should operate over a certain distance to avoid collisions. To minimise the makespan, the welding tasks given by line segments should be evenly distributed among the three gantry robots. The welding tasks assigned to each robot should be optimally sequenced to minimise the completion time, including the waiting time required to prevent collisions with neighbouring robots. In addition, long welding edges are split, and the split small length edges are assigned to the gantry robots. This paper proposes a mixed-integer linear programming model, three-stage solution approach, and variable neighbourhood search algorithm to solve this problem. Experimental tests conducted on 20 real problem instances revealed that the proposed approach can reduce the makespan by 14% on average when compared with the conventional method. [ABSTRACT FROM AUTHOR]

Summary: Obstacle avoidance planning is the primary element in ensuring safe robot applications such as welding, assembly, and drilling. The states in the configuration space (C‐space) provide the pose information of any part of the manipulator and are preferentially considered in motion planning. However, it is difficult to express the environmental information directly in the high dimensional C‐space, limiting the application of C‐space obstacle avoidance planning. This paper proposes a singular manifold splitting C‐space method and designs a compatible obstacle avoidance strategy. The specific method is as follows: first, according to the specific structure of industrial robots, arm‐wrist separation obstacle avoidance planning is proposed to fix the robot as a 3R manipulator to reduce the dimension of C‐space. Next, the C‐space is segmented according to the singular manifolds, and the unique domain is delineated to complete the streamlining of the volume of the C‐space. Then, with the help of the point cloud, the obstacles are enveloped and mapped to the unique domain to construct the pseudo‐obstacle map. Industrial robots' obstacle avoidance planning is completed based on the pseudo‐obstacle map combined with an improved Rapidly‐Exploring Random Trees (RRT) algorithm. This method dramatically improves the efficiency of obstacle avoidance planning in the C‐space and avoids the effect of singularities on industrial robots. Finally, the effectiveness of the method is verified by physical experiments. [ABSTRACT FROM AUTHOR]

In this study, a system for measuring the positional accuracy of industrial robots using 3D vision and networking technology has been proposed. The 3D vision system comprises a charge-coupled device (CCD) module, a reference sphere module, and a signal processing module. The reference sphere module serves as the sensing object, while the CCD module captures displacement images of the reference sphere module. The signal processing module receives and processes signals from the CCD module. It utilizes a self-developed Visual C + + interface to calculate the X, Y, Z, θx, and θy (five-degree-of-freedom, 5-DOF) positional accuracy of the industrial robot. Experimental results demonstrate that the linear accuracy (X, Y, Z) and angular accuracy (θx, θy) of the 3D vision system are approximately 3 μm and 0.15°, respectively. Additionally, a networking architecture connects the controller of the machine tool with the industrial robot to enable synchronized motion path planning. The industrial robot can be calibrated using the high-precision machine tool equipped with the 3D vision-based positional accuracy measurement system. [ABSTRACT FROM AUTHOR]

Today's customer lifestyles have reshaped their expectations and preferences, driving a growing demand for tailor-made products. While current conventional manufacturing (MNF) systems are robust, they often lack the flexibility needed to accommodate customization. Most MNF systems, despite advances in technology and machinery, still rely on executing predefined instructions, limiting their flexibility. In contrast, human workers excel at handling product variations due to their cognitive abilities, which allow them to perceive, analyze, and make appropriate decisions to adapt to changing conditions. This study introduces virtual numerical control (VNC) as a solution to upgrade MNF systems and overcome these limitations. VNC aims to transform MNF systems into cognitive entities capable of autonomous decision-making, enabling greater flexibility to meet customization demands. To demonstrate the potential of VNC, we implemented it in a welding system as a practical case study. The results showed that VNC enabled the system to operate autonomously. It accurately identified the shape of the objects to be welded, determined the appropriate welding paths, and executed them with high precision, all without human intervention. This highlights the significant potential of VNC technology for broader applications in industrial automation in welding and beyond. [ABSTRACT FROM AUTHOR]

Construction robots play a pivotal role in enabling intelligent processes within the construction industry. User‐friendly interfaces that facilitate efficient human–robot collaboration are essential for promoting robot adoption. However, most of the existing interfaces do not consider contextual information in the collaborative environment. The situation where humans and robots work together in the same jobsite creates a unique environmental context. Overlooking contextual information would limit the potential to optimize interaction efficiency. This paper proposes a novel context‐aware method that utilizes a two‐stream network to enhance human–robot interaction in construction settings. In the proposed network, the first‐person view‐based stream focuses on the relevant spatiotemporal regions for context extraction, while the motion sensory data‐based stream obtains features related to hand motions. By fusing the vision context and motion data, the method achieves gesture recognition for efficient communication between construction workers and robots. Experimental evaluation on a dataset from five construction sites demonstrates an overall classification accuracy of 92.6%, underscoring the practicality and potential benefits of the proposed method. [ABSTRACT FROM AUTHOR]

Computerized numerical controls (CNCs) have been invented for the automation of industrial processes. They are used, when the process to be automated is required to be exact and fast with repeatable quality. Originally, the use of CNCs was primarily focused on milling and drilling processes. Today, CNCs are utilized in a wide range of industrial processes due to the growing importance of automation. However, the integration of process information or adaptation to the needs of these processes to achieve advanced manufacturing with CNCs is difficult: Industrial CNCs are rather closed real-time machining systems. Today, process integration is possible, when the interaction between the process and the machine is decoupled in view of the bandwidth of machine dynamics and process dynamics. There are interfaces that allow for process-motivated control loops that are realized on top of the machine control loop (e.g. chatter control). Then, machine-integrated real-time control is not the focus. Besides, it is often possible to change desired values inside the control loop (position, velocity, acceleration) on an axes basis. In this case, adaptation to the process can be realized in each computation cycle. However, process dynamics and machine axes dynamics are generally treated separately. The same holds for extra actuators (e.g., in the spindle) for position control. This paper has two goals. First, it wants to create an understanding for different levels of process-machine coupling. Second, the problem of direct coupling of process dynamics and machine dynamics is focused. Machining systems design propositions as well as some examples for the coupling of process and machine dynamics in the CNC are given. [ABSTRACT FROM AUTHOR]

AbstractThe rotary vector reducer (RV reducer) is a key reduction mechanism for industrial robots, and its torsional stiffness directly determines positioning accuracy and dynamic characteristics. Unlike traditional time-varying torsional stiffness analysis methods considering limited factors and statics solution method, a time-varying torsional stiffness analysis method considering output torque variability and dynamic nonlinearity is proposed to assess the time-varying torsional stiffness of the reducer. Firstly, the rigid-flexible coupling multi-body dynamic model was built employing software ADAMS, with crank shaft and cycloidal gears being treated as flexible bodies. Its correctness was verified under standard condition through grey correlation theory and the torsional stiffness under different conditions was obtained, respectively. Secondly, a contact finite element model of the reducer was established, taking contact relationships between different shaft-gear-bearings into account. As a comparison, a rotating-join finite element model was also constructed. And their torsional stiffness under experimental condition was calculated. Finally, the torsional stiffness test was conducted to verify the accuracy of the three models under this condition. The results show that the time-varying torsional stiffness obtained by the proposed method is only 8.83% different from the test results and exhibiting a certain periodic pattern under standard conditions. [ABSTRACT FROM AUTHOR]

Commercial automation systems for small- and medium-sized laboratories, including research environments, are often complex to use. For liquid handling systems (LHS), development is required not only for the robot's movements but also for adapting the bioanalytical method to the automated system. This study investigates whether a more human-like automation strategy—using a robotic system (RS)—is more suitable for research laboratories than a professional automation approach utilizing a commercial automated LHS. We conducted a series of measurements for protein determination using a Bradford assay manually, with a fully automated LHS, and with our human-like RS. Although the hand-like RS approach requires more than twice the time of the LHS, it achieved the best standard deviation in this setup (RS = 0.5, manual = 0.71, LHS = 0.86). Due to the low limit of detection (LOD) and limit of quantification (LOQ), most protein samples could be quantified with the RS (samples below LOQ = 9.7%, LOD = 0.23; LOQ = 0.25) compared to manual (samples below LOQ = 28.8%, LOD = 0.24; LOQ = 0.26) and the LHS (samples below LOQ = 36.1%, LOD = 0.27; LOQ = 0.31). In another time-dependent enzymatic assay test, the RS achieved results comparable to the manual method and the LHS, although the required time could be a constraint for short incubation times. Our results demonstrate that a more hand-like automation system closely models the manual process, leading easier to accurate bioanalytical results. We conclude that such a system could be more suitable for typical research environments than a complex LHS. [ABSTRACT FROM AUTHOR]

The purpose of this article is to propose a fuzzy logic system as a tool for automated risk identification of potential technical challenges and social barriers during the implementation of artificial intelligence-based co-bots on work-stations in manufacturing enterprises. On the basis of an extensive literature review, as well as industry reports and expert consultations, the basic challenges and enterprise barriers occurring during the implementation of changes in enterprises, especially during the implementation of the latest technologies, were selected. A fuzzy logic model was then developed that, based on the values of the input factors, generates an answer as to whether there is a risk of technical or social challenges in an enterprise when implementing the latest technologies. The results generated by the developed model, when confronted with expert knowledge, experience and subjective assessments, showed that the model works as expected. The results of the study suggest that the use of fuzzy logic can effectively support companies in detecting challenges and obstacles, thereby facilitating decision-making in reducing the risk of their occurrence. Adaptation to the conditions currently prevailing in the company allows for dynamic adjustment of co-bot deployment strategies, which in turn can lead to more effective management of technological changes and minimization of potential operational disruptions. [ABSTRACT FROM AUTHOR]

This study examines the application of BPMN 2.0 notation within the implementation process of modern Industry 5.0 solution -- cobots (collaborative robots). The research aims to compare the operational efficiencies of traditional human-only production processes with those augmented by cobots. The authors analyze two variants of furniture production processes: one involving only human workers and another combining cobots and human workers. The efficiency of these processes was assessed in terms of production time and output quantity. The investigation revealed that the production process incorporating cobots was more efficient, with a reduction in production time by 4.76% and an increase in the amount of products produced by 35.5%. The study concludes that integrating cobots into production processes can significantly enhance efficiency, reducing time and increasing output. BPMN 2.0 notation is a critical tool for modeling, automating, and monitoring these improved processes, aiding organizations in making strategic decisions towards adopting robotic solutions to boost productivity and competitive edge in the marketplace. [ABSTRACT FROM AUTHOR]

Collaborative robots are becoming popular because they can operate without safety fences and can work in close proximity to humans. Proximity and tactile sensors on the robot's surface are important safety measures for existing robots. In this paper, we propose a string‐like ToF and self‐capacitance combined sensor that can be easily wrapped around various types of existing robots. The sensor can be freely connected to other sensors, and the length of the connected sensors attached can be adjusted depending on the shape of the robot. Prototype sensors were fabricated, and four connected sensors were wrapped around the robotic arm. Sensors on the robot's surface can detect objects within the proximity range and determine contact. In addition, a robotic arm with sensors can be controlled in real‐time using measured data from the sensors. It was confirmed that the proposed sensor can be used for proximity and tactile sensing on existing robots. © 2024 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

Accurate dynamic models that update in real‐time are vital for industrial robots to adapt flexibly and robustly to evolving environments and tasks, enabling precise model‐based control, motion planning and disturbance estimation. However, the identification of a dynamic robot model involves multiple challenges. First, existing friction models cannot accurately characterize torque variance during prolonged robot operations. Second, traditional offline identification methods such as the least squares (LS) method, suffer from over‐fitting when the inertia parameters are unknown a priori. Furthermore, existing online identification approaches, such as sliding LS, fail to meet the accuracy and real‐time requirements of industrial robots. To address these challenges, this article proposes a two‐stage Bayesian framework for rapid offline identification and online updating of industrial robot dynamics. In the offline stage, we developed a recursive sparse Bayesian learning (RSBL) method to select the dominant parameters and discover a thermal‐related nonlinear dynamic friction model from data, which was then used to find a sparser and simpler inertia model. The obtained friction and inertia models were used as prior knowledge for online updating. The RSBL method significantly reduces the computational complexity and runtime, while it also accelerates online model updating. Experimental results for a 6‐DOF medium‐load robot validate dual offline improvements in prediction accuracy and model sparsity, along with higher online prediction accuracy and shorter computational time. [ABSTRACT FROM AUTHOR]

In this study, we explore the key factors that educators must consider when designing challenges based on the Challenge-Based Learning (CBL) strategy, where the industrial sector serves as the educational partner. Building on our proposed definition of the CBL technique, we outline a methodology complete with practical suggestions to effectively tackle the challenges encountered during the strategy's implementation. The recommended steps involve choosing a training partner, establishing the company's role in the challenge, taking economic factors into account, and pinpointing crucial milestones in the course development, which includes recognizing the partner's involvement and significance in the course evaluation. Our proposal draws from the authors' experiences in applying this methodology within the context of an Industrial Automation course. [ABSTRACT FROM AUTHOR]

This article proposes a novel framework for the rapid implementation of automation programs in industrial heating and cooling systems. The global push for sustainability necessitates significant infrastructural transformations within these systems, which currently rely heavily on fossil fuels and are responsible for 75% of industrial final energy consumption. Our research highlights the critical role of design patterns and object-oriented programming principles to address the complex integration of additional energy converters and storage into automation programs. By leveraging design patterns, our framework encapsulates the intricacies of various components, such as actuators, sensors, and storage, within a comprehensive object-oriented model that also allows the integration of different control strategies. Qualitatively, this approach enhances the reusability, scalability, and adaptability of automation programs. Therefore, quantitatively, our framework enables a more resilient and efficient energy system. The framework is validated through its application to a complex, cross-linked industrial heating and cooling system at the ETA Research Factory of the Technical University of Darmstadt. Using the developed framework reduces implementation effort significantly due to its consistent and modular structure resulting from the reusable design patterns. [ABSTRACT FROM AUTHOR]

Due to the COVID-19 outbreak, industries have gained a thrust on contactless processing for computing technologies and industrial automation. Cloud of Things (CoT) is one of the emerging computing technologies for such applications. CoT combines the most emerging cloud computing and the Internet of Things. The development in industrial automation made them highly interdependent because the cloud computing works like a backbone in IoT technology. This supports the data storage, analytics, processing, commercial application development, deployment, and security compliances. Now amalgamation of cloud technologies with IoT is making utilities more useful, smart, service-oriented, and secure application for sustainable development of industrial processes. As the pandemic has increased access to computing utilities remotely, cyber-attacks have been increased exponentially. This paper reviews the CoT's contribution to industrial automation and the various security features provided by different tools and applications used for the circular economy. The in-depth analysis of security threats, availability of different features corresponding the security issues in traditional and non-traditional CoT platforms used in industrial automation have been analysed. The security issues and challenges faced by IIoT and AIoT in industrial automation have also been addressed. [ABSTRACT FROM AUTHOR]

Green energy transformation requires comprehensive strategies that include both innovations in energy production and more efficient energy use. This article investigates the potential for saving electrical energy in industrial automation systems by utilizing bistable switching/relay. Compared to traditional systems, these innovative solutions demonstrate significant reductions in energy consumption. A market analysis of available bistable relays, along with experimental determination of their control conditions, highlights their application potential and indicates the benefits of their implementation. The findings suggest that replacing classical relays with their bistable counterparts could significantly contribute to global sustainability efforts. The article presents the process of redesigning a standard industrial relay into a bistable design. Adding two additional elements achieved the intended bistable functionality. The article calls for increased research and investment in such technologies, emphasizing that the energy-saving potential offered by bistable switching/relay circuits should not be overlooked. [ABSTRACT FROM AUTHOR]

The exploration of large-scale unknown environments can benefit from the deployment of multiple robots for collaborative mapping. Each robot explores a section of the environment and communicates onboard pose estimates and maps to a central server to build an optimized global multi-robot map. Naturally, inconsistencies can arise between onboard and server estimates due to onboard odometry drift, failures, or degeneracies. The mapping server can correct and overcome such failure cases using computationally expensive operations such as inter-robot loop closure detection and multi-modal mapping. However, the individual robots do not benefit from the collaborative map if the mapping server provides no feedback. Although server updates from the multi-robot map can greatly alleviate the robotic mission strategically, most existing work lacks them, due to their associated computational and bandwidth-related costs. Motivated by this challenge, this paper proposes a novel collaborative mapping framework that enables global mapping consistency among robots and the mapping server. In particular, we propose graph spectral analysis, at different spatial scales, to detect structural differences between robot and server graphs, and to generate necessary constraints for the individual robot pose graphs. Our approach specifically finds the nodes that correspond to the drift's origin rather than the nodes where the error becomes too large. We thoroughly analyze and validate our proposed framework using several real-world multi-robot field deployments where we show improvements of the onboard system up to 90% and can recover the onboard estimation from localization failures and even from the degeneracies within its estimation. [ABSTRACT FROM AUTHOR]

Summary: The application of computer technologies associated with sensors and artificial intelligence (AI) in the quantification and qualification of quality parameters of meat products of various domestic species is an area of research, development, and innovation of great relevance in the agri‐food industry. This review covers the most recent advances in this area, highlighting the importance of computer vision, artificial intelligence, and ultrasonography in evaluating quality and efficiency in meat products' production and monitoring processes. Various techniques and methodologies used to evaluate quality parameters such as colour, water holding capacity (WHC), pH, moisture, texture, and intramuscular fat, among others related to animal origin, breed and handling, are discussed. In addition, the benefits and practical applications of the technology in the meat industry are examined, such as the automation of inspection processes, accurate product classification, traceability, and food safety. While the potential of artificial intelligence associated with sensor development in the meat industry is promising, it is crucial to recognise that this is an evolving field. This technology offers innovative solutions that enable efficient, cost‐effective, and consumer‐oriented production. However, it also underlines the urgent need for further research and development of new techniques and tools such as artificial intelligence algorithms, the development of more sensitive and accurate multispectral sensors, advances in computer vision for 3D image analysis and automated detection, and the integration of advanced ultrasonography with other technologies. Also crucial is the development of autonomous robotic systems for the automation of inspection processes, the implementation of real‐time monitoring systems for traceability and food safety, and the creation of intuitive interfaces for human‐machine interaction. In addition, the automation of sensory analysis and the optimisation of sustainability and energy efficiency are key areas that require immediate attention to address the current challenges in this agri‐food and agri‐industrial sector, highlighting and emphasising the importance of ongoing innovation in the field. [ABSTRACT FROM AUTHOR]

Industrial robot machining systems are used extensively in processing and manufacturing sectors including aerospace, large ships, rail transit, engineering machinery, semiconductor processing, medical equipment, and new energy vehicles. However, chatter during the machining process can considerably diminish the machining accuracy and surface quality. In severe cases, it can cause damage to the tool and the machining robot. Robot machining chatter research is required to improve the stability of the machining process for high-efficiency, high-quality robotic machining. This review comprehensively summarizes research progress in three areas: robot machining chatter mechanisms, chatter stability prediction and online monitoring, and chatter suppression strategies. First, the generation mechanisms of regenerative chattering and modal-coupling chattering are clarified. Second, the methods of chattering stability prediction and online monitoring of robot machining are summarized. The differences between passive suppression, semi-active suppression, and active suppression strategies are explained. Existing problems and future directions in robot machining chattering research are summarized and prospected. [ABSTRACT FROM AUTHOR]

The flange holes on large aluminum ball shells are processed on-site by industrial robots, and the flange holes need to ensure the hole position and axial accuracy requirements at the same time, but it is challenging to ensure that these two indexes meet the requirements at the same time due to the lower machining accuracy of the industrial robots. For this reason, the paper proposes a robot integrated error compensation method for hole position and axial deviation constraints. First, the robot machining path is generated, and the cutting allowance and hole inner wall measurement strategy are determined by running the machining path empty before machining. Integrated error compensation is then introduced and a new hole surface is constructed as a mirror surface under its conditions. The tool path is adjusted according to the mirror compensation principle to ensure consistency between the machined and target holes. The reconstructed target hole surface satisfies the integrated error constraints and realizes the balanced constraints of positional and axial tolerances, making full use of both tolerances. Finally, the method's effectiveness has been verified in a large-scale work. Experimental results show that hole position error is reduced from uncompensated (1.03, − 0.51) mm to compensated (0.25, − 0.005) mm, and axial error from uncompensated 22.32 mm to compensated 1.39 mm. [ABSTRACT FROM AUTHOR]

This paper explores the effects of industrial robots and information and communication technology (ICT) on regional employment in European Union countries. The empirical analysis relies on a harmonised comprehensive regional dataset that combines business statistics and national and regional accounts data. This rich dataset enables us to provide detailed insights into the employment effects of automation and computerisation in EU regions for the period 2001–16. The results suggest relatively weak effects on regional total employment dynamics. However, industrial robots show negative employment effects in local manufacturing industries and positive employment effects in local non-manufacturing industries. While the negative effect is concentrated in particular local manufacturing industries, the positive effect has operated in local service industries. Information technology investments show positive employment effects in local manufacturing industries and some individual local service industries, while communication technology investments are shown to be irrelevant for employment dynamics. In contrast, software and database investments have had a predominantly negative association with local employment. [ABSTRACT FROM AUTHOR]

The present paper describes the measurement of the drift of unidirectional pose accuracy, repeatability, and static compliance of a collaborative robot employing a measurement methodology that relies on the description of a virtual ISO cube placed in the robot's workspace. The measurements aimed to investigate and assess the suitability of the UR5 six-axis collaborative robot for its application in robotic 3D printing. An experimental laboratory measurement workstation was constructed to perform the measurements, and control measurements were performed. The measurements involved describing the TCP point of the robot tool at five measurement points located in a virtual ISO cube during a minimum of 30 repeated measurement cycles. A camera and six linear incremental sensors with assessment units were used for the measurements. The measurements were performed in compliance with the regulations of STN ISO 9283 standard for this type of measurement. As a result of the measurements, the technical specifications of the drift and static compliance of the controlled robotic arm were verified, and the results were compared with the values specified by the manufacturer. Following the measurements and assessment of the results, it was possible to assess the suitability of the used UR5 robotic arm for its application in robotic 3D printing and to propose possible recommendations for the calibration of the robot and the process settings of the printing system for the production of objects using FDM technology. [ABSTRACT FROM AUTHOR]

Traditionally, in robotic surface finishing, the entire workpiece is processed at a uniform speed, predetermined by the operator, which does not account for variations in the machinability across different regions of the workpiece. This conventional approach often leads to inefficiencies, especially given the diverse geometrical characteristics of workpieces that could potentially allow for different machining speeds. Our study introduces a region-based approach, which improves surface finishing machining time by allowing variable speeds and directions tailored to each region's specific characteristics. This method leverages a task-oriented strategy integrating robot kinematics and workpiece surface geometry, subdivided by the clustering algorithm. Subsequently, methods for optimization algorithms were developed to calculate each region's optimal machining speeds and directions. The efficacy of this approach was validated through numerical results on two distinct workpieces, demonstrating significant improvements in machining times. The region-based approach yielded up to a 37% reduction in machining time compared to traditional single-direction machining. Further enhancements were achieved by optimizing the workpiece positioning, which, in our case, added up to an additional 16% improvement from the initial position. Validation processes were conducted to ensure the collaborative robot's joint velocities remained within safe operational limits while executing the region-based surface finishing strategy. [ABSTRACT FROM AUTHOR]

In the field of rigid parallel manipulators, the Delta parallel robot is one of the most popular choices in the industry due to its ability to adapt to a wide range of applications, particularly pick-and-place tasks. In this paper, the authors present novel designs of Delta-type continuum parallel manipulators with flexible bars, solving both their direct and inverse kinematics, as well as obtaining the associated workspace. The continuum parallel manipulators, unlike conventional robots, incorporate certain flexible elements, such as slender rods that make up the kinematic chains of the Delta manipulators proposed in this work. As a consequence of the flexibility of these rods, a purely translational movement will not be generated, since it is necessary to analyze the zones of the workspace where a parasitic motion related to the inclination of the moving platform compromises the task devised. In addition, an experimental prototype of the Keops-Delta continuum manipulator has been built, and several experimental tests have been carried out to validate the proposed theoretical model. [ABSTRACT FROM AUTHOR]

We use French data over the 1994–2013 period to study how imports of industrial robots affect firm-level outcomes. Guided by a simple model, we develop a novel empirical strategy to identify the causal effects of robot adoption. Our results suggest that, while demand shocks generate a positive correlation between robot imports and employment at the firm level, exogenous exposure to automation leads to job losses. We also find that robot exposure increases labour productivity and some evidence that it may raise the relative demand for high-skill professions. [ABSTRACT FROM AUTHOR]

The emergence of modern robotic technology and artificial intelligence (AI) enables a transformation in the textile sector. Manual fabric defect inspection is time-consuming, error-prone, and labor-intensive. This offers a great possibility for applying more AI-trained automated processes with safe human–robot interaction (HRI) to reduce risks of work accidents and occupational illnesses and enhance the environmental sustainability of the processes. In this experimental study, we developed, implemented, and tested a novel algorithm that detects fabric defects by utilizing enhanced deep convolutional neural networks (DCNNs). The proposed method integrates advanced DCNN architectures to automatically classify and detect 13 different types of fabric defects, such as double-ends, holes, broken ends, etc., ensuring high accuracy and efficiency in the inspection process. The dataset is created through augmentation techniques and a model is fine-tuned on a large dataset of annotated images using transfer learning approaches. The experiment was performed using an anthropomorphic robot that was programmed to move above the fabric. The camera attached to the robot detected defects in the fabric and triggered an alarm. A photoelectric sensor was installed on the conveyor belt and linked to the robot to notify it about an impending fabric. The CNN model architecture was enhanced to increase performance. Experimental findings show that the presented system can detect fabric defects with a 97.49% mean Average Precision (mAP). [ABSTRACT FROM AUTHOR]

In the engineering practice of performing robotic measurements with a 2D scanner, a major difficulty is to accurately determine the tool center point (TCP) quickly and easily since it does not exist as a physical object. The article proposes an algorithm for determining the coordinates of the TCP point. It relies on the possibility of using a conical element, such as a milling cutter of unknown geometry. So far, the algorithms found in the literature were based on a sphere of known size. The presented solution was simulated in RobotStudio 2019 software, while calculations were performed in Maple 14 software. In addition, the correctness of the presented solutions was verified on a real object, an IRB 2400 robot equipped with a Keyence 2D scanner. [ABSTRACT FROM AUTHOR]

This article deals with the design and creation of digital twins of robotic stations. A literature review of digital twins, robot programming methods and laser tracker applications is presented. This paper shows that the construction of digital twins is closely related to one of the most popular methods of robot programming, i.e., off-line programming. In the case of digital twins of robotic stations, modeling accuracy and two-way communication with the real station proved to be crucial. The article proposes a methodology for solving the basic problem of off-line robot programming, i.e., the limited accuracy of the representation of the station and the details. The algorithm of proceeding in the case when the station already exists and its digital model is built and the case when the digital model is first created and the real solution is built on its basis is shown. According to the developed methodology, a digital twin of a real robotic station was created and the possibilities arising from the use of virtual tools were shown. The developed digital twin has the ability to communicate with advanced Matlab 2021-type tools, uses cloud solutions and virtual and augmented reality for training, simulates physical phenomena and provides the ability to accurately program robots off-line. [ABSTRACT FROM AUTHOR]

The aim of this paper is to present examples and original solutions related to the application of the digital twin concept in designing safety systems for robotic stations. This paper includes a review of publications on robot safety systems and digital twins. Based on this review, it was concluded that further work in this area is justified. This paper demonstrates the use of a digital model of a robotic casting mold preparation station to design safety components for an industrial cell. A key element of this paper is the presentation of developed algorithms and their applications in building digital twins of existing robotic stations. By characterizing advanced safety systems used in robotic stations, an example of using a digital twin of a robotic station to create safety zones and so-called restricted zones for the robot was developed. As part of the research conducted, a real, comprehensive example of creating safety zones based on the robot's TCP paths was carried out. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI)—the field of computer science that designs machines to perform tasks that typically require human intelligence—has seen rapid advances in the development of foundation systems such as large language models. In the workplace, the adoption of AI technologies can result in a broad range of hazards and risks to workers, as illustrated by the recent growth in industrial robotics and algorithmic management. Sources of risk from deployment of AI technologies across society and in the workplace have led to numerous government and private sector guidelines that propose principles governing the design and use of trustworthy and ethical AI. As AI capabilities become integrated in devices, machines, and systems across industry sectors, employers, workers, and occupational safety and health practitioners will be challenged to manage AI risks to worker health, safety, and well‐being. Five risk management options are presented as ways to assure that only trustworthy and ethical AI enables workplace devices, machinery, and processes. AI technologies will play a significant role in the future of work. The occupational safety and health practice and research communities need to ensure that the promise of these new AI technologies results in benefit, not harm, to workers. [ABSTRACT FROM AUTHOR]

Facing the challenge of improving efficiency and stability in industrial processes, this study examines the impact of implementing advanced human-machine interface (HMI) systems, complemented by the internet of things (IoT) and artificial intelligence (AI). The integration of a PLC and HMI-controlled system has resulted in a 22.85% increase in efficiency, stabilizing production and reducing process variability. Tools such as PLCSIM and TIA PORTAL were crucial for validating control logic and programming. Additionally, the study explores the potential of AI and IoT to amplify these benefits, suggesting a significant advancement in automation that could transform operational efficiency and quality in related industries. These findings provide a relevant framework for companies looking to integrate emerging technologies into their operations, promoting continuous improvement and more informed management. [ABSTRACT FROM AUTHOR]

Robotics and automation are the key industries supporting Thailand's strategic initiatives toward Thailand 4.0. This paper presents the development of an industry roadmap guiding the future research and development of related technologies in Thailand. The roadmap project was recently conducted during 2021–2022. The strategic targets for the future development of robotics and automation in Thailand were set to increase productivity and reduce adoption cost for the short term (2022–2023); create market opportunities for AI and industrial IoT technologies with data platform for the medium term (2024–2026); and prepare the Industry 5.0 system supporting emerging applications of digital twins and metaverse for the long term (2027–2030). [ABSTRACT FROM AUTHOR]