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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]

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]

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]

Objectives. Musculoskeletal disorders (MSDs) represent a prevalent global occupational health concern, primarily associated with high biomechanical solicitations, mental workload and work pace. Although cobots have shown promise in reducing risks of MSDs, a question of interest still persists as to how the pace in hybrid human–machine collaboration will affect the operator, in terms of both physical and cognitive health and the production.Methods. This study aimed to analyse the impact of pace on productivity, operators’ posture and mental workload in a human–cobot collaboration. The study, involving 20 participants engaged in a collaborative task with a cobot under three cobot-led paces, assessed productivity rapid upper limb assessment (RULA) scores (posture), dual task performance (cognitive resources) and NASA task load index (NASA-TLX) scores (workload).Results. The findings revealed that an excessively high pace had counterproductive effects, leading to reduced efficiency and increased susceptibility to MSDs, both in terms of physical and mental workloads.Conclusion. In the context of a human–cobot collaboration, it is imperative to adapt the pace to operators’ abilities in order to ensure optimal productivity while preserving their health, emphasizing the need for a balanced approach to pace management in such collaborative work scenarios. [ABSTRACT FROM AUTHOR]

ABSTRACT A robust approximate constraint‐following control (RACC) approach is proposed in this article for collaborative robots with inequality constraints. The trajectory‐following control and boundary control of the robot are investigated. First, an explicit constraint equation for the collaborative robot system is established based on the Udwadia–Kalaba (U‐K) theory. Second, due to the monotone unbounded property of the tangent function, a special function is constructed to transform the joint output angles of the constrained robot into unconstrained state variables, and a new form of the robot constraint equation is obtained. Through this transformation, the joint motion of the robot will always be confined to specified angles and follow the desired trajectory. The constraint equation ensures the safety of the robot at the algorithmic level and innovatively solves the control problem of the equality and inequality of the robot's motion. According to theoretical analysis, the control approach can deal with uncertainty and satisfy both uniform boundedness (UB) and uniform ultimate boundedness (UUB) requirements. Finally, based on the rapid controller prototype CSPACE and a two‐degree‐of‐freedom collaborative robot platform, experimental verification is carried out. Numerical simulation and experimental results demonstrate that the proposed RACC approach with state transformation exhibits significant advantages in trajectory tracking performance and safety for collaborative robots. [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]

Critical global challenges, such as climate change and the insufficient availability of resources, mean that it is a pivotal time to make cities more intelligent, efficient, and sustainable in a drive towards a net-zero carbon future. This requires intelligent, interactive, and responsive structural health monitoring (SHM) to assure the longevity and safety of ageing infrastructure. Drones have the potential to revolutionise SHM. Drone-based SHM (as a potential fly-by technique) involves equipping drones with various sensors, or using inbuilt sensors, to capture data and images of structures from different angles and perspectives. The data is then processed and analysed to facilitate accurate assessment of the structure's health and early diagnosis of damage. Although the use of fly-by is relatively new, the speedy advances in various technologies that could be integrated with it, such as computer vision with artificial intelligence, deep learning, and links to digital twins, put these systems on the verge of a potential breakthrough. This paper provides an overview of fly-by SHM technique using both scientometric and qualitative systematic literature review processes, in order to provide a distinct understanding of the state of the art of research. As an original contribution, our research identified four main clusters of research within the field of fly-by SHM: (1) the application of UAV-enabled vision-based monitoring; (2) the integration of drones, advanced sensor technologies, and artificial intelligence; (3) drone-based SHM integrating modal analysis, energy harvesting, and deep learning; and (4) automation and robotics in drone-based SHM. The paper highlights the integration of new technologies such as artificial intelligence, machine learning, and sensors with the fly-by technique for SHM, identifies the gaps in current fly-by SHM research, and suggests new directions for research. [ABSTRACT FROM AUTHOR]

In the construction industry, where work environments are complex, unstructured and often dangerous, the implementation of human–robot collaboration (HRC) is emerging as a promising advancement. This underlines the critical need for intuitive communication interfaces that enable construction workers to collaborate seamlessly with robotic assistants. This study introduces a conversational virtual reality (VR) interface integrating multimodal interaction to enhance intuitive communication between construction workers and robots. By integrating voice and controller inputs with the robot operating system (ROS), building information modeling (BIM), and a game engine featuring a chat interface powered by a large language model (LLM), the proposed system enables intuitive and precise interaction within a VR setting. Evaluated by 12 construction workers through a drywall installation case study, the proposed system demonstrated its low workload and high intuitiveness and ease of use with succinct command inputs. The proposed multimodal interaction system suggests that such technological integration can substantially advance the integration of robotic assistants in the construction industry. [ABSTRACT FROM AUTHOR]

In recent years, providing additional visual feedback about the interaction forces has been found to offer benefits to haptic-assisted teleoperation. However, there is limited insight into the effects of the design of force feedback-related visual cues and the type of visual display on the performance of teleoperation of robotic arms executing industrial tasks. In this study, we provide new insights into this interaction by extending these findings to the haptic assistance teleoperation of a simulated robotic arm in a virtual environment, in which the haptic assistance is comprised of a set of virtual fixtures. We design a novel method for providing visual cues about the interaction forces to complement the haptic assistance and augment visual feedback in virtual reality with a head-mounted display. We evaluate the visual cues method and head-mounted display method through human factors experiments in a teleoperated dross removal use case. The results show that both methods are beneficial for task performance, each of them having stronger points in different aspects of the operation. The visual cues method was found to significantly improve safety in terms of peak collision force, whereas the head-mounted display additionally improves the performance significantly. Furthermore, positive scores of the subjective analysis indicate an increased user acceptance of both methods. This work provides a new study on the importance of visual feedback related to (interaction) forces and spatial information for haptic assistance and provides two methods to take advantage of its potential benefits in the teleoperation of robotic arms. [ABSTRACT FROM AUTHOR]

The paper presents a simulation investigation into the individual time of parcel transportation in a multi-agent robot operated warehouse for two different artificial intelligence planning strategies (independent and cooperative), two different warehouse setups and different number of robots operating at the same time. The robots are assumed to operate in a non-communicating manner, and their only physical interactions are the collision detection and avoidance maneuvers. It is additionally assumed, that every robot is aware of its location, is able to compute the shortest path to the required destination and has access to a global orders list from which orders can be reserved and removed. The warehouse setup and robots movement is simulated using a cellular automata paradigm, and the simulation is implemented in Python. It has been shown that the cooperation of robots passing packages to each other significantly improves the overall work time needed to complete the task and reduces the number of empty runs compared to an individual approach. The presented simulation results suggest it is possible to design a multi-robot delivery system in such a way, that robots do not communicate with each other and that no central planning or global optimization center is needed. The novel cooperative planning algorithm brings a contribution in engineering solutions, mainly for robot development in automated warehouses. [ABSTRACT FROM AUTHOR]

This study presents a comprehensive investigation of the impact and mechanism of industrial robot utilization on total factor productivity (TFP) using panel data from A‐share listed companies in China's manufacturing industry. Our findings reveal the significant contribution of industrial robots to enhancing TFP in microenterprises, with this influence persisting over time. Mechanism tests demonstrate that industrial robot utilization effectively reduces labor costs, mitigates financing constraints, and enhances innovation capabilities, thereby improving enterprises' TFP. Additionally, a heterogeneity analysis indicates a more pronounced impact of industrial robot utilization on TFP in labor‐intensive, non‐high‐tech enterprises, as well as in enterprises operating in highly competitive markets. This study deepens our understanding of industrial robot utilization and TFP, which broadens the scope of this field. This has significant practical implications for transforming traditional factors, integrating new technologies, and facilitating digital and intelligent transformations of manufacturing enterprises in emerging economies. [ABSTRACT FROM AUTHOR]

Purpose: Increasing personnel costs and labour shortages have pushed retailers to give increasing attention to their intralogistics operations. We study hybrid order picking systems, in which humans and robots share work time, workspace and objectives and are in permanent contact. This necessitates a collaboration of humans and their mechanical coworkers (cobots). Design/methodology/approach: Through a longitudinal case study on individual-level technology adaption, we accompanied a pilot testing of an industrial truck that automatically follows order pickers in their travel direction. Grounded on empirical field research and a unique large-scale data set comprising N = 2,086,260 storage location visits, where N = 57,239 storage location visits were performed in a hybrid setting and N = 2,029,021 in a manual setting, we applied a multilevel model to estimate the impact of this cobot settings on task performance. Findings: We show that cobot settings can reduce the time required for picking tasks by as much as 33.57%. Furthermore, practical factors such as product weight, pick density and travel distance mitigate this effect, suggesting that cobots are especially beneficial for short-distance orders. Originality/value: Given that the literature on hybrid order picking systems has primarily applied simulation approaches, the study is among the first to provide empirical evidence from a real-world setting. The results are discussed from the perspective of Industry 5.0 and can prevent managers from making investment decisions into ineffective robotic technology. [ABSTRACT FROM AUTHOR]

The industrial Internet of Things (IIoT) is prominently emerging in applications of large-scale and wide-area applications, such as oilfield management, smart grid management, real-time equipment monitoring, and integration of traffic management systems for smart cities. Relying on short-range wireless technologies (e.g., WirelessHART and ISA100.11a), traditional wireless solutions for industrial automation find it challenging to support the expansive scale of today's IIoT. To address this limitation, we propose to adopt LoRaWAN, a prominent low-power wide-area network technology, for industrial automation. LoRaWAN for industrial automation poses some unique challenges. The fundamental building blocks of any industrial automation system are feedback control loops that largely rely on real-time communication. LoRaWAN traditionally adopts a simple protocol based on ALOHA with no collision avoidance or Listen Before Talk with Clear Channel Assessment and Random Backoff mechanisms to minimize energy consumption, which are less suitable for real-time communication. Existing real-time protocols for short-range technologies cannot be applied to a LoRaWAN network due to its unique characteristics such as asymmetry between downlink and the uplink spectrum, predefined modes (or classes) of operation, and concurrent reception through orthogonal spreading factors. In this paper, we address these challenges and propose RTPL- a Real-Time communication Protocol for LoRaWAN networks. RTPL is a low-overhead and conflict-free communication protocol allowing autonomous real-time communication of low-energy devices and exploits LoRa's capability of parallel communication. We implement our approach on LoRa devices and evaluate through both physical experiments and extensive simulations. All results show that RTPL achieves on average 75% improvement in real-time performance without sacrificing throughput or energy compared to traditional LoRaWAN. [ABSTRACT FROM AUTHOR]

This paper aims to develop a miniature mobile robot suitable to assist archeologists in their first exploration of unknown underground tombs. Due to the rather complex and irregular terrains in the tombs and inspired by the classic RHex design, we have developed a two‐segment articulated robot (A‐RHex) with two RHex design units. The robot is compact and lightweight, with dimensions of 25 cm long, 6.5 cm wide, 7 cm high, and weighs 283 g. To assist the robot in entering the tomb, we have also designed a deployment platform that can take the robots underground through a 10‐cm exploration hole. We introduce the overall design, control, and communication methods of A‐RHex, and theoretically analyze how the articulated design can improve the stability of the robot on slopes. Laboratory experiments and field testings at two real archeological excavation sites in China have validated A‐RHex's mechanical design, control strategies, communications, and capabilities for pre‐exploration of open and closed tombs. We believe that this kind of robot with high terrain adaptability and a small profile may become an important tool for field archeology in the future. [ABSTRACT FROM AUTHOR]

With the increasing development of industry, the market demand for manufacturing has shifted to large‐scale customized production. This poses new challenges to the production flexibility of industrial robots. The offline programming method can perfectly meet this challenge. But its disadvantage is that it relies heavily on the absolute positioning accuracy of industrial robots. In recent years, there has been an increasing number of studies using neural networks (NN) to predict the positioning errors of industrial robots to improve their absolute positioning accuracy. However, most of these studies only focus on the application of NNs, and do not compare the prediction results and performance of different kinds of NNs. This paper selects three typical network models: backpropagation neural network (BPNN), particle swarm algorithm optimization BPNN (PSO‐BPNN), and radial basis function neural network (RBFNN). Through in‐depth experiments and analysis of these networks, the purpose is to reveal their respective prediction effects and characteristics and to summarize their advantages and disadvantages. Experimental results show that BPNN performs poorly in predicting positioning errors. As an optimization method, the particle swarm algorithm can effectively improve the prediction performance of BPNN. In contrast, the RBFNN performs well, which makes it very suitable for predicting the positioning error of industrial robots. [ABSTRACT FROM AUTHOR]

While there is much research in advanced additive manufacturing (AM) techniques using robotic manipulators, establishing a functional robotic framework can be costly and time-consuming. The lack of reliable sources to address the challenges faced during this process can pose a serious obstacle for researchers looking to start research in this field. This paper presents a solution aimed at streamlining the establishment of a robotic framework for the material extrusion process (such as fused deposition modeling or fused filament fabrication) to implement single- and multi-axis 3D printing, serving as a foundation for further research and development in the field of robotic AM (RAM). A volume decomposition approach is presented to produce supportless prints via multi-axis printing, which saves time and material. The effect of multi-axis printing on the printing time and material usage was experimentally investigated and discussed. Additionally, this paper introduces the concept of the "printability index," which can assist in efficiently positioning the build platform within the robot's workspace. The proposed methodology in this paper is not intended to be unique or optimal; however, it provides helpful insights into developing such frameworks. [ABSTRACT FROM AUTHOR]

In manufacturing, visual inspection of parts' surface is traditionally an important examination before the parts can proceed to the next manufacturing step. For example, the timely detection of minor surface defects, such as dents and scratches, in small-sized airfoils of aircraft engine, is typically the final stage of quality assurance before acceptance for assemblage. While such a process is critically important, current practices rely heavily on human operator's judgment, which is subjective and labor-intensive. In this study, we establish an automated, image-based inspection system that utilizes robotic automation to acquire high-resolution images of the parts under inspection and employs a specifically tailored machine learning technique to facilitate decision-making of inspection. Leveraging deep learning as the underlying methodology, we address a key challenge in the flexible automation of surface inspection, i.e., the scarcity of labeled data during the initial training process. In other words, we tackle the challenge of limited samples with known defects. Specifically, we synthesize an adaptive semi-supervised learning framework, building upon the residual neural network (ResNet) and the deep convolutional generative adversarial network (DCGAN) to extract features from both ground truth and synthetic data. This approach can overcome the shortcomings of the current approaches, leading to more objective and accurate defect detection right from the beginning of implementation with a small labeled dataset. Our results show that the overall classification accuracy on this challenging dataset reaches 92.30%, a 27.79% improvement over the baseline model achieved through optimal use of synthetic and ground truth data. The system also investigates the impact of synthetic data, providing guidelines for integrating it effectively into iterative training. This approach offers a robust solution for surface inspection and quality assurance in diverse manufacturing applications. [ABSTRACT FROM AUTHOR]

Stiffness modeling is an essential subject for the composition of robot control. Accurate stiffness modeling is helpful for improving the control accuracy of industrial robots, particularly under dynamic load circumstances. The classic virtual joint modeling (VJM) method is challenging in predicting the deformation of the end-effector throughout the full workspace due to the nonlinear deformation of the robot joint and its serial articulated structure. This paper proposes a full-space stiffness modeling method for robots based on the integration of a multi-layer perceptual (MLP) model and VJM. To provide enough training data for the MLP model, VJM is used to build a stiffness model with a small set of experimental data to generate 106,400 training data. A model-based transfer learning approach is proposed to improve the model's accuracy and generalization regarding the difference between generated training data and actual experimental data. The VJM stiffness model is compared with the MLP stiffness model and the existing CNN-based transfer learning model based on the same experimental data. Considering the deformation prediction in the three directions in Cartesian space, the mean absolute error, standard deviation, and maximum error of the MLP model are decreased by at least 24.90%, 14.20%, and 8.50%, respectively, than the VJM. These prediction results demonstrate that the proposed modeling technique can significantly increase the accuracy of robot stiffness modeling, which is essential for position compensation in precise motion control of robots under dynamic load. [ABSTRACT FROM AUTHOR]

Robotics technology transforms industries by driving sustainable development, improving healthcare delivery, decreasing inequality, and promoting economic growth. Through innovation and automation, robotics fosters inclusivity and efficiency, empowering underserved communities while tackling global challenges in line with the United Nations' sustainable development goals (SDGs). Despite extensive research on robotics' impacts on sustainable development goals (SDGs), there is limited focus on how these technologies influence Healthcare, innovation, inequality, and economic growth, particularly in developing nations. This study uses a systematic literature review to enhance understanding of robotics as a catalyst for sustainable development. Findings show that SDGs' economic and Healthcare aspects are well-studied, representing 17% of the literature, with innovation at 18.9%, inequality at 9.4%, and SDG 8 at 3.8%. Other SDG aspects account for 34% of the research. The literature emphasizes the role of automation and robotics in achieving ecologically and economically aligned SDGs, particularly in sustainable Healthcare, where robotics can improve medication management in home care. However, many areas in robotics applications remain underexplored, indicating a need for further research to bridge existing gaps. There are untapped opportunities to leverage new tools to accelerate the achievement of global SDGs, particularly for deepening and expanding current efforts. SDG 8 remains under investigated. This study proposes a conceptual framework for sustainable technologies to advance SDG achievement. The findings underscore the critical role of robotics in advancing SDGs, especially in innovation and Healthcare, though highlighting gaps in economic growth and inequality research. Future research should focus on expanding robotics applications, particularly for underexplored areas such as SDG 8, to fully utilise their potential for sustainable development. Highlights: The study aims to improve understanding of robotics as a catalyst for sustainable development. The role of automation and robotics in achieving ecologically and economically aligned SDGs was highlighted. There are untapped opportunities to leverage new tools to accelerate the achievement of global SDGs. [ABSTRACT FROM AUTHOR]

This study presents a novel algorithm for protocol reverse analysis of EtherCAT. The algorithm combines sequence alignment and the Pearson correlation coefficient. We utilize value distribution statistics and the bit flip rate algorithm to effectively partition the protocol fields. We propose a semantics analysis method based on sequence alignment when HMI data and EtherCAT messages have a direct correlation. Conversely, for circumstances where there exists a decoding relationship between HMI data and EtherCAT messages, a semantic analysis method is proposed that employs the Pearson correlation coefficient. We completed a reverse analysis of the EtherCAT messages from an industrial robot system. By comparing the experiment results with the protocol description document, we validated the effectiveness of the method. [ABSTRACT FROM AUTHOR]

Short-range imaging radar, with its all-day and all-weather perception capabilities, has gained considerable attention in emerging fields such as autonomous vehicle sensing and industrial robotic perception. However, compared to traditional imaging radar, short-range imaging radar systems face more stringent constraints in terms of physical sampling resources, particularly the number of sampling channels and the resulting aperture size. These limitations lead to reduced resolution and a lower signal-to-noise ratio, ultimately degrading the imaging quality and making it difficult to interpret. To address these challenges, we explore coprime sampling as a strategy to achieve high-quality short-range radar imaging using limited physical sampling resources. Our approach is built upon three core perspectives: (1) physical sampling: we adopt a coprime pattern to form an extended sampling aperture with a structured layout, enabling effective utilization of limited channels and minimizing aperture loss; (2) signal measurement: we utilize the second-order statistics of the measured data to generate additional equivalent measurements, thus enhancing the system's capability to capture diverse spatial information; and (3) scene reconstruction: we establish a novel forward measurement model, linking these equivalent measurements to the scene, and then formulate a sparsity-regularized optimization problem. We design a background-texture-preserving, target-enhanced resolving method based on the first-order proximal gradient algorithm to achieve robust and high-quality imaging results. Our method is verified on several measured data. The results show that our proposed approach achieves high-quality imaging while utilizing approximately half of the typical sampling resources. This study not only validates the effectiveness of coprime sampling for short-range radar imaging but also highlights its potential to alleviate sampling constraints in various resource-constrained applications. [ABSTRACT FROM AUTHOR]

In the current technological landscape, Multi-Robot Systems (MRS) have become crucial for complex tasks, with applications in industrial automation, search and rescue, and intelligent transportation. However, existing techniques face challenges in path planning and task allocation, particularly regarding adaptability, real-time decision-making, and efficiency. Deep Reinforcement Learning (DRL) has emerged as a promising solution due to its robust learning capabilities. To address these challenges, we propose an innovative DRL-MPC-GNNs model that integrates Deep Reinforcement Learning, Model Predictive Control (MPC), and Graph Neural Networks (GNNs). Our model aims to optimize path planning and task allocation in multi-robot systems. Through rigorous experiments in simulated environments, we validated our model's effectiveness, demonstrating significant improvements in path planning precision, task allocation efficiency, and inter-robot collaboration performance. These results highlight our model's practicality and offer new insights for future research and applications in multi-robot systems. Overall, our integrated model addresses key issues in multi-robot collaboration, contributing an innovative solution to the field's development. This research provides a novel approach for path planning and task allocation in multi-robot systems, laying a solid foundation for deploying intelligent robotic systems in complex and dynamic environments. [ABSTRACT FROM AUTHOR]

Against the background of China's economic transformation, it is of great practical significance to explore the impact of artificial intelligence on enterprise innovation to promote innovation-driven development strategies. Using patent data from Chinese industrial enterprises and robot data provided by the International Federation of Robotics, this study empirically tests the impact of artificial intelligence on improving the innovation abilities of Chinese enterprises. The study finds the following: (1) Artificial intelligence significantly improves enterprise innovation, and this conclusion remains valid after robustness tests. (2) Artificial intelligence optimizes the skill structure of the enterprise labour force, increases enterprise R&D expenditure, and strengthens the technology spillover effect, thus improving enterprise innovation. (3) The domestic market and the development of the Internet have further strengthened the role of artificial intelligence in promoting enterprise innovation. (4) Artificial intelligence is more helpful in promoting the innovation ability of technology-intensive, general trading, mixed trading, and non-state-owned enterprises. This study provides important policy implications for promoting the deep integration of artificial intelligence and real economy and realizing high-quality economic development. [ABSTRACT FROM AUTHOR]

In industrial automation systems, motors are crucial in driving mechanisms for speed or positioning control tasks. The widespread adoption of AC motors has been driven by their advantages such as low cost, solid structure, and easy maintenance. AC motors have gradually supplanted traditional DC motors with brushes and commutators and have become the predominant choice for motor and drive applications in industrial settings. In this paper, we introduce a hybrid control scheme that combines a sliding mode controller (SMC) and time delay estimation to enhance the robustness of speed control for permanent magnet synchronous motors (PMSM). Following the field-oriented principle, a flux SMC is initially designed to meet stator flux control requirements promptly. Subsequently, a speed controller is introduced, employing SMC with time delay estimation to address challenges such as torque and flux ripples, ultimately enhancing the overall robustness of the control system. Simulation results validate the effectiveness of the proposed control scheme under conditions of load disturbance and parameter uncertainties. Future work will involve implementing the proposed approach on digital signal processors to validate its performance and practicality in real-world applications. [ABSTRACT FROM AUTHOR]

The editorial discusses the application of Artificial Intelligence (AI) technologies in collaborative and service robots. It highlights the definition and challenges of service and collaborative robots, emphasizing the importance of human wellness in robot interactions. The article also provides an overview of recent studies focusing on optimizing unmanned aerial vehicles, improving collision avoidance methods, simplifying robot programming, and enhancing robot learning capabilities. These advancements aim to make robot behavior adaptable to changes in production processes, particularly beneficial for small and medium-sized enterprises. [Extracted from the article]

With the growing demand for industrial robots in the aerospace manufacturing process, the lack of positioning accuracy has become a critical factor limiting their broad application in precision manufacturing. To enhance robot positioning accuracy, one crucial approach is to analyze the distribution patterns of robot errors and leverage spatial similarity for error prediction and compensation. However, existing methods in Cartesian space struggle to achieve accurate error estimation when the robot is loaded or the end-effector orientations are varied. To address these challenges, a novel method for robot error prediction and accuracy compensation within configuration space is proposed. The analysis of robot error distribution reveals that the spatial similarity of robot errors is more pronounced and stable in configuration space compared to Cartesian space, and this property exhibits significant anisotropy across joint dimensions. A spatial-interpolation-based unbiased estimation method with joint weights optimization is proposed for robot errors prediction, and the particle filter method is utilized to search for the optimal joint weights, enhancing the anisotropic characteristics of the prediction model. Based on the robot error prediction model, a cyclic searching method is employed to directly compensate for the joint angles. An experimental system is established using an industrial robot equipped with a 120 kg end-effector and a laser tracker. Eighty sampling points with diverse poses are randomly selected within the task workspace to measure the robot errors before and after compensation. The proposed method achieves an error prediction accuracy of 0.172 mm, reducing the robot error from the original 4.96 mm to 0.28 mm, thus meeting the stringent accuracy requirements for hole machining in robotic aerospace assembly processes. [ABSTRACT FROM AUTHOR]