Since 2013,China has been the world’s largest market for industrial robots.Despite the gradual maturity of the industrial robot system,the lagging R&D and backward technology level of industrial robots have led t...Since 2013,China has been the world’s largest market for industrial robots.Despite the gradual maturity of the industrial robot system,the lagging R&D and backward technology level of industrial robots have led to a strong dependence on the import of core components and key technologies,which to a certain extent has restricted the development and improvement of industrial robots.At present,the“neck problem”in the field of industrial robots in China is not only in the reducer,controller,and servo but also in the basic processing equipment,basic technology,and basic materials.In this paper,we propose measures to improve the“neck problem”of industrial robots to promote the high-quality development of industrial robots in China.展开更多
The rapid evolution of industrial robots from automation tools to intelligent systems marks a pivotal shift in manufacturing practices within the framework of Industry 4.0.Industrial robots,once limited to repetitive ...The rapid evolution of industrial robots from automation tools to intelligent systems marks a pivotal shift in manufacturing practices within the framework of Industry 4.0.Industrial robots,once limited to repetitive tasks on assembly lines,are now increasingly powered by advanced technologies such as Artificial Intelligence(AI),machine learning,and the Internet of Things(IoT),enabling them to perform complex,adaptive tasks in real-time.This paper explores the technological advancements that have transformed industrial robots,highlighting the integration of AI,smart sensors,and autonomous systems.Furthermore,it examines the implications of this paradigm shift for industries,human-robot collaboration,and the workforce.While intelligent robots promise greater efficiency,flexibility,and safety in manufacturing,challenges regarding implementation,economic impact,and ethical considerations remain significant.The paper concludes by looking at the future trends in robotics and their potential to reshape the global industrial landscape.展开更多
Carbon Fiber Reinforced Polymer(CFRP)and aluminum stacked are widely used in aircraft assemble thanks to the high strength-to-weight ratio.Riveting is an important joining technique of stacked structure and requires d...Carbon Fiber Reinforced Polymer(CFRP)and aluminum stacked are widely used in aircraft assemble thanks to the high strength-to-weight ratio.Riveting is an important joining technique of stacked structure and requires drilling and countersinking.Robotic machining systems are gradually used in the machining of holes due to their high flexibility.However,weakly rigid stacked structure and low-stiffness industrial robot system bring about complex and diverse countersinking depth errors,which significantly affects the fatigue life of components.In this paper,the influence mechanism of ultrasonic energy on the accuracy of robotic countersinking of stacked structure is investigated.Firstly,a workpiece deformation model is established with the thinwalled plate deformation theory,defined as static error.Then,the vibration of the industrial robot is calculated from the acceleration with the frequency domain integration,defined as dynamic error.The suppression of ultrasonic energy on the two kinds of errors were elucidated,respectively.Base on this,a depth compensation model of robotic ultrasonic countersinking is established.Finally,the feasibility of the accuracy compensation is experimentally verified,and the countersinking depth error can be controlled within±0.09 mm.展开更多
With the progress of Industry 4.0,collaborative robots(cobots) have become a key area of innovation.However,safety standards such as ISO/TS 15066 often lag behind rapid technological advances,failing to balance safety...With the progress of Industry 4.0,collaborative robots(cobots) have become a key area of innovation.However,safety standards such as ISO/TS 15066 often lag behind rapid technological advances,failing to balance safety and innovation.This paper analyzes the conflicts between standards and innovation of industrial cobots,including lag,rigidity,and safetyperformance trade-offs.It proposes flexible standards,regulatory sandboxes,and lifecycle safety approaches to align safety with technological progress.展开更多
This paper focuses on the problem of multi-station multi-robot spot welding task assignment,and proposes a deep reinforcement learning(DRL)framework,which is made up of a public graph attention network and independent...This paper focuses on the problem of multi-station multi-robot spot welding task assignment,and proposes a deep reinforcement learning(DRL)framework,which is made up of a public graph attention network and independent policy networks.The graph of welding spots distribution is encoded using the graph attention network.Independent policy networks with attention mechanism as a decoder can handle the encoded graph and decide to assign robots to different tasks.The policy network is used to convert the large scale welding spots allocation problem to multiple small scale singlerobot welding path planning problems,and the path planning problem is quickly solved through existing methods.Then,the model is trained through reinforcement learning.In addition,the task balancing method is used to allocate tasks to multiple stations.The proposed algorithm is compared with classical algorithms,and the results show that the algorithm based on DRL can produce higher quality solutions.展开更多
Fault detection in industrial robot drive systems is a critical aspect of ensuring operational reliability and efficiency.To address the challenge of balancing accuracy and robustness in existing fault detection metho...Fault detection in industrial robot drive systems is a critical aspect of ensuring operational reliability and efficiency.To address the challenge of balancing accuracy and robustness in existing fault detection methods,this paper proposes an enhanced fault detection method based on the unscented Kalman filter(UKF).A comprehensive mathematical model of the brushless DC motor drive system is developed to provide a theoretical foundation for the design of subsequent fault detection methods.The conventional UKF estimation process is detailed,and its limitations in balancing estimation accuracy and robustness are addressed by introducing a dynamic,time-varying boundary layer.To further enhance detection performance,the method incorporates residual analysis using improved z-score and signal-tonoise ratio(SNR)metrics.Numerical simulations under both fault-free and faulty conditions demonstrate that the proposed approach achieves lower root mean square error(RMSE)in fault-free scenarios and provides reliable fault detection.These results highlight the potential of the proposed method to enhance the reliability and robustness of fault detection in industrial robot drive systems.展开更多
Industrial robots are integral to modern manufacturing systems,enabling high precision,high throughput,and flexibility.However,errors in accuracy and repeatability,which arise from a variety of sources such as mechani...Industrial robots are integral to modern manufacturing systems,enabling high precision,high throughput,and flexibility.However,errors in accuracy and repeatability,which arise from a variety of sources such as mechanical wear,calibration issues,and environmental factors,can significantly impact the performance of industrial robots.This paper aims to explore the theoretical modeling of errors in industrial robot systems and propose compensation strategies to enhance their accuracy and repeatability.Key factors contributing to errors,such as kinematic,dynamic,and environmental influences,are discussed in detail.Additionally,the paper explores various compensation techniques,including geometric error compensation,dynamic compensation,and adaptive control approaches.Through the integration of error modeling and compensation methods,industrial robots can achieve improved performance,ensuring higher operational efficiency and product quality.The paper concludes by highlighting the challenges and future research directions for improving the accuracy and repeatability of industrial robots in practical applications.展开更多
The integration of 5G technology with cloud-based control systems in industrial robots holds significant promise for the future of industrial automation.With its ultra-low latency,high data transfer speeds,and massive...The integration of 5G technology with cloud-based control systems in industrial robots holds significant promise for the future of industrial automation.With its ultra-low latency,high data transfer speeds,and massive connectivity,5G is poised to revolutionize real-time communication and coordination in manufacturing environments.This paper explores the prospects and challenges of applying 5G technology in industrial robots,focusing on cloud-based control systems that enable scalable,flexible,and efficient operations.Key advantages of 5G,including improved communication speed,enhanced real-time control,scalability,and predictive maintenance capabilities,are discussed.However,the transition to 5G also presents challenges,such as network reliability,security concerns,integration with legacy systems,and high implementation costs.The paper also examines case studies in the automotive,electronics,and aerospace industries,providing real-world examples of 5G adoption in industrial automation.The conclusion highlights key insights and outlines potential research directions for overcoming existing barriers and fully realizing the potential of 5G technology in industrial robot control.展开更多
To address the challenges of insufficient visualization in the industrial robot assembly operation system and the limitation of visualizing only geometric attributes of physical properties,a method is proposed for con...To address the challenges of insufficient visualization in the industrial robot assembly operation system and the limitation of visualizing only geometric attributes of physical properties,a method is proposed for constructing an industrial robot assembly system based on virtual reality technology.Focusing on the shaft hole assembly,the mechanical characteristics of the industrial robot shaft hole assembly process are analyzed and a dynamic model is established for shaft hole assembly operations.The key elements of virtual assembly operations for industrial robots are summarized and a five-dimensional model is proposed for industrial robot virtual operations.Utilizing the Unity3D engine based on the 5-D model for industrial robot virtual operations,an industrial robot shaft hole assembly system is developed.This system enables virtual assembly operations,displays physical attributes,and provides valuable references for the research of virtual systems.展开更多
As a typical representative and main technicalmeans of advanced manufacturing technology,robotic technology plays an important role in raisingan enterpse’s engineering level, improving its prod-uct quality and produc...As a typical representative and main technicalmeans of advanced manufacturing technology,robotic technology plays an important role in raisingan enterpse’s engineering level, improving its prod-uct quality and productivity, and realizing civilizedproduction. Currently, there are nearly one millionrobots of various kinds, which are employed widelyin different fields of manufacturing industry. Robot-ics is now one of the high technologies, which arecompetitively developed by the developed coun-展开更多
The diffusion of industrial robot technology has coincided with increasing divergence in firms’market shares,potentially leading to enhanced market power and shifts in the distribution of factor income.This paper inv...The diffusion of industrial robot technology has coincided with increasing divergence in firms’market shares,potentially leading to enhanced market power and shifts in the distribution of factor income.This paper investigates the impact of industrial robot adoption on firms’labor income share and explores the underlying mechanisms,with particular attention to the rise of superstar firms.The findings suggest that,overall,the use of industrial robots contributes to an increase in labor’s income share,reflecting a generally favorable trend for labor’s position in primary income distribution.This effect,however,is markedly heterogeneous across different types of firms,regions,and industries.A significant concern is that robot adoption strengthens firms’relative market power within industries,fueling the emergence of superstar firms.These firms jointly influence labor income share through both a competition effect and a demonstration effect:the former is the main cause of declining labor shares,while the latter introduces a new channel through which labor’s share is further reduced.Although antitrust policies can help improve labor’s income share,they are not well-suited to curbing the market power expansion driven by industrial robot adoption.Thus,the concern over superstar firms’suppression of labor income remains.Amid the intensifying trend of“machines replacing humans”,this paper offers empirical insights into how to address the distributional implications brought about by the rise of superstar firms.展开更多
This paper provides a comparative sociological analysis of the application models for industrial robots in the automotive and electronics industries.The integration of robots in these two key sectors has been a signif...This paper provides a comparative sociological analysis of the application models for industrial robots in the automotive and electronics industries.The integration of robots in these two key sectors has been a significant milestone in the evolution of modern manufacturing,contributing to major shifts in production processes,labor markets,and organizational structures.Through a comprehensive review of literature and case studies,the paper identifies and contrasts the driving factors for robot adoption,the impact of automation on the workforce,and the sociocultural factors influencing these transitions.The automotive industry,characterized by high-volume production and cost-efficiency,and the electronics industry,known for precision and fast-paced production,present unique challenges and opportunities in robot integration.By examining these differences,the paper aims to offer insights into the broader social and economic implications of industrial robot deployment and its effect on industry dynamics and labor relations.The findings highlight not only the technological benefits but also the social challenges associated with automation in these industries.展开更多
Industrial robots,as the fundamental component for intelligent manufacturing,have attracted considerable attention from both academia and industry.Since its absolute positioning accuracy can suffer from collision,wear...Industrial robots,as the fundamental component for intelligent manufacturing,have attracted considerable attention from both academia and industry.Since its absolute positioning accuracy can suffer from collision,wear,elastic,or inelastic deformation during its operation,a data-driven calibration(DDC)model has become a trending technique.It utilizes abundant data to decrease the difficulty in building complex system models,making it an economic and efficient approach to robot calibration.This paper conducts a comprehensive survey of the state-of-the-art DDC models with the following six-fold efforts:a)Summarizing the DDC modeling methods;b)Categorizing the latest progress of DDC optimization algorithms;c)Investigating the publicly available datasets and several typical metrics;d)Evaluating several widely adopted DDC models to demonstrate their calibration performance;e)Introducing the applications of the current DDC models;f)Discussing the progressing trend of DDC models.This paper strives to present a systematic and thorough overview of the existing DDC models from modeling to kinematic parameter optimization,thereby providing some guidance for research in this field.展开更多
With the rapid development of the intelligent manufacturing industry,the demand for technical talents in industrial robot technology has become increasingly urgent.Focusing on the application value of virtual simulati...With the rapid development of the intelligent manufacturing industry,the demand for technical talents in industrial robot technology has become increasingly urgent.Focusing on the application value of virtual simulation technology in the practical teaching of industrial robot major and combining with the national vocational education reform policies,this paper explores the innovative role of virtual simulation technology in the practical teaching model from four dimensions:teaching cost optimization,safety improvement,scenario expansion,and evaluation innovation.The research shows that virtual simulation technology can effectively solve the“three highs and three difficulties”problems in traditional practical teaching,promote the in-depth development of the integration of industry and education,and provide strong support for cultivating high-quality technical and skilled talent in the field of industrial robots.展开更多
Industrial robot application(IRA)provides an opportunity for the low-carbon development of trade.This study focuses on the green revolution of manufacturing export trade,analyzes the mechanism by which IRA affects CO_...Industrial robot application(IRA)provides an opportunity for the low-carbon development of trade.This study focuses on the green revolution of manufacturing export trade,analyzes the mechanism by which IRA affects CO_(2) emissions embodied in manufacturing exports(CIE),and conducts an empirical test based on panel data from 37 countries from 2000 to 2019.This study found that first,IRA can significantly reduce CIE,but there is a U-shaped nexus between the two,which shows a rebound effect.Second,the heterogeneity test demonstrates that in com-parison to both the low-tech and high-tech sectors,IRA in the medium-tech industry can significantly reduce CIE;compared with the low-IRA sectors,the high-IRA sectors exhibit a more obvious reduction.In addition,IRA has a stronger effect on high-carbon-intensity areas.Third,the mechanism test shows that IRA mainly affects CIE through low-carbon technology and productivity effects.Moreover,environmental regulations and the manufacturing in-telligence process positively moderate the nexus between IRA and CIE.Finally,these conclusions provide possible empirical evidence for the smart evolution of the manufacturing industry and the green development of trade.展开更多
Based on an analysis of the relative shaft-to-hole position and attiude errors, as well as of the mechanics and Kinematics in the process of automatic assembly of industrial robots, the paper studies the principle of ...Based on an analysis of the relative shaft-to-hole position and attiude errors, as well as of the mechanics and Kinematics in the process of automatic assembly of industrial robots, the paper studies the principle of construction of dynamic wrists. Type I-3 and Ⅱ-6 dynamic compliant wrists have been designed and made. Prblems in the production of compliant elements and the connection between compliant elements and wrists were also solved. A study on the results of tests of the function of two kinds of dynamic compliant wrists shows that the dynamic compliant wrist's compliancy function can be improved by adding metallic materials having higher longitudinal and transverse rigidity into the softer elstomer. And the design Principle is proved to be feasible and practicable. It can be expected that the use of dynamic compliant wrist will greatly lower the technical requirements of the shaft-hole assembly and the requirements in the resetting accuracy.展开更多
The advancement of the intelligent manufacturing industry(IMI)represents the future direction for the world's manufactur-ing sector,offering a promising avenue to bolster national competitiveness and enhance indus...The advancement of the intelligent manufacturing industry(IMI)represents the future direction for the world's manufactur-ing sector,offering a promising avenue to bolster national competitiveness and enhance industrial manufacturing efficiency.In this study,we took the industrial robot industry(IRI)as a case study to elucidate the spatial distribution and interconnections of IMI from a geographical perspective,and the modified diamond model(DM)was used to analyze the influencing factors.Results show that:1)the spatial pattern of IRI with various investment attributes in different industrial chain links is generally similar,centered in the southeast.Key investment areas are in the east and south.The spatial distribution of China's IRI covers a multitude of provinces and obtains differ-ent scales of investment in different countries(regions).2)The spatial correlation between foreign investors and China's provincial-level administrative regions(PARs)forms a network,and the network of foreign-invested enterprises is more stable.Different countries(regions)have distinct location preferences in China,with significant spatial differences in correlation degrees.3)Overall,the interac-tion of these factors shapes the location decisions and correlation patterns of industrial robot enterprises.This study not only contributes to our theoretical knowledge of the industrial spatial structure and industrial economy but also offers valuable references and sugges-tions for national IMI planning and relevant industry investors.展开更多
The 6D pose estimation of objects is of great significance for the intelligent assembly and sorting of industrial parts.In the industrial robot production scenarios,the 6D pose estimation of industrial parts mainly fa...The 6D pose estimation of objects is of great significance for the intelligent assembly and sorting of industrial parts.In the industrial robot production scenarios,the 6D pose estimation of industrial parts mainly faces two challenges:one is the loss of information and interference caused by occlusion and stacking in the sorting scenario,the other is the difficulty of feature extraction due to the weak texture of industrial parts.To address the above problems,this paper proposes an attention-based pixel-level voting network for 6D pose estimation of weakly textured industrial parts,namely CB-PVNet.On the one hand,the voting scheme can predict the keypoints of affected pixels,which improves the accuracy of keypoint localization even in scenarios such as weak texture and partial occlusion.On the other hand,the attention mechanism can extract interesting features of the object while suppressing useless features of surroundings.Extensive comparative experiments were conducted on both public datasets(including LINEMOD,Occlusion LINEMOD and T-LESS datasets)and self-made datasets.The experimental results indicate that the proposed network CB-PVNet can achieve accuracy of ADD(-s)comparable to state-of-the-art using only RGB images while ensuring real-time performance.Additionally,we also conducted robot grasping experiments in the real world.The balance between accuracy and computational efficiency makes the method well-suited for applications in industrial automation.展开更多
A novel approach is proposed to detect the normal vector to product surface in real time for the robotic precision drilling system in aircraft component assembly, and the auto-normalization algorithm is presented base...A novel approach is proposed to detect the normal vector to product surface in real time for the robotic precision drilling system in aircraft component assembly, and the auto-normalization algorithm is presented based on the detection system. Firstly, the deviation between the normal vector and the spindle axis is measured by the four laser displacement sensors installed at the head of the multi-function end effector. Then, the robot target attitude is inversely solved according to the auto-normalization algorithm. Finally, adjust the robot to the target attitude via pitch and yaw rotations about the tool center point and the spindle axis is corrected in line with the normal vector simultaneously. To test and verify the auto-normalization algorithm, an experimental platform is established in which the laser tracker is introduced for accurate measurement. The results show that the deviations between the corrected spindle axis and the normal vector are all reduced to less than 0.5°, with the mean value 0.32°. It is demonstrated the detection method and the autonormalization algorithm are feasible and reliable.展开更多
Due to the characteristics of high efficiency,wide working range,and high flexibility,industrial robots are being increasingly used in the industries of automotive,machining,electrical and electronic,rubber and plasti...Due to the characteristics of high efficiency,wide working range,and high flexibility,industrial robots are being increasingly used in the industries of automotive,machining,electrical and electronic,rubber and plastics,aerospace,food,etc.Whereas the low positioning accuracy,resulted from the serial configuration of industrial robots,has limited their further developments and applications in the field of high requirements for machining accuracy,e.g.,aircraft assembly.In this paper,a neural-network-based approach is proposed to improve the robots’positioning accuracy.Firstly,the neural network,optimized by a genetic particle swarm algorithm,is constructed to model and predict the positioning errors of an industrial robot.Next,the predicted errors are utilized to realize the compensation of the target points at the robot’s workspace.Finally,a series of experiments of the KUKA KR 500–3 industrial robot with no-load and drilling scenarios are implemented to validate the proposed method.The experimental results show that the positioning errors of the robot are reduced from 1.529 mm to 0.344 mm and from 1.879 mm to 0.227 mm for the no-load and drilling conditions,respectively,which means that the position accuracy of the robot is increased by 77.6%and 87.9%for the two experimental conditions,respectively.展开更多
文摘Since 2013,China has been the world’s largest market for industrial robots.Despite the gradual maturity of the industrial robot system,the lagging R&D and backward technology level of industrial robots have led to a strong dependence on the import of core components and key technologies,which to a certain extent has restricted the development and improvement of industrial robots.At present,the“neck problem”in the field of industrial robots in China is not only in the reducer,controller,and servo but also in the basic processing equipment,basic technology,and basic materials.In this paper,we propose measures to improve the“neck problem”of industrial robots to promote the high-quality development of industrial robots in China.
文摘The rapid evolution of industrial robots from automation tools to intelligent systems marks a pivotal shift in manufacturing practices within the framework of Industry 4.0.Industrial robots,once limited to repetitive tasks on assembly lines,are now increasingly powered by advanced technologies such as Artificial Intelligence(AI),machine learning,and the Internet of Things(IoT),enabling them to perform complex,adaptive tasks in real-time.This paper explores the technological advancements that have transformed industrial robots,highlighting the integration of AI,smart sensors,and autonomous systems.Furthermore,it examines the implications of this paradigm shift for industries,human-robot collaboration,and the workforce.While intelligent robots promise greater efficiency,flexibility,and safety in manufacturing,challenges regarding implementation,economic impact,and ethical considerations remain significant.The paper concludes by looking at the future trends in robotics and their potential to reshape the global industrial landscape.
基金co-supported by the National Key Research and Development Program of China(No.2024YFB4711201)National Natural Science Foundation of China(Nos.U22A20204,52305472)。
文摘Carbon Fiber Reinforced Polymer(CFRP)and aluminum stacked are widely used in aircraft assemble thanks to the high strength-to-weight ratio.Riveting is an important joining technique of stacked structure and requires drilling and countersinking.Robotic machining systems are gradually used in the machining of holes due to their high flexibility.However,weakly rigid stacked structure and low-stiffness industrial robot system bring about complex and diverse countersinking depth errors,which significantly affects the fatigue life of components.In this paper,the influence mechanism of ultrasonic energy on the accuracy of robotic countersinking of stacked structure is investigated.Firstly,a workpiece deformation model is established with the thinwalled plate deformation theory,defined as static error.Then,the vibration of the industrial robot is calculated from the acceleration with the frequency domain integration,defined as dynamic error.The suppression of ultrasonic energy on the two kinds of errors were elucidated,respectively.Base on this,a depth compensation model of robotic ultrasonic countersinking is established.Finally,the feasibility of the accuracy compensation is experimentally verified,and the countersinking depth error can be controlled within±0.09 mm.
文摘With the progress of Industry 4.0,collaborative robots(cobots) have become a key area of innovation.However,safety standards such as ISO/TS 15066 often lag behind rapid technological advances,failing to balance safety and innovation.This paper analyzes the conflicts between standards and innovation of industrial cobots,including lag,rigidity,and safetyperformance trade-offs.It proposes flexible standards,regulatory sandboxes,and lifecycle safety approaches to align safety with technological progress.
基金National Key Research and Development Program of China,Grant/Award Number:2021YFB1714700Postdoctoral Research Foundation of China,Grant/Award Number:2024M752364Postdoctoral Fellowship Program of CPSF,Grant/Award Number:GZB20240525。
文摘This paper focuses on the problem of multi-station multi-robot spot welding task assignment,and proposes a deep reinforcement learning(DRL)framework,which is made up of a public graph attention network and independent policy networks.The graph of welding spots distribution is encoded using the graph attention network.Independent policy networks with attention mechanism as a decoder can handle the encoded graph and decide to assign robots to different tasks.The policy network is used to convert the large scale welding spots allocation problem to multiple small scale singlerobot welding path planning problems,and the path planning problem is quickly solved through existing methods.Then,the model is trained through reinforcement learning.In addition,the task balancing method is used to allocate tasks to multiple stations.The proposed algorithm is compared with classical algorithms,and the results show that the algorithm based on DRL can produce higher quality solutions.
基金Supported by the Natural Science Foundation of the Higher Education Institutions of Jiangsu Province(22KJB520012)the Research Project on Higher Education Reform in Jiangsu Province(2023JSJG781)the College Student Innovation and Entrepreneurship Training Program Project(202313571008Z)。
文摘Fault detection in industrial robot drive systems is a critical aspect of ensuring operational reliability and efficiency.To address the challenge of balancing accuracy and robustness in existing fault detection methods,this paper proposes an enhanced fault detection method based on the unscented Kalman filter(UKF).A comprehensive mathematical model of the brushless DC motor drive system is developed to provide a theoretical foundation for the design of subsequent fault detection methods.The conventional UKF estimation process is detailed,and its limitations in balancing estimation accuracy and robustness are addressed by introducing a dynamic,time-varying boundary layer.To further enhance detection performance,the method incorporates residual analysis using improved z-score and signal-tonoise ratio(SNR)metrics.Numerical simulations under both fault-free and faulty conditions demonstrate that the proposed approach achieves lower root mean square error(RMSE)in fault-free scenarios and provides reliable fault detection.These results highlight the potential of the proposed method to enhance the reliability and robustness of fault detection in industrial robot drive systems.
文摘Industrial robots are integral to modern manufacturing systems,enabling high precision,high throughput,and flexibility.However,errors in accuracy and repeatability,which arise from a variety of sources such as mechanical wear,calibration issues,and environmental factors,can significantly impact the performance of industrial robots.This paper aims to explore the theoretical modeling of errors in industrial robot systems and propose compensation strategies to enhance their accuracy and repeatability.Key factors contributing to errors,such as kinematic,dynamic,and environmental influences,are discussed in detail.Additionally,the paper explores various compensation techniques,including geometric error compensation,dynamic compensation,and adaptive control approaches.Through the integration of error modeling and compensation methods,industrial robots can achieve improved performance,ensuring higher operational efficiency and product quality.The paper concludes by highlighting the challenges and future research directions for improving the accuracy and repeatability of industrial robots in practical applications.
文摘The integration of 5G technology with cloud-based control systems in industrial robots holds significant promise for the future of industrial automation.With its ultra-low latency,high data transfer speeds,and massive connectivity,5G is poised to revolutionize real-time communication and coordination in manufacturing environments.This paper explores the prospects and challenges of applying 5G technology in industrial robots,focusing on cloud-based control systems that enable scalable,flexible,and efficient operations.Key advantages of 5G,including improved communication speed,enhanced real-time control,scalability,and predictive maintenance capabilities,are discussed.However,the transition to 5G also presents challenges,such as network reliability,security concerns,integration with legacy systems,and high implementation costs.The paper also examines case studies in the automotive,electronics,and aerospace industries,providing real-world examples of 5G adoption in industrial automation.The conclusion highlights key insights and outlines potential research directions for overcoming existing barriers and fully realizing the potential of 5G technology in industrial robot control.
基金Sponsored by the National Natural Science Foundation of China(Grant No.52005003)the Science and Technology Planning Project of Wuhu City(Grant No.2022jc41)。
文摘To address the challenges of insufficient visualization in the industrial robot assembly operation system and the limitation of visualizing only geometric attributes of physical properties,a method is proposed for constructing an industrial robot assembly system based on virtual reality technology.Focusing on the shaft hole assembly,the mechanical characteristics of the industrial robot shaft hole assembly process are analyzed and a dynamic model is established for shaft hole assembly operations.The key elements of virtual assembly operations for industrial robots are summarized and a five-dimensional model is proposed for industrial robot virtual operations.Utilizing the Unity3D engine based on the 5-D model for industrial robot virtual operations,an industrial robot shaft hole assembly system is developed.This system enables virtual assembly operations,displays physical attributes,and provides valuable references for the research of virtual systems.
文摘As a typical representative and main technicalmeans of advanced manufacturing technology,robotic technology plays an important role in raisingan enterpse’s engineering level, improving its prod-uct quality and productivity, and realizing civilizedproduction. Currently, there are nearly one millionrobots of various kinds, which are employed widelyin different fields of manufacturing industry. Robot-ics is now one of the high technologies, which arecompetitively developed by the developed coun-
基金supported by General Project of the National Social Science Fund of China(NSSFC),“Mechanisms and Strategies of Artificial Intelligence’s Impact on Inter-firm Wage Disparities”(Grant No.21BJY097).
文摘The diffusion of industrial robot technology has coincided with increasing divergence in firms’market shares,potentially leading to enhanced market power and shifts in the distribution of factor income.This paper investigates the impact of industrial robot adoption on firms’labor income share and explores the underlying mechanisms,with particular attention to the rise of superstar firms.The findings suggest that,overall,the use of industrial robots contributes to an increase in labor’s income share,reflecting a generally favorable trend for labor’s position in primary income distribution.This effect,however,is markedly heterogeneous across different types of firms,regions,and industries.A significant concern is that robot adoption strengthens firms’relative market power within industries,fueling the emergence of superstar firms.These firms jointly influence labor income share through both a competition effect and a demonstration effect:the former is the main cause of declining labor shares,while the latter introduces a new channel through which labor’s share is further reduced.Although antitrust policies can help improve labor’s income share,they are not well-suited to curbing the market power expansion driven by industrial robot adoption.Thus,the concern over superstar firms’suppression of labor income remains.Amid the intensifying trend of“machines replacing humans”,this paper offers empirical insights into how to address the distributional implications brought about by the rise of superstar firms.
文摘This paper provides a comparative sociological analysis of the application models for industrial robots in the automotive and electronics industries.The integration of robots in these two key sectors has been a significant milestone in the evolution of modern manufacturing,contributing to major shifts in production processes,labor markets,and organizational structures.Through a comprehensive review of literature and case studies,the paper identifies and contrasts the driving factors for robot adoption,the impact of automation on the workforce,and the sociocultural factors influencing these transitions.The automotive industry,characterized by high-volume production and cost-efficiency,and the electronics industry,known for precision and fast-paced production,present unique challenges and opportunities in robot integration.By examining these differences,the paper aims to offer insights into the broader social and economic implications of industrial robot deployment and its effect on industry dynamics and labor relations.The findings highlight not only the technological benefits but also the social challenges associated with automation in these industries.
基金supported in part by the National Key Research and Development Program of China(2024YFF0908200)the National Natural Science Foundation of China(62372385,62272078,62002337)the Chongqing Natural Science Foundation(CSTB2022 NSCQ-MSX1486,CSTB2023NSCQ-LZX0069).
文摘Industrial robots,as the fundamental component for intelligent manufacturing,have attracted considerable attention from both academia and industry.Since its absolute positioning accuracy can suffer from collision,wear,elastic,or inelastic deformation during its operation,a data-driven calibration(DDC)model has become a trending technique.It utilizes abundant data to decrease the difficulty in building complex system models,making it an economic and efficient approach to robot calibration.This paper conducts a comprehensive survey of the state-of-the-art DDC models with the following six-fold efforts:a)Summarizing the DDC modeling methods;b)Categorizing the latest progress of DDC optimization algorithms;c)Investigating the publicly available datasets and several typical metrics;d)Evaluating several widely adopted DDC models to demonstrate their calibration performance;e)Introducing the applications of the current DDC models;f)Discussing the progressing trend of DDC models.This paper strives to present a systematic and thorough overview of the existing DDC models from modeling to kinematic parameter optimization,thereby providing some guidance for research in this field.
文摘With the rapid development of the intelligent manufacturing industry,the demand for technical talents in industrial robot technology has become increasingly urgent.Focusing on the application value of virtual simulation technology in the practical teaching of industrial robot major and combining with the national vocational education reform policies,this paper explores the innovative role of virtual simulation technology in the practical teaching model from four dimensions:teaching cost optimization,safety improvement,scenario expansion,and evaluation innovation.The research shows that virtual simulation technology can effectively solve the“three highs and three difficulties”problems in traditional practical teaching,promote the in-depth development of the integration of industry and education,and provide strong support for cultivating high-quality technical and skilled talent in the field of industrial robots.
基金the National Social Science Foundation of China(Grant No.23FGLB024)Special Project on“Promoting High-Quality Development through the Integration of the Yangtze River Delta”of Shaoxing University(Grant No.2024CSJYB01)to provide fund for the study。
文摘Industrial robot application(IRA)provides an opportunity for the low-carbon development of trade.This study focuses on the green revolution of manufacturing export trade,analyzes the mechanism by which IRA affects CO_(2) emissions embodied in manufacturing exports(CIE),and conducts an empirical test based on panel data from 37 countries from 2000 to 2019.This study found that first,IRA can significantly reduce CIE,but there is a U-shaped nexus between the two,which shows a rebound effect.Second,the heterogeneity test demonstrates that in com-parison to both the low-tech and high-tech sectors,IRA in the medium-tech industry can significantly reduce CIE;compared with the low-IRA sectors,the high-IRA sectors exhibit a more obvious reduction.In addition,IRA has a stronger effect on high-carbon-intensity areas.Third,the mechanism test shows that IRA mainly affects CIE through low-carbon technology and productivity effects.Moreover,environmental regulations and the manufacturing in-telligence process positively moderate the nexus between IRA and CIE.Finally,these conclusions provide possible empirical evidence for the smart evolution of the manufacturing industry and the green development of trade.
文摘Based on an analysis of the relative shaft-to-hole position and attiude errors, as well as of the mechanics and Kinematics in the process of automatic assembly of industrial robots, the paper studies the principle of construction of dynamic wrists. Type I-3 and Ⅱ-6 dynamic compliant wrists have been designed and made. Prblems in the production of compliant elements and the connection between compliant elements and wrists were also solved. A study on the results of tests of the function of two kinds of dynamic compliant wrists shows that the dynamic compliant wrist's compliancy function can be improved by adding metallic materials having higher longitudinal and transverse rigidity into the softer elstomer. And the design Principle is proved to be feasible and practicable. It can be expected that the use of dynamic compliant wrist will greatly lower the technical requirements of the shaft-hole assembly and the requirements in the resetting accuracy.
基金Under the auspices of the Natural Science Foundation Project of Heilongjiang Province(No.LH2019D009)。
文摘The advancement of the intelligent manufacturing industry(IMI)represents the future direction for the world's manufactur-ing sector,offering a promising avenue to bolster national competitiveness and enhance industrial manufacturing efficiency.In this study,we took the industrial robot industry(IRI)as a case study to elucidate the spatial distribution and interconnections of IMI from a geographical perspective,and the modified diamond model(DM)was used to analyze the influencing factors.Results show that:1)the spatial pattern of IRI with various investment attributes in different industrial chain links is generally similar,centered in the southeast.Key investment areas are in the east and south.The spatial distribution of China's IRI covers a multitude of provinces and obtains differ-ent scales of investment in different countries(regions).2)The spatial correlation between foreign investors and China's provincial-level administrative regions(PARs)forms a network,and the network of foreign-invested enterprises is more stable.Different countries(regions)have distinct location preferences in China,with significant spatial differences in correlation degrees.3)Overall,the interac-tion of these factors shapes the location decisions and correlation patterns of industrial robot enterprises.This study not only contributes to our theoretical knowledge of the industrial spatial structure and industrial economy but also offers valuable references and sugges-tions for national IMI planning and relevant industry investors.
基金supported by the Knowledge Innovation Program of Wuhan-Shuguang Project(Grant No.2023010201020443)the School-Level Scientific Research Project Funding Program of Jianghan University(Grant No.2022XKZX33)the Natural Science Foundation of Hubei Province(Grant No.2024AFB466).
文摘The 6D pose estimation of objects is of great significance for the intelligent assembly and sorting of industrial parts.In the industrial robot production scenarios,the 6D pose estimation of industrial parts mainly faces two challenges:one is the loss of information and interference caused by occlusion and stacking in the sorting scenario,the other is the difficulty of feature extraction due to the weak texture of industrial parts.To address the above problems,this paper proposes an attention-based pixel-level voting network for 6D pose estimation of weakly textured industrial parts,namely CB-PVNet.On the one hand,the voting scheme can predict the keypoints of affected pixels,which improves the accuracy of keypoint localization even in scenarios such as weak texture and partial occlusion.On the other hand,the attention mechanism can extract interesting features of the object while suppressing useless features of surroundings.Extensive comparative experiments were conducted on both public datasets(including LINEMOD,Occlusion LINEMOD and T-LESS datasets)and self-made datasets.The experimental results indicate that the proposed network CB-PVNet can achieve accuracy of ADD(-s)comparable to state-of-the-art using only RGB images while ensuring real-time performance.Additionally,we also conducted robot grasping experiments in the real world.The balance between accuracy and computational efficiency makes the method well-suited for applications in industrial automation.
基金co-supported by Key Technology Research and Development Program of Jiangsu Province, China (No. BE2011178)the Aviation Industry Innovation Fund (No. AC2011214)
文摘A novel approach is proposed to detect the normal vector to product surface in real time for the robotic precision drilling system in aircraft component assembly, and the auto-normalization algorithm is presented based on the detection system. Firstly, the deviation between the normal vector and the spindle axis is measured by the four laser displacement sensors installed at the head of the multi-function end effector. Then, the robot target attitude is inversely solved according to the auto-normalization algorithm. Finally, adjust the robot to the target attitude via pitch and yaw rotations about the tool center point and the spindle axis is corrected in line with the normal vector simultaneously. To test and verify the auto-normalization algorithm, an experimental platform is established in which the laser tracker is introduced for accurate measurement. The results show that the deviations between the corrected spindle axis and the normal vector are all reduced to less than 0.5°, with the mean value 0.32°. It is demonstrated the detection method and the autonormalization algorithm are feasible and reliable.
基金co-supported by the Natural Science Foundation of Jiangsu Province(No.BK20190417)the National Natural Science Foundation of China(No.52005254)the National Key R&D Program of China(No.2018YFB1306800)。
文摘Due to the characteristics of high efficiency,wide working range,and high flexibility,industrial robots are being increasingly used in the industries of automotive,machining,electrical and electronic,rubber and plastics,aerospace,food,etc.Whereas the low positioning accuracy,resulted from the serial configuration of industrial robots,has limited their further developments and applications in the field of high requirements for machining accuracy,e.g.,aircraft assembly.In this paper,a neural-network-based approach is proposed to improve the robots’positioning accuracy.Firstly,the neural network,optimized by a genetic particle swarm algorithm,is constructed to model and predict the positioning errors of an industrial robot.Next,the predicted errors are utilized to realize the compensation of the target points at the robot’s workspace.Finally,a series of experiments of the KUKA KR 500–3 industrial robot with no-load and drilling scenarios are implemented to validate the proposed method.The experimental results show that the positioning errors of the robot are reduced from 1.529 mm to 0.344 mm and from 1.879 mm to 0.227 mm for the no-load and drilling conditions,respectively,which means that the position accuracy of the robot is increased by 77.6%and 87.9%for the two experimental conditions,respectively.
