Industrial Robot Applications Research Articles

Engineering the Future: Robotics in the Automotive Industry Explained

The study provides an overview of the potential applications of industrial robots in the automobile sector, which is now the market's most significant client. An overview of the use of robots in global industry is provided in the first section of the article, which also discusses the state of industrial robot utilization. The most popular uses of robots, particularly in the automotive sector, are covered later, along with developments and outlooks for the field's future. The essay concluded with a specific project overview regarding robotized screw tightening on the car seat production line, which served as a model task from actual industrial practice.The many uses of robots in the automobile industry are examined in this review, including material handling, welding, painting, assembly line automation, and quality control. It emphasizes how collaborative robots (cobots) enhance human-robot interaction and how advances in machine learning and artificial intelligence maximize robotic performance. Keywords: Industrial robot; robotics in automotive; production of passenger cars; transport and production; automated assembly line

The Impact of Industrial Robot Applications on Global Value Chains Division Position

This paper surveys the GVC division position across forty-five countries and fourteen manufacturing sub-sectors during the period from 1999 to 2018 to illustrate the impact of applications of industrial robots on the GVC position. The study proves that an increasing level of utilization of industrial robots significantly improves the GVC division position in the manufacturing sector. By automating repetitive and precision tasks, robots enhance efficiency and productivity, therefore allowing deeper specialization in specific stages of the manufacturing process. This technological progress allows for a more connected global production network and hence better integration into GVC. The study also evaluates the spillover effects across value chains and the moderating impact of industrial agglomeration on smart manufacturing.The empirical results show that the application of industrial robots enhances productivity and encourages innovation in related sectors, thereby driving downstream industry growth and promoting industry specialization.Despite the improvement in GVC division positions, robots also contribute to making global value chains more diversified and resilient. Flexible and adaptive manufacturing systems can adapt to disruptions, such as pandemics and geopolitical tensions, ensuring continuity in production and increasing stability and competitiveness in smart manufacturing systems.

Does the application of industrial robots reduce the intensity of CO2 emissions embodied in manufacturing exports?

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 CO2 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 comparison 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 intelligence 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.

Kinematic calibration of industrial robot using Bayesian modeling framework

Positioning accuracy of an end-effector is a crucial metric for evaluating industrial robot performance. Uncertainties in joint angles and joint backlash deviate actual angles from the designed nominal values to negate positioning accuracy. Most existing parameter identification methods overlook or not properly account for such uncertainties, leading to usually overconfident identification results. To this gap, the present study introduces a kinematic calibration methodology employing Bayesian parameter estimation to achieve identification of joint variables. New formulas based on data features of industrial robots for constructing the likelihood function are proposed, and model selection is applied to assess various likelihood functions for a tradeoff balance between complexity and accuracy. To evaluate the robustness of the proposed approach, identification of joint variables is conducted under different measurement noises. The position response of kinematic model is predicted based on the identified joint uncertainty information. The efficacy is verified through rigorous scrutiny involving both a numerical example and an engineering application. Results indicate that the proposed method exhibits satisfactory kinematic parameter identification accuracy and robustness. In addition, the uncertainty of parameters can be measured and the prediction of trajectory uncertainty intervals is realized simultaneously, which promotes the application of industrial robots in high-precision scenes.

Industrial robots and corporate risk-taking value

This paper empirically examines the impact of industrial robot application on corporate risk-taking value using data from Chinese A-share listed manufacturing companies, measured by the Sharpe ratio. The findings indicate that industrial robots significantly enhance corporate risk-taking value through information effects, innovation, and production optimisation. Heterogeneity tests reveal that firms with higher capital density, non-state ownership, lower financing constraints, and more government subsidies experience a more substantial increase in their risk-taking value. These insights contribute to understanding the evolution of enterprise risk decisions in industrial intelligence and provide theoretical references for policies promoting industrial development.

How artificial intelligence affects international industrial transfer ——Evidence from industrial robot application

The development of high technology represented by artificial intelligence is an essential driver of international industrial transfer. Using country-industry-level industrial robot data and value-added trade data from 2002-2018, this paper empirically examines the impact and mechanism of artificial intelligence on the international industrial transfer undertaken by a country (region). The study shows that the application of artificial intelligence can significantly enhance the international industrial transfer undertaken by a country (region). Further analysis shows that artificial intelligence positively affects international industry transfer through two channels: Improving the quality of labor force and promoting the level technological innovation. Further heterogeneity analysis shows that, first, from the economic development degree perspective, artificial intelligence's role in promoting developing economies is more substantial than that in developed economies. Second, the financial crisis has weakened the impact of artificial intelligence on international industrial transfer from the perspective of time division，especially in developing economies. Third, from the perspective of industry heterogeneity, artificial intelligence has significantly promoted the transfer of medium-low and medium-high technology industries. The research in this paper extends the analysis of the effect of artificial intelligence. It provides realistic insights for optimizing the strategic layout of the robotics industry, achieving industrial structure upgrading, optimizing resource allocation efficiency, and grasping the opportunities of global industrial chain restructuring.

Service robots in my workplace: effects of employee-service robot co-work experiences on psychological empowerment

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

Optimal robot task scheduling in cluttered environments considering mechanical advantage

Abstract In various industrial robotic applications, the effective traversal of a manipulator amidst obstacles and its ability to reach specific task-points are imperative for the execution of predefined tasks. In certain scenarios, the sequence in which the manipulator reaches these task-points significantly impacts the overall cycle time required for task completion. Moreover, some tasks necessitate significant force exertion at the end-effector. Therefore, establishing an optimal sequence for the task-points reached by the end-effector’s tip is crucial for enhancing robot performance, ensuring collision-free motion and maintaining high-force application at the end-effector’s tip. To maximize the manipulator’s manipulability, which serves as a performance index for assessing its force capability, we aim to establish an optimal collision-free task sequence considering higher mechanical advantage. Three optimization criteria are considered: the cycle time, collision avoidance and the manipulability index. Optimization is accomplished using a genetic algorithm coupled with the Bump-Surface concept for collision avoidance. The effectiveness of this approach is confirmed through simulation experiments conducted in 2D and 3D environments with obstacles employing both redundant and non-redundant robots.

Does the application of industrial robots promote export green transformation? Evidence from Chinese manufacturing enterprises

Green exports are key in propelling China's foreign trade toward high-quality development. Based on rigorous theoretical analysis, this article employs comprehensive data from the International Federation of Robotics (IFR) from 2006 to 2014, along with data from the China Customs and industrial enterprise databases, to evaluate the impact of industrial robot utilization on fostering the green transformation of exports. The results reveal that the application of industrial robots unequivocally contributes to advancing the eco-friendly transformation of exports. This conclusion withstands endogeneity and robustness tests. In particular, industrial robots directly promote the green transformation of exports and exert an indirect influence through technological and scale effects. Technological effects can be divided into technological progress and green production, while scale effects relate to enterprise size and industry competitiveness. In terms of heterogeneity, the promoting effect of industrial robot utilization on the green transformation of exports is more pronounced in foreign-funded enterprises, technology-intensive enterprises, and enterprises in the eastern region. To efficiently leverage policy effects and facilitate the green transformation of exports, this article suggests strengthening policy guidance, providing special funds, tax incentives, and rewards, enhancing enterprise research and development capabilities, and encouraging domestic and non-technology-intensive enterprises, and enterprises in the eastern region to utilize industrial robots.

Technological anxiety: Analysis of the impact of industrial intelligence on employment in China

Employment is the greatest livelihood. Whether the impact of industrial robotics technology materialized in machines on employment in the digital age is an “icing on the cake” or “adding fuel to the fire” needs further study. This study aims to analyze the impact of the installation and application of industrial robots on labor demand in the context of the Chinese economy. First, from the theoretical logic and the economic development law, this study gives the prior judgment and research hypothesis that industrial intelligence will increase jobs. Then, based on the panel data of 269 cities in China from 2006 to 2021, we use the two-way fixed effect model, dynamic threshold model, and two-stage intermediary effect model. The objective is to investigate the impact of industrial intelligence on enterprise labor demand and its path mechanism. Results show that the overall effect of industrial intelligence on the labor force with the installation density index of industrial robots as the proxy variable is the “creation effect”. In other words, advanced digital technology has created additional jobs, and the overall supply of employment in the labor market has increased. The conclusion is still valid after the endogeneity identification and robustness test. In addition, the positive effect has a nonlinear effect on the network scale. When the installation density of industrial robots exceeds a particular threshold value, the division of labor continues to deepen under the combined action of the production efficiency and compensation effects, which will cause enterprises to increase labor demand further. Further research showed that industrial intelligence can increase employment by promoting synergistic agglomeration and improving labor price distortions. This study concludes that in the digital China era, the introduction and installation of industrial robots by enterprises can affect the optimal allocation of the labor market. This phenomenon has essential experience and reference significance for guiding industrial digitalization and intelligent transformation and promoting the high-quality development of people’s livelihood.

Impact of population ageing on the application of industrial robots: Evidence from China

China is experiencing a rise in its ageing population alongside rapid advancements in industrial robotics. Using panel data from China’s industries (2006 to 2021), this study empirically examines the impact of population ageing on the application of industrial robots. The results show that population ageing significantly promotes industrial robot application. The impact of population ageing on the application of industrial robots varies by industry. The promotion effect is greater in low and medium-technology industries than that of high-tech industries. This effect became more evident after 2012. Industries with high state ownership exhibit stronger influence coefficients than those with lower state ownership. Mechanism analysis indicates that population ageing promotes industrial robot adoption through the labour cost substitution effect. These findings offer insights for government policies to promote sustainable ageing and upgrading the manufacturing sector through artificial intelligence represented by the application of robotics.

The application of industrial robots and changes in capital-labour income disparity in China

The technological revolution, particularly the rise of industrial robots and artificial intelligence, has significantly impacted global economic growth and income disparity. This study examines the impact of industrial robot applications on the capital-labour income disparity in China, a country with the highest number of industrial robot applications and a commitment to reducing income inequality. The findings indicate that industrial robots intensify the income disparity between capital and labour, particularly in economically underdeveloped regions characterized by less sophisticated industrial structures, low levels of human capital, underdeveloped physical capital, and inadequate market economies. The promotion of industrial structure sophistication, enhancement of human capital levels, deepening of physical capital, and advancement of market development can mitigate the adverse impact of industrial robot applications on the income disparity between capital and labour. The study underscores the necessity of institutional reforms and targeted policies across these dimensions to address the negative impacts of new technologies.

Automating efficiency: The impact of industrial robots on labor investment in China

This study examines the impact of industrial robot applications on labor investment efficiency. While previous studies have mainly focused on how industrial robots affect productivity, economic structures, and labor markets, their influence on labor investment efficiency has remained relatively underexplored. Using data from Chinese manufacturing firms, our results demonstrate that the adoption of industrial robots significantly improves labor investment efficiency. This improvement is mainly achieved by optimizing the human capital structure and enhancing capital deepening. We also find that this effect is more pronounced in non-state-owned enterprises, in environments with reduced information asymmetry, and in firms with low R&D investment and labor intensity. Overall, our results highlight the positive effect of industrial robot applications on the efficiency of corporate labor investments from a microeconomic perspective, providing important insights for labor investment decisions and resource allocation optimization.

Industrial robot applications and individual migration decision: evidence from households in China

The integration of industrial robot application (IRA) into various sectors has catalyzed significant transformations in the labor market, reshaping the dynamics of individual migration decision (IMD). As industries increasingly adopt automation, the implications for workforce distribution and migration patterns become critical areas of study. Based on the conditional logit model and using CMDS data, this paper explores the relationship between IRA and IMD, as well as the heterogeneity effect and influencing mechanism. The findings indicate that IRA reduces the probability of the floating population choosing the city, and the results are robust after considering endogeneity and further robustness test. The impact of IRA on IMD is heterogeneous. Relatively speaking, the floating population with high education levels, health, younger, unmarried, female, agricultural household registration, state-owned enterprises, and non-routine task work does not over-think the impact of IRA during the migration process. In addition, the floating population also considers geographical location, city size, marketization level, wage level, and environmental quality when making migration decisions. the mechanism test shows that IRA attracts or suppresses the floating population through factors such as wage levels, housing price level, difficulty in finding a job, etc., depending on the result of individual utility maximization. These insights reveal the complex interplay between technological advancements in robotics and labor migration, emphasizing the need for comprehensive policy frameworks to manage these transformations effectively.

CLIP feature-based randomized control using images and text for multiple tasks and robots

This study presents a control framework leveraging vision language models (VLMs) for multiple tasks and robots. Notably, existing control methods using VLMs have achieved high performance in various tasks and robots in the training environment. However, these methods incur high costs for learning control policies for tasks and robots other than those in the training environment. Considering the application of industrial and household robots, learning in novel environments where robots are introduced is challenging. To address this issue, we propose a control framework that does not require learning control policies. Our framework combines the vision-language CLIP model with a randomized control. CLIP computes the similarity between images and texts by embedding them in the feature space. This study employs CLIP to compute the similarity between camera images and text representing the target state. In our method, the robot is controlled by a randomized controller that simultaneously explores and increases the similarity gradients. Moreover, we fine-tune the CLIP to improve the performance of the proposed method. Consequently, we confirm the effectiveness of our approach through a multitask simulation and a real robot experiment using a two-wheeled robot and robot arm.

High precision control of industrial robot based on flexible joint response model

For industrial robots, their repetition accuracy is high, but their absolute accuracy and trajectory accuracy are low, which limits the application of industrial robots in more scenarios. Current solutions mainly address precision issues caused by static errors through adjustments in kinematic parameters. Approaches such as dynamic feedforward control, torque computation methods, intelligent control, dual encoder control, visual servoing, etc., are employed to enhance dynamic tracking accuracy, yet they commonly suffer from insufficient precision, poor robustness, high costs, and usability challenges. In response to the difficulty in reducing dynamic errors, this paper proposes a high-precision control technique based on compensating for flexible joint responses. By compensating for deviations in flexible joint responses, the trajectory precision at the end-effector of industrial robots is improved.

Unveiling the impact of industrial robots on consumption-based embodied carbon intensity: A global perspective

With the widespread application of industrial robots, exploring the patterns of consumption-based embodied carbon intensity is crucial for understanding the fairness of carbon emissions responsibility and supporting global sustainability efforts. Employing the dynamic panel Generalized Method of Moments estimation methodology to a dataset including 52 countries, this paper evaluates the influence of the applications of industrial robot on the consumption-based embodied carbon intensity and its corresponding mechanisms. The main findings reveal that: (i) the application of industrial robots significantly reduces consumption-based embodied carbon intensity. The result remains robust after undergoing a series of robustness checks. (ii) In countries with greater advantages in environmental technology and a higher abundance of natural resources, the application of industrial robots results in more noticeable environmental benefits. (iii) While the impact of industrial robots on consumption is complex, their implementation boosts human capital and reduces income inequality, thereby lowering carbon intensity. The findings highlight the importance of human capital and reduced inequality in promoting eco-friendly consumption behaviors. Furthermore, the adoption of industrial robots has driven green innovation, enhancing environmental quality from the supply side. The findings provide policy implications for fostering global low-carbon transformation using industrial intelligence approaches.

The impact of industrial robots on export stability

AbstractThe application of industrial robots has emerged as a significant influencing factor on international trade. Based on product export stability data from 49 major exporting countries and corresponding industrial robot application data between 2010 and 2019, this study investigates the impact and mechanisms of industrial robot application on export stability in these countries. The findings demonstrate that the application of industrial robots enhances export stability. The mechanism results indicate that the application of industrial robots principally impacts trade stability by lowering export costs and raising the technological complexity of export products. Moreover, this effect can be enhanced by increased financial development, an innovative environment, and the empowerment of women. This study provides valuable policy implications for advancing the application of industrial robots and improving export stability.

Novel View Synthesis with Neural Radiance Fields for Industrial Robot Applications

Abstract. Neural Radiance Fields (NeRFs) have become a rapidly growing research field with the potential to revolutionize typical photogrammetric workflows, such as those used for 3D scene reconstruction. As input, NeRFs require multi-view images with corresponding camera poses as well as the interior orientation. In the typical NeRF workflow, the camera poses and the interior orientation are estimated in advance with Structure from Motion (SfM). But the quality of the resulting novel views, which depends on different parameters such as the number and distribution of available images, the accuracy of the related camera poses and interior orientation, but also the reflection characteristics of the depicted scene, is difficult to predict. In addition, SfM is a time-consuming pre-processing step, and its robustness and quality strongly depend on the image content. Furthermore, the undefined scaling factor of SfM hinders subsequent steps in which metric information is required. In this paper, we evaluate the potential of NeRFs for industrial robot applications. To start with, we propose an alternative to SfM pre-processing: we capture the input images with a calibrated camera that is attached to the end effector of an industrial robot and determine accurate camera poses with metric scale based on the robot kinematics. We then investigate the quality of the novel views by comparing them to ground truth, and by computing an internal quality measure based on ensemble methods. For evaluation purposes, we acquire multiple datasets that pose challenges for reconstruction typical of industrial applications, like reflective objects, poor texture, and fine structures. We show that the robot-based pose determination reaches similar accuracy as SfM in non-demanding cases, while having clear advantages in more challenging scenarios. We also report results of the novel view quality for different NeRF approaches, showing that an additional online pose refinement may be disadvantageous. Finally, we present first results of applying the ensemble method to estimate the quality of the synthetic novel view in the absence of a ground truth.

Robot base position and spacecraft cabin angle optimization via homogeneous stiffness domain index with nonlinear stiffness characteristics

The use of mobile robots for machining large components has received considerable research interest for the application of industrial robots in the machinery manufacturing sector. However, the low structural stiffness of industrial robots can result in poor machining quality under the action of cutting forces. Therefore, this paper proposes a simultaneous optimization method the mobile robot base position and cabin angle using homogeneous stiffness domain (HSD) index for large spacecraft cabins. First, a nonlinear joint stiffness model that considers the gravity compensator mechanism is established to describe the stiffness characteristics of heavy-duty robots more accurately. Subsequently, a HSD index is proposed to evaluate the overall stiffness values and stiffness fluctuation for all robot postures in the machining program. An optimization model is then established based on the HSD under the constraints of machining accessibility, joint angle limitation and singularity. The optimal base position and cabin angle are determined simultaneously using the sparrow search algorithm. Finally, simulation and milling experiments are used to demonstrate that the optimization method proposed in this paper can effectively improve the machining quality.