Industrial Robot Applications Research Articles

Impact of industrial robot on labour productivity: Empirical study based on industry panel data

This empirical analysis explores the impact of industrial robots on labour productivity using panel data of 17 Chinese industries from 2006 to 2021. The results reveal that the development of industrial robots significantly improves labour productivity; a series of robustness tests validate this outcome. However, the impact of industrial robots on labour productivity varies across industry types. The influence coefficient of the low-density robotics industry is larger than that of the high-density robotics industry. Furthermore, although the scale of industrial robot usage before 2012 was smaller than that after 2012, its effect on labour productivity was more significant. Our findings indicate the possibility of diminishing marginal effect of industrial robots in promoting labour productivity. The mechanism analysis demonstrates that human capital level has a complete intermediating effect between industrial robots and labour productivity. Thus, industrial robot applications can contribute to labour productivity by optimising human capital structure. These findings provide crucial insights for governments and policy makers to improve labour productivity and economic growth.

The impact of peer effect of industrial robot application on enterprise carbon emission reduction.

The application of intelligent technology, such as industrial robots, is related to the environmental governance effectiveness of enterprises and the realization of the goal of "carbon peak and carbon neutrality". Due to their similar external environments, driven by economic rationality, peer enterprises will mimic the robotics applications of other enterprises, which in turn will affect the enterprises' carbon emissions. However, little literature has explored the impact of industrial robot application on enterprise carbon emissions from the perspective of peer effect. Based on the data of Shanghai and Shenzhen A-share manufacturing listed enterprises in China from 2011 to 2021, this paper explores the impact of industrial robot application on carbon emission reduction of manufacturing enterprises from the perspective of peer effect. It is found that the industry peer effect and regional peer effect brought by the application of industrial robots are conducive to promoting the carbon emission reduction of enterprises. Among them, the industry peer effect of industrial robot applications promotes carbon emission reduction by enhancing the green innovation ability of enterprises, while the regional peer effect promotes carbon emission reduction by improving the service level of enterprises. It is further found that the degree of industry competition and the level of environmental regulation have inverted U-shaped moderating effects on the industrial robot application industry peer effect, regional peer effect, and enterprises' carbon emission reduction, respectively. The results enrich the research on the impact of industrial robot application on carbon emission reduction of manufacturing enterprises and provide policy implications for improving the environmental performance of enterprises.

Research Status and Development Trend of Multi-arm Collaborative Robots

Abstract: Industrial robots are mainly used in metal forming, automotive, and electrical and electronics industries. After decades of unremitting efforts, industrial robots have achieved great success. However, problems such as low safety, weak environmental adaptability, and insufficient humancomputer interaction ability of industrial robots have greatly limited the application of industrial robots, and multi-arm collaborative robots came into being in this case. Multi-arm collaborative robots offer high flexibility and precision. Over the years, the application and development of multiarm collaborative robots have been paid more and more attention. This paper aims to summarize the patents related to multi-arm cooperative robots, including their principles, classification, latest progress and future development, in order to meet the needs of the real world. According to the structural characteristics of multi-arm collaborative robots and the requirements of multi-arm collaborative robots application field, the most typical mobile collaborative robots and stationary dual-arm collaborative robots in multi-arm collaborative robots are summarized. By investigating various patents of multi-arm collaborative robots the main problems of multi-arm collaborative robots are summarized and analyzed, such as collisions or conflicts between robotic arms, low power-to-weight ratio, loose structures, and heavy weight of the robot body. In addition, the development trend of multi-arm collaborative robots is also discussed. Multi-arm robots have broad prospects and potential in future applications and development. With the advancement of new technologies, multi-arm collaborative robots will become more efficient, intelligent, safe, and flexible. More relevant patents are expected to be invented in the future.

Towards a high-energy efficiency world: Assessing the impact of artificial intelligence on urban energy efficiency

Global warming has increased the frequency of natural disasters, making enhancing energy efficiency an inevitable choice to address the worsening global climate risks. Whether industrial robots, as a crucial component of artificial intelligence, can contribute to improving energy efficiency remains a subject of debate. This study employs the SBM and super-efficiency SBM models to assess the energy efficiency of 262 Chinese cities from 2010 to 2020. We investigate the impact and mechanisms of industrial robots at the city level on energy efficiency. The research findings indicate that the application of industrial robots promotes enhanced energy efficiency, which remains robust after subjecting it to a battery of tests, including SYS-GMM and instrumental variable analyses. However, a significant nonlinear relationship exists between industrial robot utilization and energy efficiency, with diminishing marginal effects. Mechanism analysis reveals that the fundamental mechanisms facilitating this improvement are the digital economy, industrial structural adjustments, and innovations in green technology. Heterogeneity analysis suggests that industrial robots have a more pronounced impact on cities with higher energy efficiency, greater manufacturing levels in resource-dependent areas, and lower population densities. Furthermore, the application of industrial robots can influence the energy efficiency of neighboring regions through spatial spillover effects.

Composing MPC With LQR and Neural Network for Amortized Efficiency and Stable Control

Model predictive control (MPC) is a powerful control method that handles dynamical systems with constraints. However, solving MPC iteratively in real time, i.e., implicit MPC, remains a computational challenge. To address this, common solutions include explicit MPC and function approximation. Both methods, whenever applicable, may improve the computational efficiency of the implicit MPC by several orders of magnitude. Nevertheless, explicit MPC often requires expensive pre-computation and does not easily apply to higher-dimensional problems. Meanwhile, function approximation, although scales better with dimension, still requires pre-training on a large dataset and generally cannot guarantee to find an accurate surrogate policy, the failure of which often leads to closed-loop instability. To address these issues, we propose a triple-mode hybrid control scheme, named Memory-Augmented MPC, by combining a linear quadratic regulator, a neural network, and an MPC. From its standard form, we derive two variants of such hybrid control scheme: one customized for chaotic systems and the other for slow systems. The proposed scheme does not require pre-computation and is capable of improving the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">amortized</i> running time of the composed MPC with a well-trained neural network. In addition, the scheme maintains closed-loop stability with <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">any</i> neural networks of proper input and output dimensions, alleviating the need for certifying optimality of the neural network in safety-critical applications. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —This article was motivated by the need to reduce the amortized cost of MPC in repetitive industrial robotic applications, where long-term operational cost is important and safety is critical. Examples of such applications include factory robotic arm manipulation and fixed-route quadcopter payload transport. Unlike explicit MPC or function approximation, our approach does not require any pre-computation or pre-training. Rather, it attains task proficiency over time by learning a surrogate neural network on the spot and by gradually replacing the costly MPC with the more efficient surrogate model so long as safety permits. Consequently, the proposed scheme incurs a learning cost during the initial phase of the deployment but usually becomes more adept on the task afterwards, leading to amortized efficiency.

A general energy modeling network for serial industrial robots integrating physical mechanism priors

Industrial robots (IRs), as the core equipment of intelligent manufacturing, play increasingly important roles in various industrial scenarios such as assembly, welding, handling, and spraying, significantly improving production efficiency and product quality. The massive popularization and application of IRs have brought about a sharp increase in energy consumption (EC), and the modeling and optimization of EC is becoming imperative. In this paper, a general energy modeling network DM-PLM for serial IRs based on Dynamic Model (DM) and Power Loss Model (PLM) is proposed by integrating the prior knowledge of IR power composition and dynamic mechanism, enabling efficient and accurate modeling of dynamics, power, and EC under multi-load conditions. Considering the force transmission characteristics of serial robots, this paper proposes an improved bidirectional recurrent neural network (BiRNN) to model the joint dynamics. Additionally, a power loss model based on the ResNet convolutional neural network is employed. Experiments are carried out with a KUKA KR210 heavy-duty robot and a UR5 collaborative robot. The results show that the DM-PLM model incorporating the physical mechanism priors achieves 97 %, 98 %, and 99 % modeling accuracy in joint torques, total power, and EC for both robots under multi-load conditions. In addition, the proposed DM-PLM model is applied to the EC optimization of KUKA KR210 through trajectory planning, which achieves over 30 % EC reduction with the genetic algorithm, providing an effective approach to improving the energy efficiency of serial IRs.

The nexus between industrial robot and employment in China: The effects of technology substitution and technology creation

The widespread application of industrial robots has raised our concerns about employment. Based on the data of Chinese micro-enterprises from 2000 to 2019, we use staggered and continuous DID method to measure the impact of industrial robots on the employment in the enterprise level in China. The result of the analysis shows that, under the combined effects of technology substitution and creation, the use of industrial robots has a significant positive impact on the employment of Chinese enterprises. The substitution effect is mainly in medium skilled labor, while the creation effect happens in high skilled and machine auxiliary positions. The analysis of heterogeneity of industry penetration rate of robot shows that, the creation effect in high skill positions and the substitution effect in low and medium skill positions mostly happen in high penetration rate industries, while the substitution effect in high skill positions and the creation effect in low and medium skill positions mostly happen in low and medium penetration rate industries. This result reveals the internal mechanism of the adjustment of employment structure caused by new technology. Based on this, the relevant suggestions are put forward from unemployment security system, corporate social responsibility, training of technical personnel, etc.

The Application of Industrial Robots in Automated Production Lines

Elaborate on the definition, structure, and characteristics of industrial robots, their application in automated production lines, and provide suggestions for balancing and optimizing industrial robots in automated production lines.

Influencing Factors as well as Implementation Path of Synergistic Development of Digitalization and Greening in Manufacturing Industry: Analysis from a Configuration Perspective

&lt;p class="MsoNormal" style="text-align: justify;"&gt;&lt;span lang="EN-US" style="font-family: 'times new roman', times, serif; font-size: 14pt;"&gt;The manufacturing sector must incorporate digitalization and greening as crucial strategies to attain high level of development in the context of the digital economy, given its significant role in quality of environment. Most of existing studies explore net effect of a single or several factors on the digitalization or greening in the manufacturing industry. Limited investigation has been done to integrate various factors impacting the connection and synchronized advancement of digitalization and greening within the manufacturing. Factors affecting the synergy of digitalization and green change within manufacturing are recognized in this article, and a theoretical framework is established to analyze. FsQCA tool is also put into use to survey configurational cases, which contains synergy degree of green digitalization from 30 provinces&amp;rsquo; manufacturing in China. Results show that: (1) Between 2007 and 2020, the development level of digital-green integration in the manufacturing industry shows an upward trend. There are three types of collaborative modes, namely, "industrial robot driven" "market development driven" and "market development driven - industrial robot driven", with each mode consisting of at least one to four equivalent pathways. (2) The marketization level and application of industrial robots are the important external and internal pulling forces that drive the synergy degree of digitalization and green change within manufacturing industry, respectively.(3) The influencing factors of the collaborative mode choice of digital-green development within manufacturing industry are complementary, among which, the degree of marketization is similar to the role of industrial robots in the configuration, which are capable to weaken the negative impact from insufficient government R&amp;amp;D investment support under specific conditions; (4) The non-high collaborative state of digital green in manufacturing industry is mainly due to the low level of marketization, openness, and economic development, as well as the low application rate of industrial robots and software products. This study enriches the theoretical understanding on digitalization and green change of manufacturing industry. Meanwhile, it also provides insights to advance the integrated and synergistic advancement of digital green transformation in manufacturing practice.&lt;/span&gt;&lt;/p&gt;

Research on maintenance-free design and low friction characteristics of industrial robot sliding components

With the continuous progress of industrial automation, the application of industrial robots in various fields has become increasingly common, and ensuring their efficient and stable operation and reducing maintenance costs is crucial. This study used powder metallurgy technology, combined with multiple sintering-rolling processes and oil-immersed vacuum assistance, to successfully prepare Fe-Cu-Ni-Sn-graphite oil-immersed self-lubricating composite materials to meet this demand. In-depth microstructure and wear surface studies revealed that this porous oil-immersed self-lubricating composite material exhibits long-term low friction and high wear resistance and reduces the friction coefficient by 40% compared to untreated samples after a specific composite process. In addition, the material exhibits excellent friction performance in the high-temperature pin-on-disc friction and wear test machine. Even after continuous sliding for 24 hours, its friction coefficient remains low and stable. The study also found that its lubrication mechanism may be attributed to solid-liquid synergistic lubrication, thanks to the appearance of oil-graphite mixtures around the wear track. This design ensures high rigidity and reduced frictional loss, providing a strong reference for the design and optimization of sliding components of industrial robots, and is highly suitable for the widespread application of industrial robots.

PHIR: A Platform Solution of Data-Driven Health Monitoring for Industrial Robots

The large-scale application of industrial robots has created a demand for more intelligent and efficient health monitoring, which is more efficiently met by data-driven methods due to the surge in data and the advancement of computing technology. However, applying deep learning methods to industrial robots presents critical challenges such as data collection, application packaging, and the need for customized algorithms. To overcome these difficulties, this paper introduces a Platform of data-driven Health monitoring for IRs (PHIR) that provides a universal framework for manufacturers to utilize deep-learning-based approaches with minimal coding. Real-time data from multiple IRs and sensors is collected through a cloud-edge system and undergoes unified pre-processing to facilitate model training with a large volume of data. To enable code-free development, containerization technology is used to convert algorithms into operators, and users are provided with a process orchestration interface. Furthermore, algorithm research both for sudden fault and long-term aging failure detection is conducted and applied to the platform for industrial robot health monitoring experiments, by which the superiority of the proposed platform, in reality, is proven through positive results.

Multi-scale progressive fusion-based depth image completion and enhancement for industrial collaborative robot applications

Multi-scale progressive fusion-based depth image completion and enhancement for industrial collaborative robot applications

Research on performance comparison of patrol path planning techniques for mobile robots in nuclear power plants

Path planning, as a basic problem of mobile robots, is important in the application of industrial patrol robots. This paper takes the nuclear power plant as an example and solves the problem of multi-target patrol path of patrol robot. Firstly, this paper processes multi-target points applying Euler's formula to obtain a reasonable order of patrol target points. Then three path planning algorithms, A* algorithm based on graph search, RRT* algorithm based on sampling and Q-learning algorithm based on reinforcement learning, are applied on path planning, and combined with Minimal-Jerk algorithm for optimizing trajectories. The performance of the results is finally compared using two evaluation metrics. The acceleration variance and route analysis in planar images are used as evaluation metrics in this paper. It is considered that the smaller the acceleration variance, the more smoothly the robot can move. The gentler the route is, the more effective the robot movement is.

Industrial robots and export growth: a cross-country analysis

ABSTRACT This paper investigates the relationship between industrial robot applications and export growth using data from 54 major exporting countries from 2010 to 2018. We find that industrial robot applications have positive effects on exports by reducing export costs and upgrading product technology. This effect is stronger in economically advanced countries. Moreover, technological innovation and educational investment amplify the impact of industrial robots. These findings offer valuable insights for policymakers seeking to optimize industrial robot integration to foster export.

Industrial Robot Application and Enterprise Financialization: Empirical Evidence from the Chinese Manufacturing Firm Level

Industrial Robot Application and Enterprise Financialization: Empirical Evidence from the Chinese Manufacturing Firm Level

How can industrial intelligence change the employment structure of the floating population?

As the wave of industrial intelligence (AI) swept, the demographic dividend era in the Chinese labor market continued to decrease. This study aimed to explore how AI reshaped the labor employment structure of the floating population. Additionally, it clarified the internal mechanism of AI on the employment structure of the floating population based on the existing AI model and the theoretical model of AI technology. At the same time, the workforce was divided into high-, medium-, and low-skilled groups according to education level. Empirical analysis was conducted using relevant data from 31 Chinese provinces spanning from 2012 to 2018. The aim was to test the impact of AI technology on the employment of different types of floating populations. The results indicated that: (1) industrial robots impacted heterogeneous skilled floating population labor by bipolar promotion and central substitution. (2) The application of industrial robots had a promotion effect on unfinished school and primary school groups, a substitution effect on middle school, high school/technical secondary school, and college specialties, and a promotion effect on college undergraduate and graduate students. (3) Distinguish employment status, industrial robot application had a significant negative impact on low-skilled employees and significant positive effects on high-skilled employers. Hence, it was recommended to put forward corresponding policy suggestions to address this issue.

Overview of image-based 3D reconstruction technology

Three-dimensional (3D) reconstruction technology is the key technology to establish and express the objective world by using computer, and it is widely used in real 3D, automatic driving, aerospace, navigation and industrial robot applications. According to different principles, it is mainly divided into methods based on traditional multi-view geometry and methods based on deep learning. This paper introduces the above methods from the perspective of three-dimensional space representation. The feature extraction and stereo matching theory of traditional 3D reconstruction methods are the theoretical basis of 3D reconstruction methods based on deep learning, so the paper focuses on them. With the development of traditional 3D reconstruction methods and the development of deep learning related theories, the explicit deep learning 3D reconstruction method represented by MVSNet and the implicit 3D reconstruction method represented by NeRF have been gradually developed. At the same time, the dataset and evaluation indicators for 3D reconstruction were introduced. Finally, a summary of image based 3D reconstruction was provided.

Artificial intelligence and carbon emissions inequality: Evidence from industrial robot application

To figure out equitable and inclusive strategy to mitigate inequality of carbon emissions, we investigate the solution to narrowing carbon inequality from the perspective of developing artificial intelligence by using the generalized method of moments model and employing a panel dataset from 74 countries during 2000–2019. The asymmetric impact of artificial intelligence on carbon inequality is also checked. In addition, we examine the moderating and mediating effects in the nexus between artificial intelligence and carbon inequality. We find that (1) artificial intelligence has a negative causal relationship with carbon inequality, which indicates that developing artificial intelligence is essential for narrowing carbon inequality. (2) With the increase of the quantile of carbon inequality, artificial intelligence exerts a more remarkable inhibiting effect on carbon inequality, which implies that when the level of carbon inequality is more severe, the implementation of artificial intelligence proves to be a more potent tool for narrowing the disparity of emissions. (3) With the help of climate finance, artificial intelligence becomes even more effective in reducing carbon inequality, verifying the synergistic effect of climate finance and artificial intelligence on carbon inequality eradication. (4) Energy structure transition and industry structure transition are pathways through which artificial intelligence affects carbon inequality. Some concrete policy implications are drawn from the above main findings.

Application of automatic driving task serialisation monitoring for operating robots

Abstract With the increasing application of industrial robots, more and more simple and repetitive operations in industrial automated production lines are replaced by industrial robots. In this study, a path planning algorithm is proposed for the autopilot task of an operating robot to find the optimal path from the start point to the goal point under the constraints of safe obstacle avoidance, shortest distance, and fastest time. Forward kinematics is used to detect whether it will collide with environmental obstacles until the first feasible result is detected as the end position for navigation. Serialized monitoring is used to obtain the activity information of each computational node, the hardware state information of the system, and the state of the autopilot task processing. Finally, obstacle avoidance navigation experiments on a mobile operating robot are conducted to verify the effectiveness and feasibility of the obstacle avoidance navigation algorithm. The results show that the maximum error of the autopilot task of the operating robot is 2.91cm, and the average error is 0.13 2cm, which is in line with the trajectory tracking error requirements of the operating machine control method and verifies the validity and practicability of this study.

Application of industrial robot based on infrared sensor technology in monitoring of electrical equipment

Abstract In this paper, infrared sensor technology is applied to the monitoring of electrical equipment, and a mathematical model of infrared image processing is built. In addition, the infrared image recognition model of electrical equipment is combined with the improved CenterNet, and ResNeXt50, which has stronger feature extraction capability, is used as the backbone network of CenterNet to improve detection accuracy. An infrared dataset of electrical equipment is used to verify the effectiveness of the improved CenterNet model and the other two models. Finally, the faults in substation equipment are analyzed and studied for real cases applied in practical work as an example. The results show that the algorithm in this paper has an mAP value of 93.7% and a frame rate of 40/S, which can balance the detection accuracy and detection speed in the electrical equipment infrared scene. Compared to traditional electrical equipment detection methods, the detection method of infrared sensor imaging is more reliable, with a 90% accuracy rate, and the highest has reached 100%. The article analyzes the characteristics and relationship between the local heating and temperature increase of the fault point of the equipment concerned, summarizes the fault judgment method, and verifies the effectiveness of infrared sensor imaging technology.