Industrial Robot Research Articles

Industry and artificial intelligence: industrial robot localization based on improved monte carlo algorithm

Industry and artificial intelligence: industrial robot localization based on improved monte carlo algorithm

Analysis of Factors Influencing the Precision of Body Tracking Outcomes in Industrial Gesture Control.

The body tracking systems on the current market offer a wide range of options for tracking the movements of objects, people, or extremities. The precision of this technology is often limited and determines its field of application. This work aimed to identify relevant technical and environmental factors that influence the performance of body tracking in industrial environments. The influence of light intensity, range of motion, speed of movement and direction of hand movement was analyzed individually and in combination. The hand movement of a test person was recorded with an Azure Kinect at a distance of 1.3 m. The joints in the center of the hand showed the highest accuracy compared to other joints. The best results were achieved at a luminous intensity of 500 lx, and movements in the x-axis direction were more precise than in the other directions. The greatest inaccuracy was found in the z-axis direction. A larger range of motion resulted in higher inaccuracy, with the lowest data scatter at a 100 mm range of motion. No significant difference was found at hand velocity of 370 mm/s, 670 mm/s and 1140 mm/s. This study emphasizes the potential of RGB-D camera technology for gesture control of industrial robots in industrial environments to increase efficiency and ease of use.

Obstacle avoidance planning for industrial robots based on singular manifold splitting configuration space

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

How industrial robots affect employment: firm-level evidence from China

Using China’s Annual Survey of Industrial Firms (ASIF) and the China Customs Database (CCD) from 2000 to 2013, this study explores the impact of adopting industrial robots on the employment in Chinese manufacturing firms. We find a positive association between the adoption of industrial robots and the employment scale of China’s manufacturing enterprises. We also find evidence that the positive association is more pronounced in capital-intensive industries, non-state-owned firms, and firms in coastal areas. Mechanism test indicates that ‘scale expansion effects’ and ‘productivity effects’ are the main channels through which industrial robots promote employment growth. Further analysis indicates that the adoption of industrial robots did not have obvious industry spillover effects. Additionally, other labor market results demonstrate that the adoption of industrial robots significantly increases the average wage level of enterprises while decreasing their labor share. This study provides micro-level evidence revealing the impact of adopting industrial robots on employment in developing countries. It also provides important policy implications for countries worldwide seeking to promote automated production.

Temperature and corporate industrial robot adoption: evidence from China

ABSTRACT Our study explores the impact of temperature changes on corporate industrial robot adoption using data from Chinese non-financial listed firms in the manufacturing sector between 2009 and 2019. We find that an increase in temperature positively affects corporate adoption of industrial robots. This positive relationship between temperature increases and corporate adoption of industrial robots is significant in the years following the introduction of China’s high-temperature subsidy policy and for non-state-owned firms, eastern-located firms, firms with low equity concentration, and firms with high financial constraints. A mechanism analysis reveals that rising temperatures decrease labour productivity and increase the employee cost.

The fourth industrial revolution and the future of work: Reasons to worry and policies to consider

AbstractA striking feature of the contemporary U.S. economy is the widespread adoption of technologies associated with the 4th Industrial Revolution (IR) including sensors, industrial robotics, artificial intelligence, and machine learning. Empirical work investigating the labor market impacts of 4th IR technological advances reveals they are skill‐biased, primarily advancing job opportunities for those with high levels of formal of education, unlike prior industrial revolutions that were skill neutral. This paper explains how the 4th IR has fostered job polarization, greater technological unemployment, and increasing wage inequality. Moreover, we delineate why these developments are worrisome and likely to expand over time. We assert that policy intervention to promote reinstatement of technologically unemployed persons is warranted, because displacement of labor caused by automation creates negative externalities. Consequently, we provide an example of how tax policies could be used to curtail technological unemployment—without creating hurdles for the adoption of 4th IR innovations. We advance and describe a new, innovative way, for fiscal policy to foster employment opportunities for those at greatest risk of job loss through automation. The initiative entails government subsidies to promote the provision and acquisition of skill certificates that employers view as complementary with 4th Industrial Revolution Technologies.

The impact of industrial robots on low-carbon green performance: Evidence from the belt and road initiative countries

Global warming is intensifying, with Belt and Road Initiative (BRI) countries accounting for over two-thirds of global carbon dioxide emissions. As a pivotal technology in advanced manufacturing, industrial robots significantly impact the overall carbon footprint through their role in production processes. This study examines how the use of industrial robots influences the Low-Carbon Green Performance (LCGP) across BRI nations, utilizing a dataset spanning from 2004 to 2020. The findings reveal that the integration of industrial robots notably boosts the LCGP within these countries, a conclusion supported by extensive robustness evaluations. The novelty of this study lies in uncovering the underlying mechanism by which industrial robots improve LCGP through the promotion of technological innovation. Specifically, we find that industrial robots have a pronounced effect on technological progress (TC), an impact that is further amplified by increases in labor productivity and human capital levels. This discovery provides policy implications for BRI governments, suggesting that actively promoting the development of industrial robots can accelerate energy transformation and upgrading, thereby reducing carbon emissions. Our research fills a significant void concerning the environmental impact of industrial robots and offers new perspectives and strategies for BRI countries to achieve low-carbon development goals. These findings contribute significant theoretical and practical value to global environmental protection and sustainable development.

An Integrated Software-Defined Networking–Network Function Virtualization Architecture for 5G RAN–Multi-Access Edge Computing Slice Management in the Internet of Industrial Things

The Internet of Things (IoT), namely, the set of intelligent devices equipped with sensors and actuators and capable of connecting to the Internet, has now become an integral part of the most competitive industries, as it enables optimization of production processes and reduction in operating costs and maintenance time, together with improving the quality of products and services. More specifically, the term Industrial Internet of Things (IIoT) identifies the system which consists of advanced Internet-connected equipment and analytics platforms specialized for industrial activities, where IIoT devices range from small environmental sensors to complex industrial robots. This paper presents an integrated high-level SDN-NFV architecture enabling clusters of smart devices to interconnect and manage the exchange of data with distributed control processes and databases. In particular, it is focused on 5G RAN-MEC slice management in the IIoT context. The proposed system is emulated by means of two distinct real-time frameworks, demonstrating improvements in connectivity, energy efficiency, end-to-end latency and throughput. In addition, its scalability, modularity and flexibility are assessed, making this framework suitable to test advanced and more applications.

Artificial-Intelligence-Based Condition Monitoring of Industrial Collaborative Robots: Detecting Anomalies and Adapting to Trajectory Changes

The increasing use of collaborative robots in smart manufacturing, owing to their flexibility and safety benefits, underscores a critical need for robust predictive maintenance strategies to prevent unexpected faults/failures of the machine. This paper focuses on fault detection and employs multivariate operational data from a universal robot to detect anomalies or early-stage faults using test data from designed anomalous conditions and artificial-intelligence-based anomaly detection techniques called autoencoders. The performance of three autoencoders, namely, a multi-layer-perceptron-based autoencoder, convolutional-neural-network-based autoencoder, and sparse autoencoder, was compared in detecting anomalies. The results indicate that the autoencoders effectively detected anomalies in the examined complex and noisy datasets with more than 93% overall accuracy and an F1 score exceeding 96% for the considered anomalous cases. Moreover, the integration of trajectory change detection and anomaly detection algorithms (i.e., the dynamic time warping algorithm and sparse autoencoder, respectively) was proposed for the local implementation of online condition monitoring. This integrated approach to anomaly detection and trajectory change provides a practical, adaptive, and economical solution for enhancing the reliability and safety of collaborative robots in smart manufacturing environments.

A framework of insole blanking robot based on adaptive edge detection and FSPS-BIT* path planning

Insole blanking production technology plays a vital role in contemporary machining and manufacturing industries. Existing insole blanking production models have limitations because most robots are required to accurately position the workpiece to a predetermined location, and special auxiliary equipment is usually required to ensure the precise positioning of the robot. In this paper, we present an adaptive blanking robotic system for different lighting environments, which consists of an industrial robot arm, an RGB-D camera configuration, and a customized insole blanking table and mold. We introduce an innovative edge detection framework that utilizes color features and morphological parameters optimized through particle swarm optimization (PSO) techniques to Adaptive recognition of insole edge contours. A path planning framework based on FSPS-BIT* is also introduced, which integrates the BIT* algorithm with the FSPS algorithm for efficient path planning of the robotic arm.

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.

Sparse Convolution-Based 6D Pose Estimation for Robotic Bin-Picking With Point Clouds

Abstract Estimating the orientation and position of objects is a crucial step in robotic bin-picking tasks. The challenge lies in the fact that, in real-world scenarios, a diverse array of objects is often randomly stacked, resulting in significant occlusion. This study introduces an innovative approach aimed at predicting 6D poses by processing point clouds through a two-stage neural network. In the initial stage, a network for scenes with low-textured environments is designed. Its purpose is to perform instance segmentation and provide an initial pose estimation. Entering the second stage, a pose refinement network is suggested. This network is intended to enhance the precision of pose prediction, building upon the output from the first stage. To tackle the challenge of resource-intensive annotation, a simulation technique is employed to generate a synthetic dataset. Additionally, a dedicated software tool has been developed to annotate real point cloud datasets. In practical experiments, our method demonstrated superior performance compared to baseline methods such as PointGroup and Iterative Closest Point. This superiority is evident in both segmentation accuracy and pose refinement. Moreover, practical grasping experiments have underscored the method's efficacy in real-world industrial robot bin-picking applications. The results affirm its capability to successfully address the challenges produced by occluded and randomly stacked objects.

Managing workplace AI risks and the future of work.

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

The Wear Behaviour of a New Eccentric Meshing Reducer with Small Teeth Difference

Eccentric meshing reducers are widely used in agriculture, industrial robots, and other fields due to their ability to achieve a high reduction ratio within a compact volume. However, the contact wear problem seriously affects the service performance of the eccentric meshing reducer, thereby limiting their range of applications. To effectively address this issue, this study involved a stress analysis of the contact pairs and a surface wear analysis of a new eccentric meshing reducer. The wear equation for the contact pairs was derived using Archard’s wear theory, incorporating geometric and material parameters from both the reducer gear contact pair and the spline contact pair. In parallel, a wear simulation was conducted by integrating the UMESHMOTION subprogram with ALE adaptive grids. Additionally, the effects of load amplitudes on contact pair stress and surface wear were systematically investigated. It is revealed that the contact pair stress of the reducer gear was higher than that of the spline contact pair. Furthermore, the internal spline exhibited the highest wear rate, followed by the output shaft gear, external spline, and input shaft gear, in that order. This work provides a comprehensive and in-depth understanding of the wear behaviors of general reducers with small teeth differences and offers valuable scientific references for design optimization, fault diagnosis, and maintenance strategy formulation.

Automatic and Flexible Robotic Drawing on Complex Surfaces With an Industrial Robot

Automatic and Flexible Robotic Drawing on Complex Surfaces With an Industrial Robot

METROLOGICAL SUPPLY AND METROLOGICAL CONTROL APPLICATION FOR DEVICES AIMED AT PROGRAMMABLE AUTOMATION

Automation in manufacturing refers to using technology and machines to perform specific tasks without the need for humans to intervene. The goal of automation is to increase efficiency, productivity, and accuracy in the production process, reducing manual labour and minimizing the risk of human error.Recent supply chain disruptions and a squeeze on labour markets means automation has now become more important than ever.According to the International Federation of Robotics, there are around 3.5 million industrial robots in operation around the globe. The Federation noted this represents a growth rate of more than 30% year-on-year, with a massive surge in demand since the pandemic.

METROLOGICAL SUPPLY AND METROLOGICAL CONTROL APPLICATION FOR DEVICES AIMED AT PROGRAMMABLE AUTOMATION

Automation in manufacturing refers to using technology and machines to perform specific tasks without the need for humans to intervene. The goal of automation is to increase efficiency, productivity, and accuracy in the production process, reducing manual labour and minimizing the risk of human error.Recent supply chain disruptions and a squeeze on labour markets means automation has now become more important than ever.According to the International Federation of Robotics, there are around 3.5 million industrial robots in operation around the globe. The Federation noted this represents a growth rate of more than 30% year-on-year, with a massive surge in demand since the pandemic.

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.

4 DEGREE OF FREEDOM ARM ROBOTICS TRAINING FOR STATE VOCATIONAL HIGH SCHOOL 1 DEPOK

This activity provides knowledge about the basics and latest applications in the field of Robotics, especially industrial robots and teaches students to be able to assemble robots. 4 Degree of Freedom Arm Robot is a set of mechanical and electronic tools (mechatronics) that can carry out physical tasks, with 4 degrees of freedom of arm movement robots, namely 1 forward-backward, 2 left-right, 3 up-down. Then there are also three movements at the wrist, namely 4 yaw, 5 pitch and 6 roll. The robot arms have functions to move objects with control using a joystick. With the hope that it can make human work easier in moving objects. To be able to provide students with an understanding of robotics, qualified teachers are needed who have mastered the field of electrical engineering, especially robotics. From the comparison of test results before and after participating in activities by students, a significant increase in knowledge was obtained. Apart from that, students also succeeded in assembling a robot using the materials and components provided.

Systematic analysis of geometric inaccuracy and its contributing factors in roboforming

Incremental sheet metal forming is a highly versatile die-less forming process for manufacturing complex sheet metal components. Robot-assisted incremental sheet forming, or roboforming, allows a wider range of tool motion, providing the capability to shape intricate components. This makes roboforming the most flexible variant of the incremental forming method. However, the serial arrangement of links and joints in a robotic manipulator results in low positional accuracy under forming loads due to insufficient structural stiffness. The stiffness of the machine frame and tool directly impacts the accuracy of the final formed profile. The impact of machine compliance on component shape in incremental sheet forming is substantial in roboforming. This work presents a methodology for systematic analysis of the factors contributing to the errors in the geometric shape of robot-based forming. Experimental and numerical methods are used to estimate the material springback, tool/tool holder deflections, and errors due to machine compliance. The CNC machine frame is relatively stiffer than the industrial robots, such that material springback is estimated based on the experimental trials on CNC for cone and variable wall angle cone profiles. Tool and tool holder deflections are estimated using finite element simulations. The analytical method using the Virtual Joint Model is used to model the joint stiffness, and consequently, the robot Cartesian stiffness is estimated to predict path deviation contributing to geometric shape errors. The proportional contribution of each factor in the overall deviation in the Roboforming is also quantified.