Operation Of Industrial Robots Research Articles

Research Progress on Autonomous Operation of Industrial Robots Based on Big Data and Machine Vision

Research Progress on Autonomous Operation of Industrial Robots Based on Big Data and Machine Vision

Robot Calibration Sampling Data Optimization Method Based on Improved Robot Observability Metrics and Binary Simulated Annealing Algorithm

As the societal demand for precision in industrial robot operations increases, calibration can enhance the end-effector positioning accuracy of robots. Sampling data optimization plays an important role in improving the calibration effect. In this study, a robot calibration sampling point optimization method based on improved robot observability metrics and a Binary Simulated Annealing Algorithm is proposed. Initially, a robot kinematic model based on the Product of Exponentials (POE) model and a generalized error model is established. By utilizing the least squares method, the ideal pose transformation relationship between the robot’s base coordinate system and the laser tracker measurement coordinate system is derived, resulting in an error calibration model based on spatial single points. An improved robot observability metric combined with the Binary Simulated Annealing Algorithm (BSAA) is introduced to optimize the selection of calibration sampling data. Finally, the robot’s parameter errors are obtained using a nonlinear least squares method. Experimental results demonstrate that the average end-effector positioning error of the robot after calibration can be reduced from 0.356 mm to 0.254 mm using this method.

Compact convolutional transformers- generative adversarial network for compound fault diagnosis of industrial robot

The safe operation of Industrial robots is a major concern in intelligent manufacturing. Accurate compound fault diagnosis is essential to the safe operation of industrial robots, while it is challenging to achieve since the compound fault samples are hard to be collected. Generative adversarial network (GAN) is a useful tool for addressing the data imbalance issue. However, the computation efficiency of GAN in addressing the data imbalance issue has not been investigated. Hence, this study proposes a lightweight GAN named compact convolutional Transformers-GAN (CCT-GAN) to alleviate the data imbalance issue in compound fault diagnosis modelling. Firstly, the feedback current signals collected from the industrial robot are transformed into time-frequency images via continuous wavelet transformation (CWT). Secondly, CCT-GAN is designed to achieve high-quality fake data generation and compound fault diagnosis modelling without large computational costs. Thirdly, the relation between a single fault and the compound fault is considered in the compound fault diagnosis modelling via multi-hot representation to alleviate the data imbalance issue. An experimental study based on the real-world compound fault dataset of industrial robots reveals that the proposed CCT-GAN shows merits in compound fault diagnosis modelling in comparison with the prevailing algorithms. The results indicate that CCT-GAN can performance of compound fault diagnosis when only 100 data samples from each compound fault category are available.

A digital twin approach to support a multi-task industrial robot operation using design of experiments

A digital twin approach to support a multi-task industrial robot operation using design of experiments

Evolution of Industrial Robots from the Perspective of the Metaverse: Integration of Virtual and Physical Realities and Human–Robot Collaboration

During the transition from Industry 4.0 to Industry 5.0, industrial robotics technology faces the need for intelligent and highly integrated development. Metaverse technology creates immersive and interactive virtual environments, allowing technicians to perform simulations and experiments in the virtual world, and overcoming the limitations of traditional industrial operations. This paper explores the application and evolution of metaverse technology in the field of industrial robotics, focusing on the realization of virtual–real integration and human–machine collaboration. It proposes a design framework for a virtual–real interaction system based on the ROS and WEB technologies, supporting robot connectivity, posture display, coordinate axis conversion, and cross-platform multi-robot loading. This paper emphasizes the study of two key technologies for the system: virtual–real model communication and virtual–real model transformation. A general communication mechanism is designed and implemented based on the ROS, using the ROS topic subscription to achieve connection and real-time data communication between physical robots and virtual models, and utilizing URDF model transformation technology for model invocation and display. Compared with traditional simulation software, i.e., KUKA Sim PRO (version 1.1) and RobotStudio (version 6.08), the system improves model loading by 45.58% and 24.72%, and the drive response by 41.50% and 28.75%. This system not only supports virtual simulation and training but also enables the operation of physical industrial robots, provides persistent data storage, and supports action reproduction and offline data analysis and decision making.

Exploration on Task-driven Teaching Design of Industrial Robot Operation and Programming Course in Secondary Vocational Schools

Currently, the national goal for the training of talents in vocational colleges and universities is to cultivate high-quality skilled talents with powerful practical ability, good thinking, and in line with the needs of the manufacturing industry. For the industrial robot operation and programming, which is a comprehensive practical training course with both theory and practice, focusing on the improvement of students' Introductory operation, offline programming ability, and familiarity with the use of network communication systems and other learning objectives, it is proposed to apply the "task-driven teaching method" throughout the whole teaching process to fully mobilize students' initiative and guide them to analyze the problems and improve their practical skills from different perspectives. The article focuses on the requirements of the course and discusses how to improve the student's capacity to apply theory in practice. The article focuses on the specific teaching design ideas of the task-driven teaching method and the multi-dimensional evaluation and assessment mode in this course in combination with the requirements of the course.

Multivariate time series anomaly detection: Missing data handling and feature collaborative analysis in robot joint data

The efficient operation of industrial robots relies on reliable anomaly detection systems, but the problem of missing data caused by sensor failures, data transmission errors, and system maintenance or updates in the industrial field poses a serious challenge to their accuracy and may lead to delays in recognizing critical faults, which in turn threatens production safety and causes economic losses. To address this problem, this paper constructs an Anomaly Detection Method with Combined Data Imputation and Feature Collaboration (AD-CIFC), which firstly employs a comprehensive imputation strategy to impute the missing data in order to better adapt to the natural flow of time series and complex patterns; then two graph attention mechanisms are proposed to be used in parallel to capture the relationship between nodes more comprehensively by introducing synergies, thus effectively utilizing the correlation between features to make the model more sensitive; finally, two collaborative learning models are proposed to be used in parallel to more comprehensively take into account the strong correlation between features in the data, and a collaborative loss function computation method is proposed, thus improving the overall detection accuracy and discrimination ability. Through anomaly detection experiments on industrial robots with different levels of missing joint data, it is demonstrated that the AD-CIFC model demonstrates a performance that matches or even outperforms other mainstream anomaly detection models in complete fault data tests, while the model performance using the comprehensive imputation strategy to impute in the joints is about 25 % higher on average compared to the model performance of traditional linear interpolation methods, and meanwhile, in the case of missing data at the minor to severe levels, the model still demonstrates robustness beyond traditional methods, which is very suitable for industrial robot production lines.

Calibration and pose measurement of a combined vision sensor system for industrial robot grasping of brackets

Recently, visual sensing measurement and its application in industrial robot operations have been widely researched, promoting the development of instrumentation and automation. This study proposes a combined vision sensor system for robot grasping, focusing on combined sensor system calibration and bracket pose measurements. The system configuration and working strategy of the combined vision system are introduced. Thereafter, the calibration of the combined vision coordinate systems is presented, wherein a global vision system acts as the external measuring equipment for accurately calibrating the local vision. Furthermore, a pose estimation method using a local vision system (LVS) is proposed, including morphology-based image enhancement and principal component analysis based corner recognition methods. Verification experiments, including combined calibration and bracket pose measurements, were performed to validate the effectiveness and accuracy of the proposed combined vision measurement strategy. The results demonstrated that the proposed system applies to industrial robot grasping of brackets. In addition, the proposed robot-sensor calibration method improves calibration accuracy. Finally, the proposed corner detection method is effective and accurate for different bracket detection applications. This study provides a system that improves robot grasping results by considering key factors, such as vision measurement accuracy, and calibration methods.

Industrial Robot Digital Twin Application Technology

This paper mainly discusses the core position of the course "Industrial robot Application Technology" in the national double high construction professional group and the innovation of teaching methods. The "task-driven, process" of production-teaching collaboration built through digital twin technology aims to cultivate students' design development. The importance of the course "Industrial Robot Application Technology" is introduced. Teachers' teaching innovation team, national production training base and national science and technology innovation service platform support the digital development of this technology in intelligent manufacturing to create a digital twin teaching platform. In the course material content-based teaching design and the docking of industrial needs, including and industrial robot operation certification. In addition, the action-oriented teaching concept, ideological and political materials are integrated into the teaching design, and the paper emphasizes the teaching material compilation method and three-dimensional teaching material design. At the same time, it also has a potential impact on the expected model innovation of the application and promotion of teaching materials and continuing education.

Exploration of Civics Teaching in the ‘Industrial Robot Programming and Operation’ Course

In order to actualize moral and collaborative education, the reform of ideological and political aspects within professional courses becomes imperative. This paper explicates the necessity of instigating ideological and political education due to the singular nature of traditional teaching in the ‘Industrial Robot Programming and Operation’ course and its lack of emphasis on ideological and political elements. It extensively explores the embedding of ideological and political components within the course. By amplifying teachers’ awareness of ideological and political principles, refining the course’s training objectives, diversifying teaching methodologies, and enhancing the course evaluation mechanism, we implement specific practices that integrate ideology and politics into the course. The aim is to cultivate highly proficient technical and skilled individuals.

Voice user interfaces in manufacturing logistics: a literature review

AbstractDue to the increasing digitalization in manufacturing logistics, devices to integrate the worker into the digital manufacturing system are necessary. A voice user interface (VUI) can be considered suitable for this purpose due to its flexibility and intuitive operability. Despite the popularity and acceptance of VUIs in everyday life, their use in industrial applications, especially in manufacturing logistics, is still rare. While VUIs have been successfully used in order picking for decades, hardly any other industrial fields of application exist. In this paper, we have identified various barriers to the use of VUI in industrial applications. We categorized them and identified four key barriers. We then conducted a systematic literature review to determine and compare already investigated application areas of VUIs, their characteristics, advantages and disadvantages. We found that in particular the operation of machines and industrial robots, as well as general data and information output on machine and system status, maintenance and employee training are frequently investigated. It is noticeable that VUIs are often used in combination with other user interfaces (UIs). Some challenges to VUI usage, such as high ambient noise levels, have already been solved through various approaches, while other challenges remain. Based on the results of the literature review, we put forward a research agenda regarding further suitable industrial application areas as well as general challenges for the use of VUIs in industrial environments.

Determining the Advantages of a Linear Driven 3-Axis Industrial Robot by Structural and Force Analysis

The most important factors affecting the operation of industrial robots are the carrying capacity, the weight of the parts, the vibrations in the arms and the cost. The cost of the robot increases as the weight of the parts are increased. The forces falling on the motors and the accelerations at the end of the robot driven by the pushing force or rotational moment were measured and compared. In addition, modal analysis, forced vibration analysis and harmonic analysis of the robot were performed in two different drive systems, and the structural condition of the robot was examined. Thus, the robot designed in this study has advantages in terms of both structural and cost compared to other robot arms. In this design, the torques of the motors are very small and using smaller motors in each arm of the robot and to making the movement according to these weights reduce the cost very much. Likewise, it is seen that the robot can be operated using this design according to the accelerations measured at the end-point and the structural analysis. This robot can be applied to systems that have lower cost and need to carry more payloads.

P‐38: Display Industry Industrial Robot Failure Warning System

Along with the concept of "Industry 4.0" in recent years, intelligent manufacturing has been increasingly emphasized by many enterprises in the production process. In the display industry, the normal and efficient operation of industrial robots is directly related to the output and yield of a company 's products. The "failure warning system" introduced in this paper enables real‐time monitoring and analysis of key parameters of the robot. This ensures that the status and operation of the robot's components are always available, so that maintenance can be planned in advance and uncontrollable downtime can be avoided

The Importance of Embedding a General forward Kinematic Model for Industrial Robots with Serial Architecture in Order to Compensate for Positioning Errors

This paper proposes a methodology for creating simplified structural schemes and forward geometric models for industrial robots with serial architecture, with the goal of reducing thermal deformation errors that negatively impact positioning accuracy during operation. Unlike classical approaches, the proposed methodology introduces modifications to the order of matrix multiplication and incorporates new parameters to create a forward geometric model that better corresponds to the deformation characteristics of these robots. Details are presented on how to build and employ this extended model and integrate it into a thermal error compensation algorithm. The implementation of the algorithm in a software application is presented along with experimental results that demonstrate its effectiveness. This work addresses a real phenomenon that occurs in industrial robot operation and has implications for improving the performance of robots in manufacturing applications.

Review on vibration test and fault analysis of rv reducer

As the core component of industrial robot RV reducer, its health condition determines the life and accuracy of the robot. The main reason that restricts the long-term stable operation of industrial robots. This paper analyzes and compares the advantages and disadvantages of various technologies from three aspects: state detection, signal processing and fault diagnosis. The problems of developing real-time online evaluation are also discussed. Deep data mining and exploring new methods of RV reducer condition monitoring are the research difficulties in this field. It provides a reference for further research in the field of RV reducer.

Fault diagnosis model of multi-axis industrial robot based on triplet network

Fault diagnosis is an important link in intelligent development of industrial robots. Aiming at the problem of weak fault diagnosis performance caused by insufficient training samples, a fault diagnosis model based on triplet network is proposed. Firstly, we combine the multiscale convolutional neural network (MSCNN) with channel attention networks (squeeze-and-excitation network, SENet), and use it to construct a triple sub-network structure MS-SECNN, which can adaptively extract features from the original fault signal. Then, the feature similarity is calculated by triplet loss in the low dimensional space to realize the fault classification task. The experiments are based on the real industrial robot operation data set. In this model, we use Few-shot learning strategy to test the diagnostic performance under small samples, and compare it with WDCNN, FDCNN and MSCNN models. Experimental results show that the proposed model has more effective fault classification ability under small samples. In addition, when the training sample size is 1400, the average accuracy of MS-SECNN reaches 99.21%.

Teaching of Industrial Robot Spot Welding Project Based on Work Process Orientation

The work process oriented curriculum originated in Germany and is a typical curriculum model of the integration of theory and practice with work and learning. Based on the concept of work process orientation, this article takes a complete industrial robot spot welding debugging application project as an example, selects a knowledge point that covers the operation and programming of industrial robots, and conducts teaching practice analysis and discussion according to the six basic working processes of information, planning, decision-making, implementation, inspection, and evaluation, with a view to providing reference for the teaching of industrial robot related courses.

Non-intrusive Anomaly Detection of Industrial Robot Operations by Exploiting Nonlinear Effect

With the development of Internet of Robotic Things concept, low-cost radio technologies open up many opportunities to facilitate the monitoring system of industrial robots, while the openness of wireless medium exposes robots to replay and man-in-the-middle attackers, who send pre-recorded movement data to mislead the system. Recent advances advocate the use of high-resolution sensors to monitor robot operations, which however require invasive retrofit to the robots. To overcome this predicament, we present RobotScatter, a non-intrusive system that exploits the nonlinear effect of RF circuits to fuse the propagation of backscatter tags attached to the robot to defend against active attacks. Specifically, the backscatter propagation interacted by the tags significantly depends on various movement operations, which can be captured with the nonlinearity at the receiver to uniquely determine its identity and the spatial movement trajectory. RobotScatter then profiles the robot movements to verify whether the received movement information matches the backscatter signatures, and thus detects the threat. We implement RobotScatter on two common robotic platforms, Universal Robot and iRobot Create, with over 1,500 operation cycles. The experiment results show that RobotScatter detects up to 94% of anomalies against small movement deviations of 10mm/s in velocity, and 2.6cm in distance.

A Remote Monitoring and Maintenance System for Industrial Robots

Technologies such as external sensors, data acquisition, trend analysis, and cloud services make remote operation and maintenance of equipment status monitoring more convenient and lower maintenance costs. In this paper, an industrial robot operation and maintenance system is designed. First, the online monitoring of the robot is realized by collecting the control parameters, state parameters, fault information and external sensor monitoring information of the industrial robot. Then, the health baseline of the industrial robot is established based on the historical operation data. The online data is compared with the baseline data to realize the online assessment of the robot’s health status. Finally, a visualization platform for remote operation and maintenance of the robot is built to realize the online display of parameters, health trend tracking and fault status early warning, which is convenient for maintenance personnel to take timely countermeasures.

An effective self-collision detection algorithm for multi-degree-of-freedom manipulator

In order to address the self-collision problem associated with the operation of modern industrial robots, this paper proposes a multi-degree-of-freedom collision detection algorithm that can detect self-collision in single arm and double arm robots as well as collision with the load. Firstly, the zero pose of the Denaviti−Hartenberg model is built based on the manipulator configuration, and the coordinate information of each key point is obtained through a rotation and translation operation of the matrix. Then, the positional relation and distance between the detected objects are determined by the spatial geometry theory, and finally, collision is detected using a collision matrix. By simulating two groups of single arms and two groups of double arms, and from the laboratory testing of SR10C in the SIASUN robot factory, it has been verified that the proposed algorithm has good collision detection capability. Without the use of sensors, cameras, and other external devices, the collision between the arm and the load, and the collision between the cooperative robot and the load may be effectively detected and mitigated.