Industrial Robot Research Articles

Vision-guided robot calibration using photogrammetric methods

We propose novel photogrammetry-based robot calibration methods for industrial robots that are guided by cameras or 3D sensors. Compared to state-of-the-art methods, our methods are capable of calibrating the robot kinematics, the hand–eye transformations, and, for camera-guided robots, the interior orientation of the camera simultaneously. Our approach uses a minimal parameterization of the robot kinematics and hand–eye transformations. Furthermore, it uses a camera model that is capable of handling a large range of complex lens distortions that can occur in cameras that are typically used in machine vision applications. To determine the model parameters, geometrically meaningful photogrammetric error measures are used. They are independent of the parameterization of the model and typically result in a higher accuracy. We apply a stochastic model for all parameters (observations and unknowns), which allows us to assess the precision and significance of the calibrated model parameters. To evaluate our methods, we propose novel procedures that are relevant in real-world applications and do not require ground truth values. Experiments on synthetic and real data show that our approach improves the absolute positioning accuracy of industrial robots significantly. By applying our approach to two different uncalibrated UR3e robots, one guided by a camera and one by a 3D sensor, we were able to reduce the RMS evaluation error by approximately 85% for each robot.

Development and potential of robotisation and automation in Kazakhstan's automobile industry: a bibliographical and analytical review

The engineering sector, which includes automobile production, plays an important role in the national economy and industrial progress. It affects the development of production, scientific and technical progress, labor productivity, national security, quality of life. In order to increase productivity, reduce labor costs and improve product quality, industrial automation and robotization processes have been significantly developed in industrialized countries of the world. The global market for industrial robots is expected to grow 14.3% annually and reach $30.8 billion by 2027. However, Kazakhstan faces special difficulties in adopting these technologies due to the lack of development of automation infrastructure. This study aims to develop priorities and directions of advanced scientific and technological solutions for intelligent joint systems of robotics and automation, especially in the automotive industry of Kazakhstan. In this regard, the purpose of the study is to assess the current state of automation in the automotive industry of Kazakhstan, to identify challenges and opportunities associated with the introduction of robotics, and to provide a strategic basis for the advancement of these technologies. The research corresponds to the state programs of Kazakhstan on science, technology and digitization. The scientific and practical importance of this work is that it is aimed at providing solutions for increasing efficiency by adapting advanced robotic technologies to local production systems. Through the development of domestic innovations and the transfer of global technologies, research contributes to the field of industrial automation and helps Kazakhstan's industries to achieve global competitiveness. It is expected that the practical results of the research will contribute to the improvement of production quality, efficiency and productivity in the automotive sector of Kazakhstan, contributing to sustainable development in accordance with world trends. The value of the work lies in providing individual, technologically advanced solutions to meet the nation's urgent need for robotics automation and integration.

AAT4IRS: automated acceptance testing for industrial robotic systems.

Industrial robotic systems (IRS) consist of industrial robots that automate industrial processes. They accurately perform repetitive tasks, replacing or assisting with dangerous jobs like assembly in the automotive and chemical industries. Failures in these systems can be catastrophic, so it is important to ensure their quality and safety before using them. One way to do this is by applying a software testing process to find faults before they become failures. However, software testing in industrial robotic systems has some challenges. These include differences in perspectives on software testing from people with diverse backgrounds, coordinating and collaborating with diverse teams, and performing software testing within the complex integration inherent in industrial environments. In traditional systems, a well-known development process uses simple, structured sentences in English to facilitate communication between project team members and business stakeholders. This process is called behavior-driven development (BDD), and one of its pillars is the use of templates to write user stories, scenarios, and automated acceptance tests. We propose a software testing (ST) approach called automated acceptance testing for industrial robotic systems (AAT4IRS) that uses natural language to write the features and scenarios to be tested. We evaluated our ST approach through a proof-of-concept, performing a pick-and-place process and applying mutation testing to measure its effectiveness. The results show that the test suites implemented using AAT4IRS were highly effective, with 79% of the generated mutants detected, thus instilling confidence in the robustness of our approach.

Influence of financial inclusion and industrial robots on the efficiency of China's OFDI: A heterogeneous spatial stochastic frontier analysis

Enhancing the efficacy of China's outward foreign direct investment (COFDI) constitutes a significant avenue for realizing the sustainable development of China's economy. This study analyzes panel data from 63 countries from 2010 to 2021 to create a heterogeneous spatial stochastic frontier model to examine how financial inclusion and industrial robotics in host nations affect COFDI efficiency. The findings are as follows. To begin with, COFDI manifests positive spatial spillover effects across countries. Moreover, from a spatial perspective, financial inclusion diminishes COFDI efficiency, whereas the interaction between financial inclusion and industrial robots increases its efficiency. Industrial robots, thus, counteract the diminishing impact of financial inclusion on COFDI efficiency. Additionally, concerning the heterogeneity of location choice and investment motivation in COFDI, the interaction between financial inclusion and industrial robots in developed economies augments COFDI efficiency as opposed to emerging market economies. Conversely, technology-seeking investment mitigates the adverse effects of financial inclusion on the efficacy of COFDI when juxtaposed with resource-seeking investment. This study provides theoretical insights and practical recommendations for improving the efficiency and guiding the destination selection of COFDI.

Nonlinear terminal sliding mode trajectory tracking control algorithm for industrial robots based on dung beetle optimizer

Abstract To enhance the precision of trajectory tracking control in industrial robots, a nonlinear end-to-end sliding mode trajectory tracking control algorithm for industrial robots based on the dung beetle algorithm is proposed. By employing nonlinear terminal sliding mode control to resolve the issue of inadequate trajectory tracking control performance in industrial robots, and integrating the dung beetle optimizer (DBO) algorithm for control parameter adjustment, significant enhancements are achieved in trajectory tracking accuracy.

An error compensation method for on-machine measuring blade with industrial robot

To achieve the seamless integration of precision grinding blade with timely measured data, an on-machine measurement system employing industrial robot is developed. The limitation to its widespread application lies in the accuracy of measurement, prompting the proposal of an error compensation method to address the measurement error within the implemented blade on-machine measurement system. To address the pre-travel error in the measuring device of the on-machine measurement system, an analysis of the factors influencing the pre-travel error of the contact probe is conducted. Subsequently, pre-travel error data is gathered utilizing the experimental calibration method involving a standard ball, and a prediction model employing the bilinear interpolation algorithm is established to facilitate error compensation. Employing pre-travel error compensation for the analysis of the operating body error in the on-machine measurement system, a spherical center distance constraint error model is derived through closed-loop kinematic calibration of the six-degrees-of-freedom industrial robot. Kinematic parameter error identification is conducted using a hybrid algorithm combining the L-M algorithm and the adaptive factor double-variable DE algorithm. This approach diminishes the spherical center distance measurement error, reducing it from 0.081 mm to 0.016 mm. Subsequently, experiment is conducted to measure blade machining allowances using an on-machine robotic measurement system, comparing the obtained data with measurement from a blue light scanner and a coordinate measuring machine (CMM). The results reveal average absolute deviations of 0.020 mm, 0.015 mm, and 0.016 mm in the three blade cross sections for the on-machine robotic measurement system and the blue light scanner, respectively. Correspondingly, the average absolute deviations for the on-machine robotic measurement system and the CMM in the three blade sections are 0.028 mm, 0.029 mm, and 0.029 mm. Moreover, the on-machine measurement system demonstrates commendable measurement repeatability, with a standard deviation of 0.003 mm.

An experimental study of ultrasonic-knife cutting for a woven carbon fiber preform by an industrial robot

This study investigates the effect of the cutting process parameters on the cutting forces and the quality parameters of a woven carbon fiber preform during robotic ultrasonic-knife cutting. An ultrasonic cutting device, with a power of 1200 W, a frequency of 24 kHz, and an amplitude of 60 µm, mounted on the end effector of a six-axis degree of freedom industrial robot to make linear cuts. A three-level factorial experimental design was used to examine the effect of the feed rate (1 m/min to 5 m/min) and the knife’s attack angle (45° to 75°) on the cutting forces, the dimensional accuracy of the machined preform coupons, and the damage on the machined preform edges. The cutting force analysis results show that the increasing feed rate resulted in increasing feed force and thrust force. However, the increase of the attack angle increases the feed force but decreases the thrust force. The average width and damage of the ultrasonic knife cut preform coupons are highly related to the process conditions. The combination of the low feed rate, 1 m/min, and the low attack angle, 45°, resulted in dimensional errors ranging from 253 μm to 365 μm oversized from the programmed 15.0 mm width with no damage. When the feed became 3 m/min and 5 m/min at the attack angle of 75°, the preform coupons’ dimensional accuracy and damage formation worsened. In these conditions, the ultrasonic knife attached to the industrial robot arm could not cut the preform plate effectively, so the tows on the preform were unevenly cut or dislodged.

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.

Industrial robot energy consumption model identification: A coupling model-driven and data-driven paradigm

Due to wide distribution and low energy efficiency, the energy-saving in industrial robots (IRs) is attracting extensive attention. Accurate energy consumption (EC) models of IRs lay the foundation for energy-saving. However, most dynamic and electrical parameters of IRs are not disclosed by manufacturers, which leads to the invalidity of most model-based EC prediction methods. To bridge this gap, a mechanism-data hybrid-driven method is proposed to predict the EC of IRs in this paper. First, a joint torque prediction model integrating a hybrid-driven parameter identification is developed based on deep reinforcement learning (DRL). The framework for DRL-based parameter identification is constructed through tailored design of interfaces and training mechanisms, wherein the DRL agent can learn to identify the dynamic parameters from the trajectory database. And a deep neural network based on long short-term memory (LSTM) is proposed to predict the EC of IRs according to the joint torques and velocities. The nonlinear item, which is not modeled in the robot dynamic equation, are also encapsulated in the deep neural network with one-dimensional convolutional neural network (1D-CNN) layers to improve the prediction accuracy. To validate the accuracy and efficacy of the proposed method, experiments are conducted on a KUKA KR60-3 industrial robot with different loads. The results demonstrate that the proposed method can predict EC with a mean absolute percentage error of less than 2% under a fixed load and less than 3% under loads not used for agent training.

5G-Cloud-based real-time robotic part repairing for advanced manufacturing via computer vision

In this paper, we delve into the transformative capabilities of 5G wireless communication technology within the realm of robotics, particularly in the context of advanced manufacturing. Leveraging the power of computer vision and cloud/edge computing, we unveil the boundless possibilities that this technology heralds. Leveraging the ultra-reliable low latency communication (uRLLC) of 5G, we establish a distributed system showcasing its potential to revolutionize industrial processes. Additionally, to validate our framework, we developed a testbed tailored for real-world applications, focusing on milling operations using an industrial robot. Through high-level lidar data processing, we identify defects and create toolpaths, seamlessly to execute milling tasks. This research highlights 5G’s potential to redefine industrial processes while elucidating operational requirements for achieving uRLLC. Empirical testing underscores the benefits of 5G-robotics integration, enhancing efficiency and innovation in advanced manufacturing.

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.

Motion priority optimization framework towards automated and teleoperated robot cooperation in industrial recovery scenarios

In this study, we introduce an optimization framework to enhance the efficiency of motion priority design in scenarios involving automated and teleoperated robots within an industrial recovery context. The increasing utilization of industrial robots at manufacturing sites has been instrumental in reducing human workload. Nevertheless, achieving effective human–robot collaboration/cooperation (HRC) remains a challenge, especially when human workers and robots share a workspace for collaborative tasks. For instance, when an industrial robot encounters a failure, such as dropping an assembling part, it triggers the suspension of the corresponding factory cell for safe recovery. Given the limited capacity of pre-programmed robots to rectify such failures, human intervention becomes imperative, requiring entry into the robot workspace to address the dropped object while the robot system is halted. This discontinuous manufacturing process results in productivity loss. Robotic teleoperation has emerged as a promising technology enabling human workers to undertake high-risk tasks remotely and safely. Our study advocates for the incorporation of robotic teleoperation in the recovery process during manufacturing failure scenarios, which is referred to as “Cooperative Tele-Recovery”. Our proposed approach involves formulating priority rules designed to facilitate collision avoidance between manufacturing and recovery robots. This, in turn, ensures a continuous manufacturing process with minimal production loss within a configurable risk limitation. We present a comprehensive motion priority optimization framework composed of an HRC simulator and a cooperative multi-robot controller to identify optimal parameters for the priority function. The framework dynamically adjusts the allocation of motion priorities for manufacturing and recovery robots while adhering to predefined risk limitations. Through quantitative and qualitative assessments, we validate the novelty of our concept and demonstrate its feasibility.

Robust feature design for early detection of ball screw preload loss

The ball screw is a critical device for precision linear motion control that has widespread applications in industrial robots, computer numerical control (CNC) machines, and high-precision leveling systems, among others. Because high-precision positioning is ensured by the addition of a preload to the ball screw system, it is crucial to detect and monitor the loss of preload at the earliest possible stage of degradation. The degradation process of the ball screw can be characterized in two stages: the initial reduction of preload without backlash, followed by a loss of preload with an emergence of backlash. To explore the change of the ball screw dynamics caused by degradation, a novel fixed cycle feature test (FCFT) is implemented in combination with multi-level mass experiments and a run-to-failure (RTF) test. The relationships of the ball screw dynamics with preload, worktable mass, and axis position are investigated, with a focus on the axial natural frequency as an indicator of preload loss. Experimental results validate the axial natural frequency’s role as a reliable early detector of preload loss for interventive use in a prognostic system.

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.

Target control point position calibration based on coordinate measuring machine and global bundling optimization algorithm.

The target-based coordinate measurement system is primarily composed of a target, a camera, and a computer, relying on image processing to achieve coordinate measurement. It finds extensive applications in spatial positioning and precision measurement, such as industrial manufacturing, robot navigation, and shipbuilding. The accuracy of the target-based coordinate measurement system is highly dependent on the calibration precision of control point positions. A new method based on the calibration of control point positions using a traditional coordinate measuring machine is proposed in this paper. This method does not require the individual calculation of spatial positions for each control point; instead, it utilizes a global bundle algorithm to obtain the positions of all the control points within the field of view, thereby simplifying the calibration process. The simulation results indicate that the key factor influencing the accuracy of visual calibration is the motion range of the measured target within the field of view, and it is positively correlated with calibration accuracy. For a large-sized target, a method of stitching control points within a near-sighted distance is proposed, which achieves higher accuracy compared to full-field calibration without requiring all the control points to be present within the field of view. Experiments demonstrate that using the proposed calibration method, the standard deviations of measurement accuracy of the target-based coordinate measurement system in the x, y, and z directions are 0.01, 0.01, and 0.08mm, respectively.

Kinematics-guided data-driven energy surrogate model for robotic additive manufacturing

With the increase in the usage of industrial robots for manufacturing applications, there will be a corresponding rise in energy consumption. Especially, robotic additive manufacturing (AM) has demonstrated significant potential for working autonomously in hazardous or extraterrestrial settings, as well as for producing large-scale structures. Recent advancements have made it possible for teams of robots to collaborate for printing structures even larger than their sizes together. Utilizing robots efficiently for AM is essential for achieving energy sustainability goals. The literature on energy models for robots and AM processes lacks a comprehensive energy model for robotic AM. To address this gap, a multi-point trajectory-based energy model is proposed for robotic AM. The model is created to help users manufacture parts in an energy-efficient way. These simulations require a significant amount of computation time, which limits their usage to offline purposes only. Hence, we propose two energy surrogate models for real-time predictions: a pure data-driven model and a kinematics-guided data-driven model. The kinematics-guided approach is an enhanced version of pure data-driven model which learns from inverse kinematics solutions along the trajectory to understand the robot’s dynamics and kinematics. There were two test cases conducted. The first test case consisted of paths that were feasible, printable, and within the robot’s reach, while the second test case consisted of a mixture of paths that were both feasible and infeasible. In comparison to the pure data-driven approach, the kinematics-guided approach stood out in both testing cases. Two real-world case studies have been used to demonstrate that the proposed approach can be used in lieu of simulations for real-time applications. Especially in the second case, where the part was placed very close to the robot making a portion of the part infeasible to print, the proposed approach correctly identified that it was not possible to print the geometry due to the robot’s kinematics. The proposed kinematics-guided approach is 195 and 700 times faster than the simulation results, respectively, in two case studies, and thus supporting the use of the proposed approach for real-time applications.

A Convex Optimization Method to Time-Optimal Trajectory Planning With Jerk Constraint for Industrial Robotic Manipulators

A Convex Optimization Method to Time-Optimal Trajectory Planning With Jerk Constraint for Industrial Robotic Manipulators

Vision-based multi-view reconstruction for high-precision part positioning in industrial robot machining

Traditional industrial robot machining typically involves offline programming of machining trajectories, with part positioning accomplished through robotic tools. However, this method has limitations, particularly when dealing with complex workpieces and dynamic environments. To tackle this problem, this paper introduces a vision-based multi-view reconstruction approach for part positioning in industrial robot machining. Multiple cameras capture multi-view images of the part, reconstruct its 3D point cloud, and match it with the CAD model of the part to achieve high-precision positioning. This method effectively translates offline programmed robot trajectories to real-world scenarios, improving the flexibility and adaptability of robot machining. Experimental results confirm the high accuracy and robustness of the proposed vision-based reconstruction method, achieving a relative error of less than 2 mm for 85.7% of points, with an overall root mean square error of 1.09 mm.

Fusion IK: Solving inverse kinematics using a hybridized deep learning and evolutionary approach

Inverse kinematics is a core aspect of robot manipulation. This paper presents an approach to solving Inverse Kinematics (IK) for robots, including articulated industrial ones, combining deep learning with an evolutionary algorithm. Fusion IK passes the manipulator’s target and current joint values into a neural network, the results of which are then used to seed an evolutionary algorithm, Bio IK, to complete the solution of the inverse kinematics problem. Fusion IK allows for solving the position and orientation of the robot while attempting to minimize joint movement times. Comparisons between Fusion IK and its underlying algorithm Bio IK are tested on a six-degree-of-freedom articulated industrial robot as well as a 20-degree-of-freedom robot to explore the move times that Fusion IK produces. The comparisons show that the variations of the Fusion IK algorithm show comparable results to its underlying evolutionary Bio IK algorithm on a six-degrees-of-freedom articulated robot and improvements on a 20-degree-of-freedom robot without any additional hyperparameter tuning. The results show that Fusion IK could be of real value regarding the movement time and the quality of the obtained solutions upon further research, especially with higher degree-of-freedom robots.

A feedforward kinematic error controller with an angular positioning deviations model for backlash compensation of industrial robots

The accuracy of industrial robots, typically on the order of several millimeters, has inhibited their adoption in advanced aerospace manufacturing applications, such as robotic machining. For this reason, extensive investigations have been conducted to develop solutions to improve their accuracy. These solutions can be categorized into offline and online compensation strategies, both of which have advantages and limitations. Offline compensation has been shown to improve the accuracy of industrial robots by two to three orders of magnitude; however, the sensitivity of these solutions to environmental changes and external disturbances can degrade their performance. In contrast, online compensation, which utilizes real-time measurement and compensation, is robust to these changes; however, the performance of these solutions is limited by the causality of time, preventing them from compensating fast changing errors such as backlash. In this work, a novel kinematic modeling strategy, which models the robot’s angular positioning deviations to predict when backlash will occur, is integrated into a novel online kinematic error compensation framework to leverage the advantages of both solutions. By integrating the proposed kinematic model into the online compensation framework, backlash errors can be predicted and preemptively compensated to improve performance. The performance of the combined system was evaluated using ball bar measurements, where it was shown to improve the circularity of the ball bar motion by 25 %.