Joint Error Research Articles

Digital twin-driven virtual commissioning for robotic machining enhanced by machine learning

Robotic machining has been increasingly applied in intelligent manufacturing production lines. Compared with the traditional machine tools, commissioning for robotic machining system (RMS) is particularly important due to the low accuracy of industrial robots (IRs). Traditional site commissioning has large workload and is difficult to handle the multi-source errors. Since digital twin (DT) provides strategies for staying synchronized with the physical entities in whole lifecycle, a DT-driven virtual commissioning (VC) system for RMS is developed in this study to improve machining accuracy and reduce the difficulty of commissioning. Firstly, the framework of DT-driven VC system is designed including several function modules such as interaction, data pre-processing, DT model of RMS (RMSDT), and optimization service. Since RMSDT is the kernel of precise VC, a machine learning-enhanced RMSDT oriented to actual machining path prediction is then constructed based on a proposed joint error equivalent strategy, which can fully consider the coupled multi-source errors of machining robot. After that, a practical consistency retention method for RMSDT is proposed based on a stepwise updating strategy, where the model performance can be maintained with low updating costs. Finally, a visual VC system is developed for the experimental 6-degree of freedom robotic milling platform to verify the feasibility and effectiveness of the VC framework. Multiple experiments are also performed to test the performance of RMSDT and contour error compensation. This study has useful reference for the enterprises engaged in RMS and has positive significance for promoting the robotic machining.

Large data density peak clustering based on sparse auto-encoder and data space meshing via evidence probability distribution

The development of big data analysis technology has brought new development opportunities to the production and management of various industries. Through the mining and analysis of various data in the operation process of enterprises by big data technology, the internal associated data of the enterprises and even the entire industry can be obtained. As a common method for large-scale data statistical analysis, clustering technology can effectively mine the relationship within massive heterogeneous multidimensional data, complete unlabeled data classification, and provide data support for various model analysis of big data. Common big data density clustering methods are time-consuming and easy to cause errors in data density allocation, which affects the accuracy of data clustering. Therefore we propose a novel large data density peak clustering based on sparse auto-encoder and data space meshing via evidence probability distribution. Firstly, the sparse auto-encoder in deep learning is used to achieve feature extraction and dimensionality reduction for input high-dimensional data matrix through training. Secondly, the data space is meshed to reduce the calculation of the distance between the sample data points. When calculating the local density, not only the density value of the grid itself, but also the density value of the nearest neighbors are considered, which reduces the influence of the subjective selection truncation distance on the clustering results and improves the clustering accuracy. The grid density threshold is set to ensure the stability of the clustering results. Using the K-nearest neighbor information of the sample points, the transfer probability distribution strategy and evidence probability distribution strategy are proposed to optimize the distribution of the remaining sample points, so as to avoid the joint error of distribution. The experimental results show that the proposed algorithm has higher clustering accuracy and better clustering performance than other advanced clustering algorithms on artificial and real data sets.

Joint encryption and error correction for secure quantum communication

Secure quantum networks are a bedrock requirement for developing a future quantum internet. However, quantum channels are susceptible to channel noise that introduce errors in the transmitted data. The traditional approach to providing error correction typically encapsulates the message in an error correction code after encryption. Such separate processes incur overhead that must be avoided when possible. We, consequently, provide a single integrated process that allows for encryption as well as error correction. This is a first attempt to do so for secure quantum communication and combines the Calderbank-Shor-Steane (CSS) code with the three-stage secure quantum communication protocol. Lastly, it allows for arbitrary qubits to be transmitted from sender to receiver making the proposed protocol general purpose.

In simple but challenging search tasks, most errors are stochastic.

In visual search tasks in the lab and in the real world, people routinely miss targets that are clearly visible: so-called look but fail to see (LBFTS) errors. If search displays are shown to the same observer twice, we can ask about the probability of joint errors, where the target is missed both times. If errors are "deterministic," then the probability of a second error on the same display-given that the target was missed the first time-should be high. If errors are "stochastic," the probability of joint errors should be the product of the error rate for first and second appearances. Here, we report on two versions of a T among Ls search with somewhat degraded letters to make search more difficult. In Experiment 1, Ts could either appear amidst crowded "clumps" of Ls or more in isolation. Observers made more errors when the T was in a clump, but these errors were mainly stochastic. In Experiment 2, the task was made harder by making Ts and Ls more similar. Again, errors were predominantly stochastic. If other, socially important errors are also stochastic, this would suggest that "double reading," where two observers (human or otherwise) look at each stimulus, could reduce overall error rates.

Positioning Error Estimation Models for Horizontal-Distributed Prismatic–Universal–Universal Parallel Mechanism

Abstract In this paper, two models were proposed to estimate the positioning error of a 3-translational prismatic–universal–universal parallel kinematic mechanism. The two models were a kinematic error model (KEM) and a backpropagation neural network (BPNN) model, respectively. The KEM was constructed by incorporating three translational joint errors into the ideal kinematic model to describe the errors that occur during machining or assembly. Additionally, a sensitivity analysis was presented for each error parameter. The BPNN model was constructed to establish the relationship between the position of the end effector, the posture of each link, and the positioning error of the end effector using a neural network approach. Moreover, a hybrid method was proposed to decrease the final estimated residual error. The average errors of the KEM and BPNN models were 35% and 15% of the original error, respectively. The hybrid model reduced the final average error to less than 10% of its original value.

The study of thermal-structural coupling deformation analysis for flexible space manipulator in orbit

Abstract The space manipulator can assist astronauts to accomplish space activities, including docking, fixing, and grasping. It is subjected to thermal radiation and produces thermal deformation during orbit operation, which makes the operation of the space manipulator deviate from the predetermined trajectory and further affects its positioning accuracy. Therefore, to solve the problem of bidirectional coupling thermal-structure deformation analysis and positioning accuracy for space manipulator, based on the thermal-structural bidirectional coupling deformation analysis, a method of its thermal deformation on the output positioning accuracy of space flexible manipulator is proposed. It analyzes the bidirectional coupling relationship between the temperature and its thermal deformation for the manipulators. Then, the influence of thermal deformation on the output joint error and end positioning accuracy of the space manipulator are analyzed. Finally, the validity of this method is verified by numerical analysis. Compared with the unidirectional coupling model, the bidirectional coupling model comprehensively considers the structure, deformation and temperature of manipulators. It is closer to the real system. Thermal deformation will reduce the reliable runtime of the space manipulator in orbit. The study provides a theoretical basis for its thermal design and control.

Prediction of Human Reaching Pose Sequences in Human–Robot Collaboration

Abstract In human–robot collaboration, robots and humans must work together in shared, overlapping, workspaces to accomplish tasks. If human and robot motion can be coordinated, then collisions between robot and human can seamlessly be avoided without requiring either of them to stop work. A key part of this coordination is anticipating humans’ future motion so robot motion can be adapted proactively. In this work, a generative neural network predicts a multi-step sequence of human poses for tabletop reaching motions. The multi-step sequence is mapped to a time-series based on a human speed versus motion distance model. The input to the network is the human’s reaching target relative to current pelvis location combined with current human pose. A dataset was generated of human motions to reach various positions on or above the table in front of the human starting from a wide variety of initial human poses. After training the network, experiments showed that the predicted sequences generated by this method matched the actual recordings of human motion within an L2 joint error of 7.6 cm and L2 link roll–pitch–yaw error of 0.301 rad on average. This method predicts motion for an entire reach motion without suffering from the exponential propagation of prediction error that limits the horizon of prior works.

HIDE: Hierarchical iterative decoding enhancement for multi‐view 3D human parameter regression

AbstractParametric human modeling are limited to either single‐view frameworks or simple multi‐view frameworks, failing to fully leverage the advantages of easily trainable single‐view networks and the occlusion‐resistant capabilities of multi‐view images. The prevalent presence of object occlusion and self‐occlusion in real‐world scenarios leads to issues of robustness and accuracy in predicting human body parameters. Additionally, many methods overlook the spatial connectivity of human joints in the global estimation of model pose parameters, resulting in cumulative errors in continuous joint parameters.To address these challenges, we propose a flexible and efficient iterative decoding strategy. By extending from single‐view images to multi‐view video inputs, we achieve local‐to‐global optimization. We utilize attention mechanisms to capture the rotational dependencies between any node in the human body and all its ancestor nodes, thereby enhancing pose decoding capability. We employ a parameter‐level iterative fusion of multi‐view image data to achieve flexible integration of global pose information, rapidly obtaining appropriate projection features from different viewpoints, ultimately resulting in precise parameter estimation. Through experiments, we validate the effectiveness of the HIDE method on the Human3.6M and 3DPW datasets, demonstrating significantly improved visualization results compared to previous methods.

Seismic data reconstruction method using generative adversarial network based on moment reconstruction error constraint.

The seismic data acquired are usually spatially undersampled due to the constraints of the field acquisition environment. However, the removal of multiple waves, offsets, and inversions requires high regularity and integrity of seismic data. Therefore, reasonable data reconstruction methods are usually applied to the missing data in the indoor processing stage to recover regular seismic data. The traditional reconstruction methods for seismic data reconstruction are generally based on some assumptions (e.g., assuming that the data satisfies linearity or sparsity, etc.) and have some limitations of use. To overcome the applicability problem of traditional seismic data reconstruction methods, this article proposes a generative adversarial network (GAN) seismic data reconstruction method based on moment reconstruction error constraints. The method can extract the deep features of the data nonlinearly without any assumptions. First, the error function in the GAN is improved, and the commonly used joint error function of adversarial loss plus L1/L2 amplitude reconstruction loss is improved to a new error function consisting of adversarial loss and moment reconstruction loss weighting. Then, an adversarial network data reconstruction generation method based on the moment reconstruction error constraint is given. Next, an experimental analysis of different types of data missing was carried out using theoretical model data, and the study method was analyzed by interpolation errors. Finally, actual seismic data is used to further validate the effect of the research method. The experimental results show that the improved algorithm performs superiorly in dealing with the data reconstruction problem. Compared with the error function of conventional GAN optimization, the reconstruction results of GAN based on the moment reconstruction error constraint have better amplitude preservation.

Trajectory Tracking and Disturbance Rejection Performance Analysis of Classical and Advanced Controllers for a SCORBOT Robot

This work presents the design and assessment of four control schemes for the monitoring and regulation of joint trajectories applied in the dynamic model of a SCORBOT-ER V plus robot, which includes the dynamics of the actuators, and the estimation of the friction forces present within the joints. The two classical control strategies calculated torque and PID, and the two advanced control strategies, fuzzy and predictive, are considered. In the latter case, a gravitational compensation stage is incorporated, as well as the inverse models of the motors and the transmissions of belt movement for each joint. Computational tests are performed by applying an external step-type disturbance to the third joint of the robot. Finally, an evaluation of the results obtained is presented through trajectory curves, joint errors, and the three performance indexes residual mean square, residual standard deviation, and index of agreement.

The Importance of Application of EN ISO 3834 and EN 1090 in Production of Steel Structures

Fractures of welded structures in operational conditions most often occur in the welded joint or its immediate vicinity. Due to this, special attention is paid to ensuring the quality of the welded joint. The application of the EN ISO 3834 and EN 1090 standards in the manufacture of steel structures aims to ensure the highest possible quality of welded joints, and thus the quality of the structures as a whole. Weld quality is achieved by welding, whereas weld quality control provides a check of product reliability. Increasing requirements in terms of reliability, better use of materials, lower weight and cost of constructions, but also high use of load-bearing structural elements, and thus welded joints, leave less room for errors in welded joints, which is why constant control and monitoring of documented procedures is required.

The Use of Digital Media Arts in Animation Filmmaking in the Age of Digital Intelligence

Abstract The development of digital media art has brought a broader application prospect for the production of digital animation movies. The incorporation of Digital Media Arts into Animation Filmmaking during the Age of Digital Intelligence The DTW algorithm is selected to temporally regularize and align the motion data of the animated character, and the motion model is downsized and optimized by combining with the PCA method, to obtain the motion sequence with the characteristics of the animated character itself. The multi-scale method is utilized to decompose the original motion data when combined with motion capture technology to establish a 3D motion database. Analyze the motion connection curves between different video clips and the generated animation data after and before motion modeling using the 3D motion database of broadcast gymnastics. Analyzing the drawing efficiency of virtual character models is done using an ordinary human character model with different voxel accuracy. Before the motion library modeling, the average joint error measured in degrees was 10.012°, as indicated by the data. The modeling procedure results in a reduction of the average joint error to 4.281°. The accuracy of animated character generation motion can be improved by using motion modeling. Character motion generation suggestions for animated movie production are provided in this study.

Joint Error Detection and Correction for Safety Communication: Reducing the Alarm Rate of Transmitted Safety Messages

Joint Error Detection and Correction for Safety Communication: Reducing the Alarm Rate of Transmitted Safety Messages

High Throughput Joint Error Detection and Correction Based On GRAND-MO and CRC

High Throughput Joint Error Detection and Correction Based On GRAND-MO and CRC

Trajectory Tracking Control of Fast Parallel SCARA Robots with Fuzzy Adaptive Iterative Learning Control for Repetitive Pick-and-Place Operations

Aiming at enhanced suppression of external disturbances and high-precision trajectory tracking of parallel SCARA robot dedicating to fast pick-and-place operations, this work presents the integrated control design of iterative learning algorithm, adaptive control and fuzzy rules, namely, fuzzy adaptive iterative learning control, for such type of robots. A step-design approach is adopted to ensure the adaptability of the designed control law, which is reflected in two aspects: ① the feedback gain of the controller is adjusted by the fuzzy rules; ② the adaptive unknown parameters are obtained by means of iterative learning estimation to suppress the uncertainties and external disturbances. The stability of the designed controller is analyzed and proved by the Lyapunov theory, and the effectiveness is verified by observing the tracking errors in joint space along with the testing pick path, in comparison with different iterative learning based algorithms. After the first-iteration learning, the motion errors of the four actuated joints can be reduced by 56.5%, 45.8%, 46.4% and 39.8%, respectively, and after 15 iterations of learning control, the final angular errors by the designed control law converge to 0.7×10−4 degree maximally. The varying maximum, root-mean-squared and mean angular displacement errors of the actuation joints can converge to zero values with the increasing iterations rapidly, which shows the robustness, effectiveness and advantages of the designed control law. The designed control law can be generalized to high-speed parallel pick-and-place robot to ensure high-precision trajectory tracking for high-quality material handling tasks.

A Minimum Joint Error Entropy-Based Localization Method in Mixed LOS/NLOS Environments

In this paper, we address the time-of-arrival (TOA) based source localization problem in mixed light-of-sight (LOS)/non-light-of-sight (NLOS) environments, where localization accuracy is degraded by both measurement noise and NLOS error. A novel exponential optimization problem is formulated based on new minimum joint error entropy criteria and statistical characteristics of measurement noise. After that, a two-step relaxation method is proposed. In the first step, the original problem is relaxed into a non-exponential problem which maintains the consistency of the solution. The second step is to transform the non-exponential problem into a convex problem. Furthermore, we extend the method to asynchronous networks where the source and anchors are not time synchronized. Numerical results show that the proposed method can provide significant robust performance in synchronous or asynchronous networks, whether in sparse or dense NLOS scenarios.

Estimation and compensation of cutting force induced position error in robot machining system

Recently, industrial robots have been widely used in manufacturing due to their various advantages when compared with other equipment, such as maneuverability in a wide range of workspaces, high degrees of freedom, and flexibility. However, due to the low stiffness, they are difficult to be used for precision metal cutting process those require high position control performance under cutting force. This paper proposes a compensation method that estimates the position error induced by the cutting force and compensates the error by controlling the workpiece position by using additional two-axis servo system. The cutting force model was constructed to estimate the cutting force generated during milling without sensors. The kinematic and stiffness models of the industrial robot were developed to calculate the angular error of each joint and position error of the tool center position caused by cutting force. To verify the performance of the proposed compensation method, series of experiments were conducted under various cutting conditions and compared with traditional on-line compensation method. The position accuracy was increased by 63.68 % after the application of the proposed compensation method.

A Comparative Study on Kinematic Calibration for a 3-DOF Parallel Manipulator Using the Complete-Minimal, Inverse-Kinematic and Geometric-Constraint Error Models

Kinematic calibration is a reliable way to improve the accuracy of parallel manipulators, while the error model dramatically affects the accuracy, reliability, and stability of identification results. In this paper, a comparison study on kinematic calibration for a 3-DOF parallel manipulator with three error models is presented to investigate the relative merits of different error modeling methods. The study takes into consideration the inverse-kinematic error model, which ignores all passive joint errors, the geometric-constraint error model, which is derived by special geometric constraints of the studied RPR-equivalent parallel manipulator, and the complete-minimal error model, which meets the complete, minimal, and continuous criteria. This comparison focuses on aspects such as modeling complexity, identification accuracy, the impact of noise uncertainty, and parameter identifiability. To facilitate a more intuitive comparison, simulations are conducted to draw conclusions in certain aspects, including accuracy, the influence of the S joint, identification with noises, and sensitivity indices. The simulations indicate that the complete-minimal error model exhibits the lowest residual values, and all error models demonstrate stability considering noises. Hereafter, an experiment is conducted on a prototype using a laser tracker, providing further insights into the differences among the three error models. The results show that the residual errors of this machine tool are significantly improved according to the identified parameters, and the complete-minimal error model can approach the measurements by nearly 90% compared to the inverse-kinematic error model. The findings pertaining to the model process, complexity, and limitations are also instructive for other parallel manipulators.

Modeling and solving of temperature field of screw–nut pair based on geometric error distribution probability

Random geometric errors are inevitable during the machining process of the screw raceway, ball and nut raceway, which makes the contact state between the ball and raceway diverse, further leading to the generation of nonequal strength heat sources inside the screw–nut pair. In the past, when studying the temperature field of the screw–nut pair, it was assumed that all the balls were evenly loaded. To achieve more accurate temperature field modeling, the modeling of temperature field of the screw–nut pair based on geometric error distribution probability was studied. First, a quantitative relationship between geometric error and contact angle are established. Second, the axial force generated by ball and raceway with different joint geometric errors is determined when subjected to axial loads. Then, on the basis of geometric error statistics, the relationship between the error probability distribution and the heat source intensity distribution is connected, and the corresponding temperature field is solved by the finite difference method. Finally, the probabilistic temperature distribution theory is verified indirectly by a multi-axis thermal deformation experiment. The probabilistic temperature distribution model shows the dynamic change process of the temperature field with the probability of geometric error distribution and has guiding significance for the temperature field modeling of the screw–nut pairs with different precision. When the joint error presents a rectangular distribution, the maximum intensity heat source will be generated, and the ultimate temperature field will be induced.

Sequential calibration of transmission ratios for joints of 6-DOF serial industrial robots based on laser tracker

PurposeIn traditional calibration methods of kinematics parameters of industrial robots, dozens of model parameters are identified together based on an optimization procedure. Due to different contributions of model parameter errors to the tool center point positioning error of industrial robots, obtaining good results for all model parameters is very difficult. Therefore, the purpose of this paper is to propose a sequential calibration method specifically for transmission ratio parameters, which includes reduction ratios and coupling ratios of industrial robot joints.Design/methodology/approachThe ABB IRB 1410 industrial robot is considered as an example in this study. The transmission ratios for each joint of the robot are identified using the spatial circle fitting method based on spatial vectors, which fit the center and radius of joint rotation with the least squares optimization algorithm. In addition, a method based on the Rodrigues’ formula is designed and presented for identifying the actual coupling ratio of the robot. Subsequently, an experiment is carried out to verify the proposed sequential calibration method of transmission ratios.FindingsIn this experiment, the actual positions of the linkages before and after joint rotations are measured by a laser tracker. Accurate results of the reduction ratios and the coupling ratios are calculated, and the results are verified experimentally. The results show that by calibrating the reduction ratios and coupling ratios of the ABB robot, the rotation angle errors of the robot joints can be reduced.Originality/valueThe authors propose a sequential calibration method for transmission ratio parameters, including reduction ratios and coupling ratios of industrial robot joints. An experiment is carried out to verify this proposed sequential calibration method. This study may be beneficial for calibrating the kinematic parameters of industrial robots and improving their positioning accuracy.