Industrial Robot Joint Research Articles

Optimization for industrial robot joint movement in non-horizontal 3D printing application

When a robot is printing a sequence of non-horizontal goal poses, its joint values often undergo significant variations, resulting in challenges such as singularities or exceeding joint limits. This paper proposes two new methods aimed at optimizing goal poses to solve the problem. The first method, employing an analytical approach, modifies the goal poses to maintain the 4th joint value of a 6-axis industrial robot at zero. This adjustment effectively reduces the motion range of the 5th and 6th axes. The second method utilizes numerical optimization to adjust the goal poses, aiming to minimize the motion range of all joints. Leveraging the analytical method to obtain one good initial value, numerical optimization is subsequently applied to complete the entire path optimization, creating an optimization workflow. It is also possible to use only analytical methods for computational efficiency. The feasibility and effectiveness of these two methods are validated through simulation and real project case.

Model Prediction and Optimization of Belt Drive System Vibration Suppression Based on Industrial Robot Joints

Addressing the vibration issues caused by torsional angle changes in the belt transmission systems of industrial robots during practical applications, this paper introduces a control strategy that integrates a Model Predictive Control (MPC) compensation mechanism. By applying the Lagrangian method, a dynamic mathematical model correlating torsional angle and torque was established, and an algorithm design combining MPC with its compensatory controller was developed. This strategy was validated in a MATLAB simulation environment. Simulation results demonstrate that, compared to traditional sliding mode control, the newly proposed controller significantly improved response speed in tracking the torsional angle's position and angular velocity, achieving enhancements of approximately 2 seconds and 1 second, respectively. This led to higher tracking accuracy and faster convergence speed, effectively enhancing the vibration suppression performance of industrial robot joint belt transmission systems.

A novel Bayesian optimization prediction framework for four-axis industrial robot joint motion state

Robot joints are the main structure for controlling the motion of the machine body, where the motion state of them directly affects the performance of the industrial robot. Due to the difficulty of obtaining the joint torque information of industrial robots, it is very hard to monitor the motion state of them. Based on the velocity and force driven by current of motors, we propose a novel Bayesian optimization framework to predict the joint motion state of industrial robot in this paper. Based on the temporal correlation of joint current and the correlation between the current and motion state of joint, we use the LSTM and BiLSTM to regressing prediction of the current and state of joint motor first. Then, the Bayesian optimization method is used to adjust the hyperparameters of our network, which realize the analysis of the joint motor current under different motion states and improve the accuracy of the prediction of joint motion states. Finally, we design the joint current acquisition platform of industrial robot based on Hall current sensors, which can collect joint currents without contact and generate experimental dataset. Comparing with the popular intelligent methods, the results show that our Bayesian optimization framework realizes a more accurate prediction of motion state for the four-axis industrial robot on the basis of contact-less current acquisition.

Vibration characteristics of industrial robot joint servo transmission system based on electromechanical coupling

The complex electromechanical coupling effect of an industrial robot’s electromechanical system significantly influences the dynamic characteristics and stability of the joint servo transmission system. This study proposes a kind of electromechanical coupling dynamic model for the industrial robot’s joint servo transmission system, incorporating multiple parameters, and investigates the vibration response characteristics of the system. Firstly, an electromechanical coupling analysis of the industrial robot’s joint servo transmission system is conducted, simplifying it as a ‘motor-reducer-load’ system and establishing the corresponding electromechanical coupling vibration mathematical model. Secondly, a dynamic model of the robot’s joint servo transmission system is developed to analyze its speed characteristics. Finally, the model is used to analyze the electromechanical coupling vibration process of the joint servo transmission system with the introduction of mechanical and electrical parameters and other factors to obtain the vibration characteristics of the industrial robot joint servo transmission system under electromechanical coupling. The experimental results show that this model can better analyze the electromechanical coupling vibration characteristics of the joint servo transmission system, which is significant for improving the performance and fault traceability of the industrial robot joint transmission.

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.

A graph neural network-based data cleaning method to prevent intelligent fault diagnosis from data contamination

The success of deep learning (DL) based-mechanical fault diagnosis hinges on the high quality of training data. However, it is difficult to acquire high-quality mechanical monitoring data due to data contamination: 1) Monitoring device irregularities, such as sensor malfunction and signal transmission disruption, bring anomalies into the training data; 2) human labour-based data annotation inevitably produces incorrectly labeled data. These two types of data contamination degrade the performance of DL models. To address the aforementioned issue, this paper proposes a graph neural network-based data-cleaning method. In the first stage, a group anomaly detector is designed to identify the presence of anomalous data. This detector incorporates affinity graphs for depicting data groups and subsequently calculates the group anomaly score to determine the abnormal group. In the second stage, a graph clustering model is developed to relabel the mislabeled data. This model takes advantage of the graph neural network's proficiency in handling affinity graphs to prepare clean labels for subsequent network training. Experimental results, conducted on a pump and an industrial robot joint reducer, show the proposed method's ability to effectively detect anomalous data and rectify incorrect labeling, surpassing the performance of baseline methods in mechanical fault diagnosis.

Dynamic modeling and vibration analysis for defect identification of single-stage gearboxes in the joints of industrial robots with six DOF

In the age of the future industry, many industrial activities have been entrusted to robots due to their ability to improve both accuracy and production. However, during the manufacturing process, the occurrence of defects in the manipulators transmissions can lead to a significant degradation of the product quality, and consequently financial losses. Hence, this paper aims to present a novel methodology to elaborate a robot dynamic model allowing the detection of the defects arising from the transmissions and more precisely the gears faults. First, a geometric model of the robot was developed based on the Modified Denavit-Hartenberg (MDH) approach, to derive the transfer matrices required to model the different components of the robot. Secondly, thanks to these matrices, to the Euler-Lagrange convention and taking into account the assumption of the joint flexibility, the deduction of the global equation of motion of the robot has been performed. The ability of the developed model to detect defects will then be investigated by a series of numerical simulations performed in the case of healthy gears and in the case where a tooth-cracking fault has been introduced, and also under various velocities. Finally, an experimental study and vibration analysis were conducted to validate the results of the numerical simulations and consequently the effectiveness of the proposed model.

Instantaneous Angular Speed Extraction Based on Nonuniform Local Polynomial Differentiator for the Stiffness Identification of the Robot Joint

In the identification of industrial robot joint system, in order to obtain the ideal frequency response of the system, additional excitation needs to be applied. The addition of excitation signals, such as chirp signals and pseudorandom binary signals, will make the instantaneous angular speed (IAS) obtained by the motor encoder present nonstationary characteristics. In order to solve this problem, this article proposes a method for system identification by IAS extraction under nonstationary conditions. Interferences, such as electrical perturbation, quantization error, and background noise, generally reduce the accuracy of extracted IAS. First, the influence of electrical perturbation on the accuracy of angle calculation is eliminated by utilizing the characteristics of the two-phase encoder signals. Second, considering the time-varying characteristics of the rotational speed in the identification process, a nonuniform local polynomial differentiator (NULPD) is proposed to optimize the estimation of IAS and reduce the influence of background noise and sampling error based on local polynomial differentiator (LPD). The IAS estimation under the condition of variable speed is simulated to verify the performance of NULPD. According to the transmission characteristics of the robot joint system, a two-mass dynamic model is established, and the transfer function between the torque and motor speed is obtained. The torque is calculated from the three-phase current signals, and the rotational speed is obtained by the encoder signal. The frequency sweep experiment is carried out on the single-degree-of-freedom articulated arm test bench, where the stiffness and inertia of the system are identified within a certain error range by the proposed method.

Design and dynamic transmission error analysis of a new type of cycloidal-pin reducer with a rotatable output-pin mechanism

A new type of one-stage precision cycloidal-pin reducer is designed and explored in this study. The design innovatively replaces the fixed output-pin mechanism of conventional cycloidal-pin reducers with a rotatable output-pin mechanism, thereby transforming the sliding contact between the output-pin and the output-hole into a rolling contact. This new structural design is found to significantly improve the transmission efficiency of the reducer. To evaluate the dynamic transmission error (DTE) characteristics of the proposed reducer, a general dynamic model of the cycloidal-pin transmission system considering the influences of multi-source error factors on the geometry of the main components is established. The geometric error of the component is determined by taking random values within a given design-tolerance zone. The geometric error distribution introduced using this method can well match the actual machining situation. Finally, the DTEs of the new reducers with three different tolerance designs are analyzed numerically and tested experimentally. The results show that the new reducer with a reasonable tolerance design can achieve high transmission accuracy and has the potential for application in the joints of industrial robots.

Open circuit fault diagnosis strategy of PMSM drive system based on grey prediction theory for industrial robot

Permanent magnet synchronous motor (PMSM) is widely used in industrial robot joints and machine tools due to its high torque density, good controllability, and compact structure. Automatic fault diagnosis and early warning of PMSM drive system of industrial robot is the key and difficult point for the smooth development of automatic production process. In view of the problems that the traditional open circuit fault diagnosis method is not fast enough, not intelligent enough, and prone to misdiagnosis in the case of load change, an open circuit fault diagnosis and location strategy for windings and power switches of PMSM drive system based on grey prediction theory is proposed. The grey prediction method only needs to collect a small amount of motor current data and uses the grey model to estimate and predict the rule of the system. It can still diagnose and locate faults accurately in the case of load change, and compared with the traditional current detection method, the diagnosis speed is fast, and the accuracy is high. Through the theoretical analysis, simulation, and experimental results, it is verified that proposed strategy can diagnose the power switch and winding open circuit faults accurately in real time, which improves the reliability of the electric drive system in the application of industrial robot.

Electromechanical coupling modeling and motor current signature analysis of bolt loosening of industrial robot joint

Since loosening of the bolt connection reduce the stability and working accuracy of industrial robot joint, developing and applying new detection methods to provide effective and reliable diagnosis is still a challenging task. In this study, a detection method for bolt loosening of industrial robot joint based on electromechanical modeling and motor current signature analysis (MCSA) is proposed. Firstly, a dynamics model is established based on the electromechanical coupling characteristics of the robot joint servo system, and the bolt loosening factor in the model is equivalent to the variations of support stiffness. The vibration performance of the system due to bolt loosening is analyzed, and the dynamical equations show the coupling relationship between the motor current and bolt loosening. Then according to the traits of motor current variation, this study proposes to utilize the time–frequency features of the motor current to detect loosening. The energy concentrated time–frequency representation (TFR) is obtained by the synchrosqueezing transform (SST) method, and then time–frequency ridges are extracted. The features of the time–frequency ridges are adopted as indicators of the bolt loosening. The support vector machine (SVM) classifier is utilized to identify the loosening of bolts. The simulated motor current signal analysis shows that the SST can highlight the current transients caused by bolt loosening. The bolt loosening experiment is conducted on the single-degree-of-freedom servo joint test bench, and the results prove the effectiveness of the proposed method.

Modelling of an Industrial Robot Joint Transmission in Presence of Gear Defect

Modelling of an Industrial Robot Joint Transmission in Presence of Gear Defect

DEVELOPMENT AND STUDY OF THE OPERATION OF THE MODULE FOR DETERMINING THE ORIENTATION OF THE MANIPULATOR JOINT

In the field of mechatronic systems, manipulators are often used for automated assembly of products, welding, painting parts and more. An important task is to optimize the travel time along a given trajectory of the manipulator. To solve this problem, it is necessary not only to accurately estimate the speed of the manipulator nodes, but also to provide a linear characteristic of the assessment of the position of the mechanism in a wide range of speeds. The matter of the article are methods for determining the orientation of the joint of the manipulator. The goal of the work is to develop a module for determining the orientation of the joint of the manipulator and study its operation in order to determine the suitability subject of the structure for practical use. The following tasks are solved in the article: to investigate the principles of determining the orientation of the joints of industrial robots; choose the design of the orientation determination module; develop an algorithm for determining the position of the joint at any time; perform experimental studies of the position determination module in order to confirm the suitability of the structure for practical use. The following methods used are: experimental research was conducted on a real object - a model of the manipulator joint, created using methods and tools of 3D prototyping; to determine the position of the joint of the manipulator used methods of processing signals received from sensors; processing of experimental results and calculation of values of errors of positioning of a joint of the manipulator is based on methods of the statistical analysis of random sizes. The following results were obtained: the principles of determining the orientation of the joints of industrial robots were studied; the design is developed and the module of definition of orientation of a joint of the manipulator is created; developed an algorithm for determining the position of the joint at any time; the suitability of the design for practical use has been experimentally confirmed. Conclusions: in this paper, two variants of the sensor design are proposed to determine the absolute angle of rotation of the manipulator joint: resistive and magnetic. The proposed design of the resistive sensor was non-technological and much larger than the design of the magnetic sensor. The data obtained in the process of conducting experimental studies of the proposed method of measuring the angle of rotation of the mechanical gearbox of the manipulator joint indicate a fairly accurate determination of the angle using a magnetic sensor. The calculated measurement error was less than 1.4 degrees. The results of the experiment also showed that in addition to the radial direction of movement of the gearbox of the manipulator joint there is a significant displacement along the working plane, and in some cases, such displacements are chaotic. This is due to some defects and imperfections of the surface of the manufactured parts of the joint model used in research.

Analytical modelling of No‐load magnetic field for equivalent poles of a new type of double‐stator PM poles spherical motor

A double-stator swing rotating permanent magnet spherical motor (SR-PMSM) is designed, which can realize rotation, swing and spiral motion, and is used in the multi-degree-of-freedom motion fields of industrial robot joints, torque gyroscopes, panoramic photography tables and so on. The inner surface of the rotor of the motor is attached with cylindrical PM poles, and the outer surface is attached with trapezoidal PM poles. Cylindrical and trapezoidal PM poles are simple to manufacture, but the magnetic field model of a spherical motor with two kinds of PM poles is complex. Therefore, a magnetic field modelling method combining the geometric equivalence principle and analytical method is proposed. Using the method of shape approximation, according to the principle of equivalence, the key parameters of cylindrical PM poles and trapezoidal PM poles are replaced by the parameters of approximate dihedral PM poles in a spherical coordinate. Select the most suitable dihedral PM poles, establish the analytical model of the magnetic field in the spherical coordinate system and verify the analytical results by FEM method. A SR-PMSM motor with 6/4 pole (swing) and 12/10 pole (rotation) structure was assembled, and the experimental device was set up. The experimental results show that the experimental curve, analytical curve and FEM curve are in good agreement, which proves the accuracy of the equivalent model and is suitable for the magnetic field model of cylindrical and trapezoidal PM poles of the spherical motor. The research results of magnetic field lay a foundation for further analysis of the stator–rotor composite magnetic field considering cogging effect and edge effect, and analysis of load characteristics of the motor.

Analytical calculation of magnetic field of double‐stator permanent magnet spherical motor considering cogging effect and edge effect

A new type of double-stator permanent magnet spherical motor (DSSRPMSM) is proposed. Compared with most existing spherical motors, this motor uses a large number of stator iron cores, which can generate larger magnetic flux density and significantly improve torque capacity. The proposed DSSRPMSM can continuously rotate, swing and spiral, and can be used in the multi-degree-of-freedom motion fields of industrial robot joints, moment gyroscopes, panoramic photography tables and so on. Because the motor has the dual characteristics of rotary motor and arc linear motor, the comprehensive effects of stator slotting and stator iron core breaking should be considered. Cylindrical and trapezoidal PM poles are equivalent to dihedral PM poles, and stator coils are equivalent to PM pole models. Analytical models of PM pole magnetic field and stator magnetic field in a spherical coordinate system are established, and the air gap magnetic flux density under unequal magnetic potential boundary conditions is calculated by conformal transformation. The analytical calculation method proposed is consistent with the results of finite element method (FEM) simulation, and the experimental test further verifies the effectiveness and accuracy of this method.

Tooth root crack detection of planet gear in RV reducer based on improved compute order tracking and angular domain synchronous averaging

Abstract Rotate vector (RV) reducers are widely used as the joints of industrial robots. Due to RV reducers often work in a reciprocating condition, it makes the corresponding vibration for fault detection is incomplete since the incompletely rotation of the output shaft. Thus, traditional methods such as the vibration separation (VS) cannot be used to detect the tooth fault of planet gear in RV reducer. Moreover, few literatures reported the tooth fault detection of RV reducer without disassembly. To address this issue, a method for detecting the planet gear tooth root crack fault of RV reducers is proposed in this paper. It combines the improved compute order tracking (ICOT), the signal extraction and reconstruction of planet gear, and the synchronous averaging (SA). The ICOT is utilized to obtain data blocks of each tooth of the gear on the reference shaft while eliminating the inevitable speed fluctuations. Subsequently, the tooth mapping relationship between the planet gear and sun gear can be calculated by the planetary meshing structure. Then, the data block of each tooth of the planet gear can be extracted from the resampled vibration. Next, data blocks of the planet gear can be constructed into a synthetic signal according to the tooth mapping relationship. Furthermore, the SA is applied to enhance the signal-noise ratio (SNR) of the synthetic signal. Finally, spectral analysis is utilized to expose the fault characteristics. The comparative experiment with the normal and faulty planet gear in an RV reducer test rig validated the effectiveness of the proposed method.

Intelligent Fault Diagnosis for Bearings of Industrial Robot Joints Under Varying Working Conditions Based on Deep Adversarial Domain Adaptation

Industrial robots are one of the most typical machines in smart manufacturing systems. Their joint bearing faults account for a significant portion of failures. Data-driven bearing fault diagnosis methods, especially deep learning methods, have become a research hotspot due to the development of the industrial Internet of Things and big data. However, the varying working conditions of industrial robots, such as the continuous changing of load and speed, challenge the existing data-driven methods. Although adversarial-based domain adaptive methods are promising for solving this problem, they still face an equilibrium issue in the model training process. Therefore, a novel deep perceptual adversarial domain adaptive (DPADA) method is proposed for fault diagnosis of industrial robot bearings under varying conditions in this article. Here, a novel perceptual loss is proposed to force the target domain and the source domain to have the same distribution, which helps to improve the stability of adversarial training. Correspondingly, a timestamp mapping-based vibration signal screening method is proposed to improve data preprocessing efficiency for fault diagnosis of industrial robots. Extensive experimental results show that the accuracy of DPADA is superior to convolutional neural network (CNN) and conditional domain-adversarial network (CDAN)-based methods. A comparison is further performed on transfer tasks in three classical transfer scenes of industrial robots.

Research on Space Motion Trajectory Optimization of the Industrial Robot

In this paper, an online iterative learning planning method is proposed for industrial robot joint space manipulator to capture moving objects. The dynamic mathematical model of the robot is established, and the trajectory of the robot manipulator is tracked and adjusted through the path planning algorithm to change the visual impedance of the robot and optimize the system parameters. It makes the velocity direction of the end effector of the manipulator consistent with the tangent direction of the weld, and realizes the tracking and recognition of the weld trajectory at the end of the manipulator, so as to improve the accuracy and reliability of the robot in capturing moving objects. The simulation results show that the algorithm has good convergence and robustness.

Passivity Guaranteed Dynamic Friction Model With Temperature and Load Correction: Modeling and Compensation for Collaborative Industrial Robot

In this paper, a new comprehensive dynamic friction model for a collaborative industrial robot joint that considers the velocity, temperature, and load torque is proposed. The variation of load-dependent friction among the four-quadrant operation depending on the sign of load-torque and speed is studied. The new model’s passivity property is analyzed to obtain a physically meaningful and experimentally identified friction model. A sufficient condition is presented in terms of a simple algebraic inequality involving the parameters of the model. The model parameter identification procedure and validation of model effectiveness are demonstrated experimentally on a commercial collaborative robot manipulator. Moreover, the proposed friction model’s benefits are demonstrated in two different robot applications: friction compensation and direct teaching (smooth lead-through programming) applications. Significant tracking performance improvement in the root-mean-square errors up to 76% was achieved with the proposed friction model compared to the uncompensated cases in the friction compensation application. In the direct teaching application, the new model, which precisely estimates joint friction, results in a significant decrease in interaction forces up to 66%. These experimental results validate the performance of the proposed friction model.

Method of Postures Selection for Industrial Robot Joint Stiffness Identification

Industrial robots are being widely applied to machining operations, and are gradually becoming competitive with traditional CNC machining centers. Obtaining accurate stiffness values of robotic joints is the foundation for deflections compensation in case of large cutting forces. A number of factors influence the accuracy of joint stiffness identification, especially robotic posture. This paper proposes a robust and accurate method for selecting suitable postures in the joint stiffness identification. The identification process of the joint stiffness matrix is presented, an index considering both the dexterity and the condition number of the observation matrix is then developed, and the procedure for postures selection based on it is provided. The results of simulations and experiments show that the proposed method is more robust and accurate than classical method.