Industrial robot precision motion control method and system

Abstract

This application provides a precision motion control method and system for industrial robots, relating to the field of industrial robot technology. It comprehensively senses the operating status by collecting multi-source signals such as joint encoder trajectory deviation, drive current fluctuations, and broadband micro-vibrations; analyzes the dynamic correlation of signals to pinpoint the main cause of thermal deformation, overcoming the limitations of traditional error source differentiation by separating thermal deformation and mechanical wear characteristics from the aliased spectrum; for thermal deformation, it generates trajectory compensation commands based on intensity, providing real-time, imperceptible compensation for dynamic errors; for mechanical wear, it triggers a graded maintenance strategy. This application solves the problem of decreased end-effector positioning accuracy caused by thermal deformation and mechanical wear, achieving precision control, improving positioning accuracy, production efficiency, and equipment lifespan, combining technological advancement with practical value.

Description

Technical Field

[0001] This application relates to the field of industrial robot technology, and more specifically, to a method and system for precision motion control of industrial robots. Background Technology

[0002] In the field of modern high-precision manufacturing, industrial robots are the core of achieving micron-level precision assembly. However, under continuous high-speed operation, the robot generates heat due to friction in its internal motors and reducers, causing minute thermal deformation of the structure. This directly results in positioning drift of the end effector, with an error of up to 0.5 millimeters, which seriously restricts precision production.

[0003] Thermal errors are time-varying and nonlinear, and their core challenge lies in the fact that uneven heat distribution causes uneven deformation of the robot arm, disrupting the inherent geometric parameters of the kinematic model. Traditional controllers, however, still rely on fixed parameters for calculations, resulting in deviations between the actual pose and the theoretical target. This type of error is particularly prominent in scenarios such as semiconductor packaging, where even deviations of tens of micrometers can lead to assembly failure.

[0004] Current error compensation techniques mainly rely on two types of methods: one is model-based predictive compensation, which establishes a thermal error-temperature mapping relationship through temperature sensors (such as Pt100), and constructs a digital model using neural networks, multibody kinematics, or finite element analysis for inverse control; the other is online measurement feedback compensation, such as using laser rangefinders or vision sensors to detect the end-effector position in real time, and dynamically updating kinematic parameters through parameter identification (such as the improved MDH model). Studies have shown that these methods can improve the robot's repeatability accuracy to ±0.03 mm, and control the end-effector error within 0.1 mm.

[0005] Future trends focus on dynamic adaptive compensation systems, which integrate digital twins, multiple sensors (such as infrared cameras and strain gauges), and extrapolation prediction algorithms (such as Kalman filtering) to achieve real-time prediction and suppression of thermal errors, driving precision manufacturing toward higher stability and adaptability. Summary of the Invention

[0006] This application provides a precision motion control method and system for industrial robots, aiming to solve the problem that the internal heat accumulation of industrial robots during long-term continuous high-speed operation causes slight structural deformation, which in turn affects the positioning accuracy of the end effector.

[0007] On the one hand, this application provides a precision motion control method for an industrial robot, comprising: The robot joints are equipped with multi-source motion feature signals, including trajectory deviation signals fed back by the joint encoder, drive current fluctuation signals, and broadband micro-vibration signals. Analyze the dynamic correlation of the multi-source motion characteristic signals to identify target signal patterns strongly correlated with thermal deformation; Based on the target signal pattern, and considering the difference in the physical coupling mechanism between the trajectory deviation signal and the broadband micro-vibration signal, thermal deformation characteristics and mechanical wear characteristics are separated from the aliasing spectrum, wherein the aliasing spectrum is formed by the kinematic deviation signal and the broadband vibration signal; For the thermal deformation feature, a motion compensation command including trajectory compensation is generated based on the thermal deformation intensity; and for the mechanical wear feature, a graded maintenance strategy matching the wear degree is triggered.

[0008] Optionally, for the thermal deformation feature, the step of generating a motion compensation command including trajectory compensation based on the thermal deformation intensity includes: When thermal deformation features are identified, the pose deviation of the robot end effector is quantified based on the thermal deformation intensity. The trajectory compensation amount is calculated based on the pose deviation, and the motion compensation command containing the trajectory compensation amount is injected into the trajectory planning so as to adjust the driving torque in the servo control. The motion compensation command is dynamically generated through a thermal deformation-pose offset mapping model, which is established based on the calibration of robot rigid body transformation parameters.

[0009] Optionally, the step of analyzing the dynamic correlation of the multi-source motion characteristic signals and identifying target signal patterns strongly correlated with thermal deformation further includes: The acquired multi-source motion feature signals are time-stamp aligned and preliminarily filtered to obtain motion feature vectors. The dynamic correlation between the moving feature vectors is calculated in real time, and the moving feature vectors are compared with the predefined physical phenomenon relationship patterns to identify target signal patterns that are strongly correlated with thermal deformation.

[0010] Optionally, the step of triggering a graded maintenance strategy matching the degree of wear for mechanical wear characteristics includes: Adaptive suppression of instantaneous high-frequency vibrations generated by identified high-speed motion; and Gated management is implemented for persistent mid-to-high frequency vibrations caused by identified wear.

[0011] Optionally, the step of gating and managing the identified persistent mid-to-high frequency vibrations caused by wear includes: The storage wear feature evolution path describes the dynamic change pattern of vibration frequency, vibration amplitude, harmonic structure and its correlation with the robot's motion state during the wear process; The frequency, amplitude, and harmonic composition of the wear vibration signal in the motion feature vector are tracked in real time, and the nonlinear correlation between the wear vibration signal and the robot's current motion state parameters is dynamically calculated to obtain the real-time wear vibration characteristics. The real-time wear vibration characteristics are compared and matched with the wear feature evolution path in real time to identify the current evolution stage of wear and predict its future trend; and Based on the current stage of wear evolution and predicted trends, the processing strategy for wear vibration signals is adjusted in real time.

[0012] Optionally, the step of adjusting the processing strategy for wear vibration signals in real time based on the current wear evolution stage and predicted trend includes: The vibration frequency, vibration amplitude, and harmonic structure composition during the wear process are analyzed in real time, and the nonlinear coupling relationship between the frequency, amplitude, and harmonic composition is dynamically calculated. Assess the extent to which the nonlinear coupling relationship affects the positioning accuracy of the robot's end effector; Based on the evaluation results, the monitoring threshold and alarm triggering conditions for the wear vibration signal are dynamically adjusted. Furthermore, based on the evaluation results, the motion trajectory optimization parameters of the affected joints are dynamically adjusted.

[0013] Optionally, the step of dynamically adjusting the motion trajectory optimization parameters of the affected joint based on the evaluation results includes: The storage of multi-joint wear coupling influence modes describes the nonlinear interaction between the wear vibration characteristics and the corresponding motion trajectory optimization parameters of multiple joints when wear occurs simultaneously. The vibration frequency, vibration amplitude, and harmonic structure composition of multiple joints in the motion feature vector are tracked in real time during the wear process, and the nonlinear coupling relationship between these wear vibration signals and between them and the robot's current motion state parameters is dynamically calculated to obtain the real-time multi-joint wear coupling characteristics. The real-time multi-joint wear coupling characteristics are compared and matched with the multi-joint wear coupling influence mode in real time to identify the current coupling effect type and intensity of multi-joint wear; and Based on the identified coupling effect type and intensity, the acceleration / deceleration curves and velocity limiting factors of the affected joints are dynamically adjusted.

[0014] Optionally, the step of dynamically adjusting the acceleration / deceleration curves and velocity limiting factors of the affected joints based on the identified coupling effect type and intensity includes: When a robot performs a complex multi-path switching task, it calculates the transition smoothness requirements of acceleration / deceleration curves and speed limit factors between path segments based on the wear coupling characteristics of the current path segment and the estimated wear coupling characteristics of the next path segment. Before the path switching point, a gradual adjustment mechanism is activated. Based on the transition smoothness requirements, this mechanism, within a preset transition time, adjusts the acceleration / deceleration curves and speed limit factors of the current path segment to those of the next path segment using a non-linear gradual change. Ensure that the rate of change of speed and the rate of change of acceleration of the robot end effector remain within the preset threshold during the transition period.

[0015] Optionally, the step of ensuring that the rate of change of velocity and the rate of change of acceleration of the robot end effector remain within a preset threshold during the transition includes: Real-time sensing of the workpiece's material, surface roughness, or viscous fluid environment characteristics to obtain the workpiece's environmental characteristics; Based on the workpiece environment characteristics, the optimal velocity change rate and acceleration change rate threshold of the robot end effector under the current operation are dynamically calculated to obtain the optimal combination of motion thresholds. During the transition, the rate of change of velocity and the rate of change of acceleration of the robot end effector are monitored in real time and compared with the optimal combination of motion thresholds; When the detected rate of change of velocity or acceleration exceeds the optimal motion threshold combination, fine-tuning control is initiated to adjust the joint drive torque and trajectory planning parameters, bringing the rate of change of velocity and acceleration back within the optimal motion threshold combination.

[0016] On the other hand, this application also provides a precision motion control system for an industrial robot, comprising: The signal acquisition module is used to acquire multi-source motion characteristic signals of the robot joints, including trajectory deviation signals fed back by the joint encoder, drive current fluctuation signals, and broadband micro-vibration signals. The pattern matching module is used to analyze the dynamic correlation of the multi-source motion feature signals and identify target signal patterns that are strongly correlated with thermal deformation. The decoupling operation module is used to separate thermal deformation features and mechanical wear features from the aliasing spectrum based on the difference in the physical coupling mechanism between the trajectory deviation signal and the broadband micro-vibration signal according to the target signal pattern, wherein the aliasing spectrum is formed by the kinematic deviation signal and the broadband vibration signal; The dynamic compensation module is used to generate motion compensation commands including trajectory compensation amounts based on the thermal deformation intensity for the thermal deformation characteristics; and to trigger a graded maintenance strategy that matches the degree of wear for the mechanical wear characteristics.

[0017] This application relates to a precision motion control method and system for industrial robots. By acquiring multi-source motion characteristic signals of robot joints, including trajectory deviation signals fed back from joint encoders, drive current fluctuation signals, and broadband micro-vibration signals, the system can comprehensively perceive the robot's operating status. By analyzing the dynamic correlation of these multi-source signals and identifying target signal patterns strongly correlated with thermal deformation, this application can accurately locate key factors leading to decreased accuracy. Based on this, this application effectively separates thermal deformation characteristics and mechanical wear characteristics from the aliased spectrum, overcoming the limitation of traditional methods in distinguishing different error sources, based on the difference in the physical coupling mechanism between trajectory deviation signals and broadband micro-vibration signals. For the separated thermal deformation characteristics, this application can generate motion compensation commands containing trajectory compensation based on the intensity of thermal deformation, achieving real-time, imperceptible compensation for dynamic errors caused by thermal deformation, thereby significantly improving the positioning accuracy of the robot's end effector. Simultaneously, for mechanical wear characteristics, this application can trigger a graded maintenance strategy matched to the degree of wear, effectively extending the robot's service life and reducing maintenance costs. In summary, the method of this application can effectively solve the problem of decreased robot positioning accuracy caused by thermal deformation and mechanical wear in the prior art, realize precise control of robot motion, improve production efficiency and product quality, and has significant technological progress and practical value. Attached Figure Description

[0018] To illustrate this application more clearly, the accompanying drawings used in the embodiments will be briefly described below. Obviously, those skilled in the art can obtain other drawings based on these drawings without any creative effort.

[0019] Figure 1 An exemplary flowchart of a precision motion control method for an industrial robot is shown. Figure 2 An exemplary schematic diagram of a precision motion control system for an industrial robot is shown.

[0020] Reference numerals: 100, Precision motion control system for industrial robot; 10, Signal acquisition module; 20, Pattern matching module; 30, Decoupling operation module; 40, Dynamic compensation module. Detailed Implementation

[0021] The technical solutions of this application will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are merely some embodiments of this application, and not all embodiments. The components of this application described and shown in the accompanying drawings can generally be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of this application provided in the accompanying drawings is not intended to limit the scope of the claimed application, but merely to illustrate selected embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without inventive effort are within the scope of protection of this application.

[0022] It should be noted that similar reference numerals and letters in the following figures indicate similar items; therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures. Furthermore, in the description of this application, terms such as "first," "second," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0023] Industrial robots are core equipment for achieving micron-level precision assembly. However, during prolonged continuous high-speed operation, their internal motors and reducers generate continuous heat. This heat spreads unevenly along the metal links, causing differential deformation of the arm due to thermal expansion and contraction. This disrupts the preset geometric parameters of the control system, leading to end effector positioning drift. In scenarios such as semiconductor packaging and micro-optical device assembly, robots often operate continuously at extreme speeds. Thermal deformation dynamically drifts over time and under varying operating conditions, with errors reaching tens of microns, which can directly cause chip placement failures. Traditional methods (such as one-time calibration and end-effector vision compensation) are insufficient: calibration cannot adapt to dynamic errors, vision compensation only corrects the endpoint position and cannot suppress motion trajectory deviations, and frequent shutdowns for calibration disrupt high-cycle production efficiency. Therefore, there is an urgent need to develop real-time, seamless precision motion control methods to compensate for thermal deformation errors and ensure precision assembly quality and continuous production capabilities.

[0024] like Figure 1 The diagram illustrates a flowchart of a precision motion control method for an industrial robot. This application proposes a precision motion control method for an industrial robot, comprising: S10, acquire multi-source motion characteristic signals of robot joints, including trajectory deviation signals fed back by joint encoders, drive current fluctuation signals, and broadband micro-vibration signals.

[0025] The multi-source motion characteristic signals mentioned in this application refer to various types of data collected from the joints of industrial robots. These data can reflect various physical states and potential anomalies during robot operation. Specifically, they include trajectory deviation signals fed back by the joint encoders, drive current fluctuation signals, and broadband micro-vibration signals. The trajectory deviation signals fed back by the joint encoders are measured in real time by encoders installed at the robot joints and are used to indicate the difference between the actual and commanded positions of the joints. The drive current fluctuation signals reflect the changes in current of the drive motor during operation and are usually closely related to load, friction, and internal mechanical conditions. The broadband micro-vibration signals are collected by high-sensitivity sensors (such as accelerometers) and cover a wide range of minute vibrations, which may originate from various factors such as mechanical wear, structural resonance, or thermal deformation. Comprehensive analysis of these signals provides rich information for accurately diagnosing the internal state of the robot.

[0026] S20, Analyze the dynamic correlation of the multi-source motion characteristic signals and identify the target signal pattern that is strongly correlated with thermal deformation; S30, based on the target signal pattern and the difference in the physical coupling mechanism between the trajectory deviation signal and the broadband micro-vibration signal, the thermal deformation characteristics and mechanical wear characteristics are separated from the aliasing spectrum, wherein the aliasing spectrum is formed by the kinematic deviation signal and the broadband vibration signal; S40, for the thermal deformation feature, generate a motion compensation command including trajectory compensation based on the thermal deformation intensity; and for the mechanical wear feature, trigger a graded maintenance strategy that matches the wear degree.

[0027] The precision motion control method for industrial robots in this application first requires acquiring multi-source motion characteristic signals of the robot joints. These signals are fundamental to understanding the robot's internal state. For example, high-precision encoders can be installed at each robot joint to acquire trajectory deviation signals in real time. These encoders can provide feedback on the actual angular position of the joints with a microsecond-level time resolution. Simultaneously, by integrating current sensors into the motor drivers, fluctuations in the drive current can be monitored in real time. Furthermore, miniature accelerometers or piezoelectric sensors can be deployed at critical parts of the robot joints, such as the reducer housing or linkage structure, to capture broadband micro-vibration signals. These sensors should possess high sensitivity and broadband response characteristics to ensure the detection of minute, high-frequency vibrations. All these signals are synchronously acquired through a high-speed data acquisition system and transmitted to a central processing unit for subsequent analysis.

[0028] After acquiring multi-source motion characteristic signals, it is necessary to analyze the dynamic correlation of these signals to identify target signal patterns strongly correlated with thermal deformation. This can be achieved by establishing a multivariate time series analysis model, using trajectory deviation signals, drive current fluctuation signals, and broadband micro-vibration signals as inputs. This model can employ methods such as cross-correlation analysis, Granger causality tests, or dynamic Bayesian networks to quantify the interdependencies between different signals over time. By training on historical data, unique cooperative change patterns among these signals can be identified when the robot undergoes thermal deformation. For example, when the robot's joint temperature rises, a slow drift in the trajectory deviation signal, specific frequency fluctuations in the drive current, and an enhancement of low-frequency components in the broadband micro-vibration signal may be observed. These specific combinations of changes are identified as target signal patterns strongly correlated with thermal deformation.

[0029] Furthermore, based on the identified target signal pattern and the difference in the physical coupling mechanism between the trajectory deviation signal and the broadband micro-vibration signal, thermal deformation features and mechanical wear features are separated from the aliased spectrum. The aliased spectrum is formed by the kinematic deviation signal and the broadband vibration signal. Blind source separation techniques, such as Independent Component Analysis (ICA) or Non-negative Matrix Factorization (NMF), can be used to process the aliased spectrum. The trajectory deviation caused by thermal deformation usually manifests as a low-frequency, slowly changing signal component, while mechanical wear may lead to a high-frequency, periodic vibration signal. Since these two physical phenomena differ significantly in time scale and frequency characteristics, they can be effectively separated from the aliased spectrum by designing specific filter banks or using multi-scale analysis methods based on wavelet transform. For example, the low-frequency characteristics of the thermal deformation signal and the high-frequency characteristics of the wear signal can be utilized to extract their respective characteristic signals through bandpass filtering or frequency domain decomposition.

[0030] For the isolated thermal deformation features, motion compensation commands containing trajectory compensation amounts need to be generated based on the intensity of the thermal deformation. For example, a thermal deformation-trajectory compensation model can be established, which takes the intensity of the thermal deformation feature (e.g., a quantized value obtained from temperature sensors or thermal deformation signal analysis) as input and outputs the corresponding trajectory compensation amount. This model can be calibrated through offline experiments, i.e., measuring the robot's thermal deformation and its impact on the end effector trajectory under different temperature and load conditions, and establishing a mapping relationship. When a thermal deformation feature is detected in real time, the required trajectory compensation amount is calculated based on its intensity using this model and encapsulated as a motion compensation command. This command is then injected into the robot's trajectory planning module to adjust the drive torque in subsequent servo control stages, thereby offsetting the trajectory deviation caused by thermal deformation.

[0031] For mechanical wear characteristics, a graded maintenance strategy matching the wear level needs to be triggered. For example, the wear level can be divided into different grades, such as slight wear, moderate wear, and severe wear, based on the intensity and evolution trend of the mechanical wear characteristics. For slight wear, preventative maintenance strategies can be triggered, such as recommending inspection or lubrication at the next downtime. For moderate wear, an early warning mechanism can be triggered, recommending maintenance in the short term, possibly accompanied by moderate restrictions on robot operating parameters (such as speed and acceleration). For severe wear, an immediate shutdown maintenance command is required to prevent further damage or safety accidents. These strategies can be pre-stored in a maintenance strategy database and dynamically matched and triggered based on real-time identified wear characteristics.

[0032] This application presents a precision motion control method for industrial robots. By acquiring multi-source motion characteristic signals of robot joints, including trajectory deviation signals fed back from joint encoders, drive current fluctuation signals, and broadband micro-vibration signals, a rich data foundation is provided for subsequent accurate diagnosis. Subsequently, by analyzing the dynamic correlation of these multi-source motion characteristic signals, target signal patterns strongly correlated with thermal deformation can be identified, allowing the system to focus on key factors leading to decreased accuracy. Furthermore, based on the difference in the physical coupling mechanism between trajectory deviation signals and broadband micro-vibration signals, thermal deformation characteristics and mechanical wear characteristics are effectively separated from the aliased spectrum. This step is the core innovation of this application, solving the problem that traditional methods struggle to distinguish different error sources. Finally, motion compensation commands containing trajectory compensation are generated for thermal deformation characteristics, achieving real-time, imperceptible compensation for dynamic errors caused by thermal deformation. For mechanical wear characteristics, a graded maintenance strategy matching the wear level is triggered, thereby extending the robot's service life and reducing maintenance costs. The entire process forms a closed-loop intelligent control system, significantly improving the motion accuracy, stability, and reliability of industrial robots under complex working conditions.

[0033] Compared with existing technologies, the precision motion control method for industrial robots proposed in this application has significant advantages and innovations. Traditional control methods often struggle to effectively distinguish and handle dynamic errors caused by thermal deformation and mechanical wear, typically employing a single compensation strategy or relying on human experience. For example, some existing methods may only use temperature sensors for thermal deformation compensation, but this method cannot directly perceive the actual impact of thermal deformation on trajectory accuracy, and the compensation accuracy is limited. Other methods may detect wear through vibration analysis, but it is often difficult to effectively distinguish wear vibrations from normal operating vibrations or vibrations caused by thermal deformation.

[0034] The core innovation of this application lies in its deep fusion and intelligent decoupling mechanism of multi-source signals. By simultaneously acquiring trajectory deviation signals, driving current fluctuation signals, and broadband micro-vibration signals, and deeply analyzing their dynamic correlations, this application can more comprehensively and accurately capture the complex internal state of the robot. In particular, this application utilizes the difference in the physical coupling mechanism between trajectory deviation signals and broadband micro-vibration signals, and through advanced signal processing techniques (such as blind source separation or multi-scale analysis), successfully separates thermal deformation characteristics and mechanical wear characteristics from the aliasing spectrum. This separation process is difficult to achieve in existing technologies, enabling the system to specifically process different types of error sources.

[0035] Specifically, regarding thermal deformation characteristics, this application can dynamically generate motion compensation commands containing trajectory compensation amounts based on their intensity and inject them into trajectory planning, thereby adjusting the driving torque in real time during servo control. This dynamic compensation mechanism based on the actual effects of thermal deformation is far superior to traditional static calibration or simple temperature compensation, enabling precise and imperceptible correction of micron-level trajectory deviations. For mechanical wear characteristics, this application can trigger a graded maintenance strategy matched to the degree of wear, from preventative maintenance to emergency shutdown, achieving intelligent management of the robot's health status. This refined maintenance strategy not only extends the service life of robot components but also avoids production losses caused by over-maintenance or untimely maintenance.

[0036] In summary, this application overcomes the limitations of existing technologies in handling dynamic errors of industrial robots through innovative multi-source signal decoupling and intelligent compensation maintenance strategies, significantly improving the robot's precision motion control capabilities, reliability, and operating efficiency, and providing a more advanced and reliable solution for the field of high-precision manufacturing.

[0037] In some embodiments, for thermal deformation features, the step of generating motion compensation instructions including trajectory compensation amounts based on thermal deformation intensity includes: When thermal deformation features are identified, the pose deviation of the robot end effector is quantified based on the thermal deformation intensity. The trajectory compensation amount is calculated based on the pose deviation, and the motion compensation command encapsulated with the trajectory compensation amount is injected into the trajectory planning so as to adjust the driving torque in the servo control; wherein, the motion compensation command is dynamically generated through the thermal deformation-pose offset mapping model, and the thermal deformation-pose offset mapping model is established based on the calibration of the robot rigid body transformation parameters.

[0038] Specifically, when the system identifies thermal deformation features by analyzing multi-source motion characteristic signals, it immediately initiates the quantization process for pose deviation. This quantization process aims to transform the abstract thermal deformation intensity into a concrete pose (position and orientation) deviation of the robot's end effector in three-dimensional space. For example, thermal deformation intensity can be characterized by comprehensive indicators such as joint temperature sensors, motor current change rate, or micro-vibration signals within a specific frequency range. Through a pre-established physical model or data-driven model, these thermal deformation intensity indicators can be mapped to the geometric deformation of each joint of the robot, and then the pose deviation of the end effector can be calculated using the forward kinematics of the robot.

[0039] The pose deviation refers to the difference between the actual pose and the ideal pose of the robot's end effector after being affected by thermal deformation. Once this pose deviation is quantified, a trajectory compensation amount can be calculated based on it. The trajectory compensation amount is a correction value used to correct the robot's preset motion trajectory, and its purpose is to offset the pose deviation caused by thermal deformation. For example, if the end effector has a slight offset in the X direction, the trajectory compensation amount will introduce a reverse correction value in the X direction. This trajectory compensation amount is then encapsulated into motion compensation instructions and injected into the trajectory planning module of the robot control system. When generating joint motion instructions, the trajectory planning module takes these compensation amounts into account, thereby adjusting the drive torque at the servo control level to ensure that the robot's end effector can move along the corrected trajectory and achieve the expected accurate pose.

[0040] In practical applications, the motion compensation command is dynamically generated through a thermal deformation-pose offset mapping model. This mapping model is key to achieving accurate compensation, as it can convert the current thermal deformation state into a precise pose offset in real time. This model can be a complex nonlinear function, a neural network model, or a lookup table-based model; its core lies in accurately capturing the complex relationship between thermal deformation and pose offset. The thermal deformation-pose offset mapping model is established based on the calibration of the robot's rigid body transformation parameters. This means that before the robot is put into use or during periodic maintenance, its rigid body transformation parameters are calibrated with high precision to obtain the accurate geometric parameters of each joint. Based on this, through experiments and data acquisition under different thermal load conditions, the mapping relationship between thermal deformation and pose offset is established and optimized, thereby ensuring the accuracy and robustness of the model in actual operation.

[0041] Through the above technical solution, this application can significantly improve the motion control accuracy and stability of industrial robots under thermal deformation conditions. By accurately quantifying the end effector pose deviation caused by thermal deformation and dynamically generating trajectory compensation based on a calibrated thermal deformation-pose offset mapping model, the robot can achieve refined and proactive compensation for the effects of thermal deformation. This avoids the problems of compensation lag or insufficient compensation that may exist in traditional methods, enabling the actual trajectory of the end effector to more closely follow the planned trajectory during long-term operation or high-load work, thereby effectively reducing processing errors and product defect rates caused by thermal deformation. In addition, injecting compensation commands into trajectory planning rather than adjusting only at the servo layer makes the entire control process smoother and more efficient, further improving the overall performance and reliability of the robot.

[0042] For example, suppose an industrial robot is performing a long-term laser welding operation. As the operation time increases, the motors and reducers at the robot joints generate heat, causing slight thermal expansion and deformation of the joint structure, which in turn affects the positioning accuracy of the robot's end effector (laser head). The solution of this application is implemented as follows: First, the system continuously acquires temperature sensor data, drive current fluctuation signals, and broadband micro-vibration signals from the robot joints. When these signal patterns are identified as strongly correlated with thermal deformation, the system quantifies the pose deviation of the laser head in three-dimensional space in real time based on these thermal deformation intensity indicators and a pre-established thermal deformation-pose offset mapping model. For example, the model may calculate that the laser head has a Z-axis offset of 0.05mm and a X-axis pose deviation of 0.01 degrees at the welding point. Then, the system calculates the corresponding trajectory compensation amount based on these quantified pose deviations. For example, to compensate for the 0.05mm Z-axis offset, a -0.05mm Z-axis compensation will be introduced in the trajectory planning. These compensation amounts are encapsulated into motion compensation instructions and injected into the trajectory planning module of the robot controller. As the laser head moves from one welding point to the next, the trajectory planner dynamically integrates these compensation values ​​into the joint motion commands. This allows the servo controller to adjust the torque output when driving the joints, thereby physically correcting the joint position and ensuring that the laser head's pose accurately meets requirements when it reaches the target welding point, maintaining high-precision welding even under the influence of thermal deformation. The thermal deformation-pose offset mapping model is calibrated and trained under different ambient temperatures and load conditions using high-precision measuring equipment (such as a laser tracker) to measure the pose of the robot's end effector, combined with data such as joint temperature and current, before the robot leaves the factory or during periodic maintenance, ensuring its accuracy under actual working conditions.

[0043] In some embodiments, the step of analyzing the dynamic correlation of the multi-source motion characteristic signals and identifying target signal patterns strongly correlated with thermal deformation further includes: The acquired multi-source motion feature signals are time-stamp aligned and preliminarily filtered to obtain motion feature vectors. The dynamic correlation between the moving feature vectors is calculated in real time, and the moving feature vectors are compared with the predefined physical phenomenon relationship patterns to identify target signal patterns that are strongly correlated with thermal deformation.

[0044] Specifically, the multi-source motion feature signals include trajectory deviation signals from the joint encoder feedback, drive current fluctuation signals, and broadband micro-vibration signals. When processing these signals, timestamp alignment ensures precise synchronization of signals from different sensors or data sources on the time axis, eliminating time deviations caused by sampling or transmission delays and thus guaranteeing the accuracy of subsequent analysis. Preliminary filtering aims to remove random noise, high-frequency interference, or low-frequency drift from the signals. Methods such as moving average filtering, Kalman filtering, or wavelet denoising can be used to improve the signal-to-noise ratio, providing a high-quality data foundation for subsequent feature extraction and correlation analysis. After timestamp alignment and preliminary filtering, these signals are integrated to form a motion feature vector, which comprehensively characterizes the motion state and potential anomalies of the robot joints at a specific moment.

[0045] Real-time calculation of the dynamic correlation between motion feature vectors can be understood as quantifying the interdependencies between different motion feature signals over time using statistical or machine learning methods. For example, algorithms such as Pearson correlation coefficient, Granger causality, mutual information, or dynamic time warping (DTW) can be used to reveal the coordinated change patterns of trajectory deviation signals, drive current fluctuation signals, and broadband micro-vibration signals under different operating conditions. These dynamic correlations reflect the complex interactions of the robot's internal physical processes.

[0046] In practical applications, predefined physical phenomenon relationship patterns refer to the typical correlation patterns that should exist between multi-source motion characteristic signals when a specific physical phenomenon (such as thermal deformation, mechanical wear, etc.) occurs, established based on robot design, material properties, operational experience, and physical models. For example, when thermal deformation occurs, the trajectory deviation signal may show a specific positive or negative correlation with the drive current fluctuation signal, and the spectral characteristics of the broadband micro-vibration signal may also undergo specific changes. These patterns can be stored in a database as a benchmark for identification. By comparing the dynamic correlation of the motion characteristic vectors calculated in real time with these predefined patterns, target signal patterns strongly correlated with thermal deformation can be effectively identified, thereby distinguishing motion anomalies caused by thermal deformation.

[0047] Through the above technical solutions, this application can significantly improve the accuracy and robustness of identifying thermal deformation features of industrial robots. Specifically, timestamp alignment and preliminary filtering effectively improve the quality of the raw data, avoiding misjudgments caused by data noise and asynchrony. Real-time calculation of dynamic correlation and comparison with predefined patterns enable the system to understand the intrinsic relationships between different motion feature signals more deeply, thereby accurately distinguishing signal changes caused by thermal deformation and other interference factors (such as mechanical wear) under complex operating conditions. This refined identification mechanism provides a more reliable basis for subsequent thermal deformation compensation and mechanical wear management, thereby improving the overall motion accuracy and operational stability of industrial robots.

[0048] In some embodiments, the step of triggering a graded maintenance strategy matching the degree of wear for mechanical wear characteristics includes: Adaptive suppression of instantaneous high-frequency vibrations generated by identified high-speed motion; and Gated management is implemented for persistent mid-to-high frequency vibrations caused by identified wear.

[0049] Specifically, adaptive suppression of transient high-frequency vibrations generated by identified high-speed motion refers to the brief, high-frequency vibration signals that may be generated by industrial robot joints during high-speed movement due to factors such as inertia, impact, or transient load changes. These vibrations are usually transient and not directly caused by continuous wear, but if left uncontrolled, they may affect motion accuracy or be misjudged as wear. The adaptive suppression mechanism aims to effectively reduce these transient high-frequency vibrations without affecting normal motion response by dynamically adjusting filtering parameters or introducing reverse compensation signals in conjunction with the robot's current motion state (such as speed and acceleration) and monitoring the frequency, amplitude, and duration of the vibration signal in real time. Its purpose is to avoid overreacting to non-wear-related transient vibrations and ensure system stability.

[0050] The gating management of persistent mid-to-high frequency vibrations caused by identified wear can be understood as employing a threshold- and time-window-based strategy to process vibration signals with a certain degree of persistence and a specific frequency range (mid-to-high frequency) caused by actual mechanical wear. This type of vibration often reflects accumulated wear and requires a more cautious response. The gating management mechanism determines whether to trigger further maintenance or adjustment measures by setting specific frequency and amplitude thresholds and considering the duration of the vibration signal. For example, only when the vibration signal persists for more than a preset time within a specific frequency range and its amplitude reaches or exceeds a certain threshold will it be identified as a wear signal and processed accordingly. The aim is to distinguish genuine wear signals from occasional noise or transient disturbances, avoid false alarms and unnecessary maintenance interventions, and ensure a timely response to genuine wear.

[0051] The above technical solutions significantly improve the accuracy and precision of the industrial robot's precision motion control system in identifying mechanical wear characteristics. Specifically, by adaptively suppressing instantaneous high-frequency vibrations, interference from non-wear noise in wear assessment is effectively avoided, reducing false alarms and unnecessary maintenance costs. Simultaneously, by gating continuous medium-to-high-frequency vibrations, it ensures that only genuine, persistent wear signals trigger corresponding maintenance strategies, thereby improving the reliability of wear diagnosis and enabling graded maintenance strategies to more accurately match the actual degree of wear, extending the service life of robot components and ensuring their long-term operational accuracy and stability.

[0052] For example, suppose an industrial robot is performing a high-speed spot welding task. At certain path switching points or during sudden stops and starts, due to mechanical shock and inertia, brief, high-amplitude transient high-frequency vibrations may appear in the broadband micro-vibration signals fed back by the joint encoder. If these transient vibrations are not differentiated and processed, they may be misjudged as early signs of mechanical wear, thereby triggering unnecessary maintenance alarms or reducing the robot's operating speed. The solution in this application, through an adaptive suppression mechanism, can analyze the duration, frequency characteristics, and current motion state of these vibrations in real time. For example, when a high-frequency vibration is detected that lasts only a few milliseconds and highly matches the transient impact mode of high-speed motion, the system will identify it as a non-wear transient vibration and dynamically adjust the filter parameters to suppress it, avoiding misjudgment.

[0053] On the other hand, when robot joint bearings begin to show slight wear, they may generate continuous, relatively stable mid-to-high frequency vibrations at specific rotational speeds. This vibration may not immediately lead to severe performance degradation, but it indicates accumulated wear. The solution in this application uses a gating management mechanism to continuously monitor the frequency, amplitude, and harmonic composition of these mid-to-high frequency vibration signals. For example, when the vibration signal of a joint exceeds a preset threshold for several hours or days within a specific frequency range (e.g., 500Hz-2kHz), and its harmonic structure matches a known bearing wear pattern, the gating management mechanism will identify it as a genuine wear characteristic. At this point, the system will trigger corresponding graded maintenance strategies based on the degree of wear. For example, it may first issue a warning, suggesting an inspection of the joint during the next planned maintenance, or, if the wear worsens, automatically adjust the joint's motion trajectory optimization parameters to slow the wear process and schedule more urgent maintenance. In this way, the system can accurately distinguish between transient noise and genuine wear and take appropriate response measures.

[0054] In some embodiments, the above-described steps for gating and managing persistent mid-to-high frequency vibrations caused by identified wear include: The storage wear feature evolution path describes the dynamic change pattern of vibration frequency, vibration amplitude, harmonic structure and its correlation with the robot's motion state during the wear process; The frequency, amplitude, and harmonic composition of the wear vibration signal in the motion feature vector are tracked in real time, and the nonlinear correlation between the wear vibration signal and the robot's current motion state parameters is dynamically calculated to obtain the real-time wear vibration characteristics. The real-time wear vibration characteristics are compared and matched with the wear feature evolution path in real time to identify the current evolution stage of wear and predict its future trend; and Based on the current stage of wear evolution and predicted trends, the processing strategy for wear vibration signals is adjusted in real time.

[0055] Specifically, the wear characteristic evolution path refers to a pre-established set of patterns describing how the vibration signal characteristics (e.g., frequency, vibration amplitude, harmonic composition) of mechanical wear dynamically change with robot motion state parameters such as time, load, and speed from the initial stage to the severe stage. This path can be constructed using historical data, experimental tests, or physical models, with the aim of providing a benchmark for subsequent real-time wear state assessment.

[0056] The process involves real-time tracking of the frequency, amplitude, and harmonic composition of the wear vibration signal within the motion feature vector, and dynamically calculating the nonlinear correlation between the wear vibration signal and the robot's current motion state parameters. This aims to obtain the actual wear vibration performance of the robot's joints at the current moment. The motion feature vector can be understood as a comprehensive data representation obtained by timestamping and initially filtering the collected multi-source motion feature signals. By analyzing this real-time data, detailed characteristics of the current wear vibration signal can be obtained, further revealing the complex nonlinear relationship between these characteristics and the robot's actual operating conditions (e.g., speed, acceleration, load), thus forming real-time wear vibration characteristics.

[0057] In practical applications, the real-time wear vibration characteristics are compared and matched with the wear feature evolution path in real time. The purpose is to accurately determine the current stage of wear, such as early wear, intermediate wear, or critical wear, and to predict the possible development direction and speed of wear in the future based on the trend information of the evolution path. This comparison and matching can be achieved through pattern recognition algorithms, machine learning models, or rule-based expert systems.

[0058] Furthermore, adjusting the processing strategy for wear vibration signals in real time based on the current wear evolution stage and predicted trends refers to dynamically optimizing the suppression, filtering, or alarm triggering mechanisms for wear vibration signals according to the severity and development trend of wear. For example, in the early stages of wear, only slight signal suppression and recording may be performed; while when wear enters the middle stage or is predicted to deteriorate rapidly, the suppression intensity may be increased, the monitoring threshold adjusted, or even a higher-level warning or recommendation for shutdown maintenance may be triggered. This strategy adjustment aims to ensure the stability and safety of robot operation, while maximizing component lifespan and optimizing maintenance cycles.

[0059] Through the above technical solution, this application enables refined and predictive management of mechanical wear of industrial robots. Compared with basic solutions that only perform simple gating management, this application introduces wear characteristic evolution paths and combines them with real-time tracking, comparison, and prediction mechanisms, allowing the processing strategy for wear vibration signals to be dynamically adjusted according to the actual evolution stage and future trend of wear. This significantly improves the accuracy of wear identification and the timeliness and effectiveness of maintenance strategies, avoiding maintenance delays or over-maintenance caused by the uncertainty of wear evolution. Therefore, it not only effectively extends the service life of robot components and reduces maintenance costs, but also significantly improves the operational reliability and positioning accuracy of industrial robots, ensuring their high-performance operation over long periods.

[0060] The following is a concrete example. Suppose that a joint of an industrial robot begins to show slight wear during long-term operation. The system first stores the joint's vibration frequency, amplitude, harmonic structure, and dynamic change patterns in relation to motion parameters such as robot speed and load at different wear stages, forming a wear characteristic evolution path. When the robot is operating normally, the signal acquisition module continuously acquires the joint's motion characteristic signals, and the pattern matching module and decoupling calculation module identify the continuous mid-to-high frequency vibrations caused by wear. At this time, the system tracks the frequency, amplitude, and harmonic composition of these wear vibration signals in real time and dynamically calculates their nonlinear correlation with the robot's current motion state (e.g., performing a handling task at 50% of maximum speed), obtaining real-time wear vibration characteristics.

[0061] The system then compares and matches these real-time wear vibration characteristics with pre-stored wear feature evolution paths in real time. For example, if the real-time characteristics match the "early wear stage" in the evolution path, and the predicted trend shows that wear will develop at a slow rate, the system may adjust its processing strategy for the wear vibration signal. For example, it may set the monitoring threshold at a relatively lenient level, perform only data logging and slight signal suppression, and mark it as "recommended to check during the next routine maintenance" in the maintenance plan.

[0062] However, if subsequent monitoring reveals that the real-time wear vibration characteristics begin to match the "mid-stage wear" in the evolution path, and the predicted trend indicates an accelerating wear rate, the system will immediately adjust its processing strategy. For example, monitoring thresholds may be tightened, alarm triggering conditions may become more sensitive, and a more proactive maintenance recommendation may be triggered, such as "inspection or replacement of parts is recommended within the next 200 working hours." Furthermore, the dynamic compensation module may fine-tune the motion trajectory optimization parameters of the affected joints to reduce the impact of wear vibration on the accuracy of the end effector. This dynamic and predictive gating management avoids unnecessary downtime maintenance in the early stages of wear and allows for timely intervention before wear deteriorates, thereby optimizing maintenance resources and improving the overall operational efficiency and lifespan of the robot.

[0063] In some embodiments, the step of adjusting the processing strategy for the wear vibration signal in real time based on the current wear evolution stage and predicted trend includes: The vibration frequency, vibration amplitude, and harmonic structure composition during the wear process are analyzed in real time, and the nonlinear coupling relationship between the frequency, amplitude, and harmonic composition is dynamically calculated. Assess the extent to which the nonlinear coupling relationship affects the positioning accuracy of the robot's end effector; Based on the evaluation results, the monitoring threshold and alarm triggering conditions for the wear vibration signal are dynamically adjusted. Furthermore, based on the evaluation results, the motion trajectory optimization parameters of the affected joints are dynamically adjusted.

[0064] Specifically, real-time analysis of the vibration frequency, amplitude, and harmonic structure during the wear process, and dynamic calculation of the nonlinear coupling relationship between these frequencies, amplitudes, and harmonic components, refers to the refined analysis of wear vibration signals extracted from trajectory deviation signals, drive current fluctuation signals, and broadband micro-vibration signals fed back by joint encoders, using advanced signal processing techniques such as wavelet transform, Hilbert-Huang transform, or deep learning models. The aim is to reveal that wear characteristics (such as the appearance of specific frequency components, amplitude increases, and harmonic distortion) are not simply linear superpositions, but rather involve complex interactions. For example, vibration at a certain frequency may induce or enhance harmonics at a specific amplitude, thus providing a more comprehensive understanding of the dynamic evolution of wear.

[0065] The assessment of the impact of the nonlinear coupling relationship on the positioning accuracy of the robot's end effector can be understood as establishing a mapping model. This model can correlate the nonlinear coupling characteristics of the wear vibration signal with performance indicators of the robot's end effector in actual operation, such as pose error, repeatability, or trajectory tracking accuracy. In practical applications, this assessment can be performed using offline experimental data, finite element analysis, or online learning algorithms. Its purpose is to quantify the degree of performance degradation of the robot under different wear conditions, providing a precise basis for subsequent compensation and maintenance.

[0066] Furthermore, based on the evaluation results, the monitoring threshold and alarm triggering conditions for the wear vibration signal are dynamically adjusted. This means that when the evaluation results show that the nonlinear coupling relationship of wear has a small impact on the robot's positioning accuracy, the monitoring threshold can be appropriately relaxed to avoid excessive intervention; conversely, when the impact is significant, the monitoring threshold should be tightened and the alarm triggered in advance to ensure timely maintenance measures are taken. The aim is to achieve intelligent and adaptive wear monitoring, avoid false alarms or missed alarms, and improve maintenance efficiency.

[0067] Furthermore, based on the evaluation results, dynamically adjusting the motion trajectory optimization parameters of the affected joints refers to fine-tuning the kinematic and dynamic parameters of the joint according to the degree of impact of wear on the positioning accuracy of a specific joint. For example, the acceleration / deceleration curve, maximum speed limit, or smoothness factor of the joint in a specific motion segment can be adjusted. The aim is to avoid or mitigate the adverse effects of wear on robot performance, extend component life, and maintain operational accuracy by optimizing the motion trajectory without affecting the overall task completion.

[0068] This application's solution, through in-depth analysis of the nonlinear coupling relationship between vibration frequency, vibration amplitude, and harmonic structure during the wear process, and quantitative evaluation of this relationship with the positioning accuracy of the robot's end effector, enables a more precise understanding of the actual impact of wear on robot performance. It is precisely this accurate perception and quantification of nonlinear coupling effects that allows the system to shift from a generalized wear handling strategy to a refined, adaptive adjustment based on actual impact. By dynamically adjusting monitoring thresholds and alarm triggering conditions, timely warnings can be ensured before the wear impact reaches a critical level. Simultaneously, by dynamically adjusting the motion trajectory optimization parameters of affected joints, the accuracy loss caused by wear can be proactively avoided or mitigated, thus maintaining the robot's high-precision operational capabilities even as wear continues to evolve.

[0069] Through the above technical solution, this application enables a deeper understanding and more refined management of the wear state of industrial robots. This solution not only identifies the presence of wear but also quantifies the nonlinear coupling effect of wear and its specific impact on the positioning accuracy of the robot's end effector, thus making the adjustment of wear management strategies more targeted and effective. Consequently, the robot can maintain high-precision operation throughout the wear evolution process, significantly extending the service life of key components, reducing unplanned downtime, lowering maintenance costs, and improving the overall operational reliability and economic benefits of industrial robots.

[0070] In some embodiments, the step of dynamically adjusting the motion trajectory optimization parameters of the affected joint based on the evaluation results includes: The storage of multi-joint wear coupling influence modes describes the nonlinear interaction between the wear vibration characteristics and the corresponding motion trajectory optimization parameters of multiple joints when wear occurs simultaneously. The vibration frequency, vibration amplitude, and harmonic structure composition of multiple joints in the motion feature vector are tracked in real time during the wear process, and the nonlinear coupling relationship between these wear vibration signals and between them and the robot's current motion state parameters is dynamically calculated to obtain the real-time multi-joint wear coupling characteristics. The real-time multi-joint wear coupling characteristics are compared and matched with the multi-joint wear coupling influence mode in real time to identify the current coupling effect type and intensity of multi-joint wear; and Based on the identified coupling effect type and intensity, the acceleration / deceleration curves and velocity limiting factors of the affected joints are dynamically adjusted.

[0071] Specifically, the multi-joint wear coupling effect mode refers to a knowledge base or model established in advance through experiments, simulations, or historical data analysis. It details how the wear vibration characteristics (e.g., frequency, amplitude, harmonic structure) of these joints interact when multiple joints of a robot wear simultaneously, and how these interactions further affect the overall motion accuracy and stability of the robot. This mode also includes adjustment strategies for motion trajectory optimization parameters (e.g., acceleration / deceleration curves, speed limit factors) to be adopted for different types and intensities of coupling effects. Its purpose is to provide a decision-making basis for subsequent real-time adjustments.

[0072] This process involves real-time tracking of the vibration frequency, amplitude, and harmonic structure of multiple joints during wear within the motion feature vector. It also dynamically calculates the nonlinear coupling relationships between these wear vibration signals and between them and the robot's current motion state parameters. This yields real-time multi-joint wear coupling characteristics, which can be understood as continuously monitoring multi-source motion feature signals from joint encoders, drive current sensors, and broadband micro-vibration sensors. Signal processing and pattern recognition techniques are then used to extract the real-time vibration characteristics of each wear-affected joint. Simultaneously, advanced nonlinear analysis algorithms, such as those based on deep learning or nonlinear dynamic models, quantify the mutual influence between different joint wear vibration signals and the complex correlations between these vibration signals and the robot's current velocity, acceleration, load, and other motion state parameters. The aim is to comprehensively and accurately grasp the actual coupling state of the robot's multi-joint wear.

[0073] In practical applications, the real-time multi-joint wear coupling characteristics are compared and matched with the multi-joint wear coupling influence patterns in real time to identify the current coupling effect type and intensity of multi-joint wear. For example, pattern recognition algorithms, machine learning classifiers, or rule-based expert systems can be used to compare the real-time acquired multi-joint wear coupling characteristics with various pre-stored coupling influence patterns. By calculating similarity, distance metrics, or feature matching, the known coupling effect type (e.g., resonant amplification, frequency shift, energy transfer, etc.) of the current multi-joint wear is determined, and its intensity is quantified. The purpose is to provide clear guidance for subsequent fine-tuning.

[0074] Furthermore, based on the identified coupling effect type and intensity, the acceleration / deceleration curves and velocity limit factors of the affected joints are dynamically adjusted. This means that, according to the identified specific coupling effect type (e.g., wear vibration of one joint is amplified by the motion of another joint) and its intensity, the system selects or generates optimal motion trajectory optimization parameters from a preset adjustment strategy library. For example, if a specific coupling effect is identified as causing a decrease in the accuracy of the end effector during high-speed motion, the velocity limit factor of the relevant joint can be appropriately reduced, or its acceleration / deceleration curve can be adjusted to make its motion smoother in the critical path segment, thereby suppressing coupled vibration. The aim is to effectively suppress the negative impact of multi-joint wear through refined trajectory parameter adjustment.

[0075] Through the above technical solution, this application can effectively solve the problem of decreased control accuracy and insufficient stability caused by the failure of traditional methods to fully consider the complex nonlinear coupling effects between joints when dealing with simultaneous wear of multiple joints. By storing and utilizing the coupling effect patterns of multi-joint wear and identifying the type and intensity of coupling effects in real time, the system can perform more targeted and global trajectory optimization, thereby significantly improving the precision motion control performance of industrial robots under long-term operation and complex tasks, extending the robot's service life, and reducing maintenance costs.

[0076] In some embodiments, the step of dynamically adjusting the acceleration / deceleration curves and velocity limiting factors of the affected joints based on the identified coupling effect type and intensity includes: When a robot performs a complex multi-path switching task, it calculates the transition smoothness requirements of acceleration / deceleration curves and speed limit factors between path segments based on the wear coupling characteristics of the current path segment and the estimated wear coupling characteristics of the next path segment. Before the path switching point, a gradual adjustment mechanism is activated. Based on the transition smoothness requirements, this mechanism, within a preset transition time, adjusts the acceleration / deceleration curves and speed limit factors of the current path segment to those of the next path segment using a non-linear gradual change. Ensure that the rate of change of speed and the rate of change of acceleration of the robot end effector remain within the preset threshold during the transition period.

[0077] Specifically, when a robot performs complex multi-path switching tasks, it means that the robot needs to frequently change its direction of motion, speed, or target point, such as when performing complex curved surface welding, multi-station assembly, or precise grasping and placement operations. These tasks have extremely high requirements for the continuity and smoothness of motion. The wear coupling characteristics of the current path segment refer to the nonlinear coupling relationship between the wear vibration signals between multiple joints and the robot's motion state parameters, obtained through real-time tracking and calculation within the motion path segment currently being executed by the robot. The estimated wear coupling characteristics of the next path segment can be understood as a prediction of the wear coupling characteristics of the upcoming path segment based on historical data, predictive models, or pre-analysis of the motion conditions of the next path segment. Based on these characteristics, the transition smoothness requirements of the acceleration / deceleration curves and speed limit factors between path segments are calculated. The purpose is to quantify how the robot's motion parameters should smoothly transition during path switching to avoid shocks and vibrations. This requirement may include limits on the maximum jerk, constraints on the rate of change of speed and acceleration, etc.

[0078] In practical applications, a gradual adjustment mechanism is initiated before the path switching point. Its purpose is to prepare and execute a smooth parameter transition in advance before the actual switch occurs. Based on the transition smoothness requirements, the gradual adjustment mechanism adjusts the acceleration / deceleration curves and speed limit factors of the current path segment to those of the next path segment within a preset transition time using a non-linear gradual change. The preset transition time is a configurable parameter, the length of which depends on the task's accuracy requirements, the robot's dynamic characteristics, and the severity of wear coupling. The non-linear gradual change means that the parameter adjustment does not use simple linear changes, but rather S-curves, polynomial curves, or other smooth functions to ensure smoother changes in speed and acceleration, thereby minimizing impact and vibration. Furthermore, it ensures that the rate of change of speed and acceleration of the robot's end effector remains within preset thresholds during the transition. This aims to strictly control the dynamic response of the robot's end effector, preventing excessive speed or acceleration fluctuations during the transition, thus ensuring motion stability and accuracy.

[0079] Through the above technical solutions, robots can achieve smoother and more precise motion transitions in complex multi-path switching tasks, significantly reducing the impact and vibration caused by wear coupling effects. This not only improves the positioning accuracy and trajectory tracking performance of the robot's end effector but also reduces wear on mechanical components, extending the equipment's lifespan. Furthermore, by optimizing the transition process, production efficiency and product quality can be improved, making it particularly suitable for precision machining and assembly scenarios where extremely high motion smoothness is required.

[0080] In some preferred embodiments, suppose an industrial robot performs spot welding on an automotive production line, a task involving rapid and precise path switching between different parts of the car body. Due to long-term operation, multiple joints of the robot experience varying degrees of wear, resulting in differences in multi-joint wear coupling characteristics under different motion states. When the robot switches from welding a side panel of the car body (path segment A) to welding a roof reinforcement (path segment B), the system first calculates the smoothness requirements for the transition of acceleration / deceleration curves and speed limit factors between the path segments based on the real-time wear coupling characteristics of path segment A and the estimated wear coupling characteristics of path segment B. For example, the system may calculate that during the switching process, the maximum jerk (Jerk) of the robot's end effector should not exceed a certain value to avoid a degradation in weld quality. Before the robot reaches the path switching point, the system initiates a gradual adjustment mechanism. Within a preset transition time (e.g., 200 milliseconds), this mechanism smoothly adjusts the acceleration / deceleration curves and speed limit factors of path segment A to the corresponding parameters of path segment B in a non-linear, gradual manner (e.g., using an S-curve transition). During this transition, the system monitors the rate of change of velocity and acceleration of the robot's end effector in real time, ensuring they remain within preset thresholds. If any trend exceeding the threshold is detected, the control system immediately makes fine adjustments, such as by adjusting the joint drive torque, to maintain smooth movement. In this way, the robot can complete path switching with extremely high smoothness and precision, ensuring welding quality and effectively reducing the risk of equipment failure due to wear.

[0081] In some embodiments described above, this application proposes dynamically adjusting the acceleration / deceleration curves and speed limit factors of affected joints based on the identified coupling effect type and strength. This aims to achieve smooth transitions between path segments when the robot performs complex multi-path switching tasks, and ensure that the rate of change of velocity and acceleration of the robot end effector remains within preset thresholds during the transition. However, in actual industrial production, the interaction environment between the robot end effector and the workpiece during path switching operations can be complex and variable, including factors such as workpiece material, surface roughness, or viscous fluid environments. These external environmental characteristics significantly affect the dynamic response and stability of the robot's motion. Relying solely on preset static thresholds may not adequately adapt to these dynamically changing workpiece environments, resulting in the actual rate of change of velocity and acceleration of the robot end effector exceeding the optimal operating range under specific working conditions, thereby affecting machining accuracy, surface quality, or operational stability.

[0082] To address this, this application further proposes a more refined control strategy to ensure that, during transitions, the rate of change of velocity and the rate of change of acceleration of the robot's end effector are dynamically maintained within optimal thresholds that match the current workpiece environment. This strategy dynamically calculates the optimal combination of motion thresholds by sensing the characteristics of the workpiece environment in real time, and then performs real-time monitoring and necessary fine-tuning control to cope with complex and ever-changing operating environments.

[0083] The steps to ensure that the rate of change of velocity and rate of change of acceleration of the robot end effector remain within preset thresholds during the transition include: Real-time sensing of the workpiece's material, surface roughness, or viscous fluid environment characteristics to obtain the workpiece's environmental characteristics; Based on the workpiece environment characteristics, the optimal velocity change rate and acceleration change rate threshold of the robot end effector under the current operation are dynamically calculated to obtain the optimal combination of motion thresholds. During the transition, the rate of change of velocity and the rate of change of acceleration of the robot end effector are monitored in real time and compared with the optimal combination of motion thresholds; When the detected rate of change of velocity or acceleration exceeds the optimal motion threshold combination, fine-tuning control is initiated to adjust the joint drive torque and trajectory planning parameters, bringing the rate of change of velocity and acceleration back within the optimal motion threshold combination.

[0084] Specifically, real-time perception of workpiece material, surface roughness, or viscous fluid environment characteristics refers to acquiring parameters related to the workpiece's physical properties and operating environment in real time through multimodal sensors integrated into or near the robot's end effector, such as vision sensors, tactile sensors, force / torque sensors, or infrared sensors. For example, vision sensors can identify the workpiece's geometry and surface texture, thereby inferring its material and surface roughness; force / torque sensors can sense the resistance characteristics when interacting with viscous fluids. This perceived information is processed and fused to form a comprehensive description of the workpiece's environment.

[0085] The process of dynamically calculating the optimal velocity and acceleration rate thresholds for the robot's end effector under the current operation, based on the workpiece environment characteristics, can be understood as using a pre-established physical model, empirical database, or machine learning model, taking the real-time acquired workpiece environment characteristics as input, and outputting optimal kinematic thresholds that match the current operation task and workpiece characteristics. For example, for high-precision polishing tasks, lower velocity and acceleration rate thresholds may be needed when handling soft materials to avoid damage; while when handling hard materials, the thresholds can be appropriately increased to improve efficiency. The optimal combination of motion thresholds aims to ensure that the smoothness, accuracy, and efficiency of robot motion are optimally balanced under different operating conditions.

[0086] In practical applications, during the transition period, the rate of change of velocity and the rate of change of acceleration of the robot's end effector are monitored in real time. This is achieved by using the kinematic and dynamic models within the robot controller, combined with real-time position and velocity data fed back from the joint encoders, to calculate the instantaneous rate of change of velocity (i.e., acceleration) and the rate of change of acceleration (i.e., jerk). These real-time calculated values ​​are then continuously compared with the optimal combination of motion thresholds obtained through dynamic calculation.

[0087] When the detected rate of change of velocity or acceleration exceeds the optimal combination of motion thresholds, fine-tuning control is activated. This means the system immediately triggers a closed-loop feedback mechanism. This fine-tuning control precisely adjusts the driving torque and trajectory planning parameters of the affected joints based on the degree and direction of the exceedance of the thresholds. For example, if the rate of change of acceleration is too high, the system may slightly reduce the driving torque of the joints and make minor corrections to subsequent trajectory points to smooth the velocity curve. This quickly and smoothly brings the rate of change of velocity and acceleration back within the optimal combination of motion thresholds, avoiding impacts on operational quality due to instantaneous overshoot or oscillations.

[0088] Through the above technical solution, this application achieves significant optimization of precision motion control for industrial robots. First, by sensing the workpiece environment characteristics in real time and dynamically calculating the optimal combination of motion thresholds, the robot can better adapt to operational needs in environments with different materials, surface roughness, or viscous fluids, avoiding performance degradation or operational risks caused by improper static threshold settings. Second, the real-time monitoring and fine-tuning control mechanism ensures that during path switching transitions, the rate of change of speed and acceleration of the robot's end effector remains within the optimal range matching the current working conditions, greatly improving motion smoothness and accuracy. Therefore, this application not only enhances the adaptability and robustness of the robot when performing complex tasks but also effectively reduces processing defects caused by unstable motion, improves product quality, and extends the service life of the robot and its components.

[0089] like Figure 2 As shown, this application also exemplarily illustrates a precision motion control system for an industrial robot. A specific embodiment of this application provides an industrial robot precision motion control system 100, comprising: The signal acquisition module 10 is used to acquire multi-source motion characteristic signals of robot joints, including trajectory deviation signals fed back by the joint encoder, drive current fluctuation signals, and broadband micro-vibration signals. The pattern matching module 20 is used to analyze the dynamic correlation of the multi-source motion feature signals and identify target signal patterns that are strongly correlated with thermal deformation. The decoupling operation module 30 is used to separate thermal deformation features and mechanical wear features from the aliasing spectrum based on the difference in the physical coupling mechanism between the trajectory deviation signal and the broadband micro-vibration signal according to the target signal pattern, wherein the aliasing spectrum is formed by the kinematic deviation signal and the broadband vibration signal; The dynamic compensation module 40 is used to generate motion compensation commands including trajectory compensation amounts based on the thermal deformation intensity for the thermal deformation characteristics; and to trigger a graded maintenance strategy that matches the degree of wear for the mechanical wear characteristics.

[0090] This application discloses a precision motion control system for industrial robots, designed to address the issue of decreased positioning accuracy of end effectors in traditional industrial robots during prolonged high-speed operation due to heat accumulation and mechanical wear. Through a modular design, the system achieves comprehensive perception, intelligent diagnosis, and precise intervention of the robot's operational status. Specifically, the signal acquisition module collects multi-source, multi-dimensional motion characteristic data, providing a foundation for subsequent analysis; the pattern matching module, based on this, identifies signal patterns closely related to thermal deformation through in-depth analysis; the decoupling operation module, the core of the system, cleverly utilizes the differences in the physical coupling mechanisms of different error sources to effectively separate thermal deformation and mechanical wear characteristics from aliased signals; finally, the dynamic compensation module performs real-time trajectory compensation for the separated thermal deformation characteristics and triggers a graded maintenance strategy for mechanical wear characteristics. Through this collaborative working mechanism, the system can adapt to and compensate for dynamic errors in real time and seamlessly, significantly improving the motion accuracy, stability, and reliability of industrial robots, thereby effectively ensuring the production efficiency and product quality of high-precision manufacturing.

[0091] The above description is merely an embodiment of this application and is not intended to limit the scope of protection of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of protection of this application.

Claims

1. A precision motion control method for an industrial robot, characterized in that, include: The robot joints are equipped with multi-source motion feature signals, including trajectory deviation signals fed back by the joint encoder, drive current fluctuation signals, and broadband micro-vibration signals. Analyzing the dynamic correlation of the multi-source motion feature signals and identifying target signal patterns strongly correlated with thermal deformation specifically includes: performing timestamp alignment and preliminary filtering on the acquired multi-source motion feature signals to obtain motion feature vectors; calculating the dynamic correlation between the motion feature vectors in real time and comparing the motion feature vectors with predefined physical phenomenon relationship patterns to identify target signal patterns strongly correlated with thermal deformation. Based on the target signal pattern, and considering the difference in the physical coupling mechanism between the trajectory deviation signal and the broadband micro-vibration signal, thermal deformation characteristics and mechanical wear characteristics are separated from the aliasing spectrum, wherein the aliasing spectrum is formed by the kinematic deviation signal and the broadband vibration signal; For the aforementioned thermal deformation features, a motion compensation command containing trajectory compensation is generated based on the thermal deformation intensity. Specifically, this includes: when a thermal deformation feature is detected, quantifying the pose deviation of the robot's end effector based on the thermal deformation intensity; calculating the trajectory compensation amount based on the pose deviation; and injecting the motion compensation command containing the trajectory compensation amount into the trajectory planning to adjust the driving torque in servo control. The motion compensation command is dynamically generated through a thermal deformation-pose offset mapping model, which is established based on the robot's rigid body transformation parameter calibration. Furthermore, for mechanical wear features, a graded maintenance strategy matching the wear level is triggered. Specifically, this includes: adaptively suppressing instantaneous high-frequency vibrations generated by identified high-speed motion; and gating the continuous medium-to-high-frequency vibrations generated by identified wear.

2. The precision motion control method for industrial robots according to claim 1, characterized in that, The steps for gating and managing the persistent mid-to-high frequency vibrations caused by identified wear include: The storage wear feature evolution path describes the dynamic change pattern of vibration frequency, vibration amplitude, harmonic structure and its correlation with the robot's motion state during the wear process; The frequency, amplitude, and harmonic composition of the wear vibration signal in the motion feature vector are tracked in real time, and the nonlinear correlation between the wear vibration signal and the robot's current motion state parameters is dynamically calculated to obtain the real-time wear vibration characteristics. The real-time wear vibration characteristics are compared and matched with the wear feature evolution path in real time to identify the current evolution stage of wear and predict its future trend; and Based on the current stage of wear evolution and predicted trends, the processing strategy for wear vibration signals is adjusted in real time.

3. The precision motion control method for industrial robots according to claim 2, characterized in that, The steps for adjusting the processing strategy for wear vibration signals in real time based on the current wear evolution stage and predicted trend include: The vibration frequency, vibration amplitude, and harmonic structure composition during the wear process are analyzed in real time, and the nonlinear coupling relationship between the frequency, amplitude, and harmonic composition is dynamically calculated. Assess the extent to which the nonlinear coupling relationship affects the positioning accuracy of the robot's end effector; Based on the evaluation results, the monitoring threshold and alarm triggering conditions for the wear vibration signal are dynamically adjusted. Furthermore, based on the evaluation results, the motion trajectory optimization parameters of the affected joints are dynamically adjusted.

4. The precision motion control method for industrial robots according to claim 3, characterized in that, The step of dynamically adjusting the motion trajectory optimization parameters of the affected joint based on the evaluation results includes: The storage of multi-joint wear coupling influence modes describes the nonlinear interaction between the wear vibration characteristics and the corresponding motion trajectory optimization parameters of multiple joints when wear occurs simultaneously. The vibration frequency, vibration amplitude, and harmonic structure composition of multiple joints in the motion feature vector are tracked in real time during the wear process, and the nonlinear coupling relationship between these wear vibration signals and between them and the robot's current motion state parameters is dynamically calculated to obtain the real-time multi-joint wear coupling characteristics. The real-time multi-joint wear coupling characteristics are compared and matched with the multi-joint wear coupling influence mode in real time to identify the current coupling effect type and intensity of multi-joint wear; and Based on the identified coupling effect type and intensity, the acceleration / deceleration curves and velocity limiting factors of the affected joints are dynamically adjusted.

5. The precision motion control method for industrial robots according to claim 4, characterized in that, The step of dynamically adjusting the acceleration / deceleration curves and velocity limiting factors of the affected joints based on the identified coupling effect type and intensity includes: When a robot performs a complex multi-path switching task, it calculates the transition smoothness requirements of acceleration / deceleration curves and speed limit factors between path segments based on the wear coupling characteristics of the current path segment and the estimated wear coupling characteristics of the next path segment. Before the path switching point, a gradual adjustment mechanism is activated. Based on the transition smoothness requirements, this mechanism, within a preset transition time, adjusts the acceleration / deceleration curves and speed limit factors of the current path segment to those of the next path segment using a non-linear gradual change. Ensure that the rate of change of speed and the rate of change of acceleration of the robot end effector remain within the preset threshold during the transition period.

6. The precision motion control method for industrial robots according to claim 5, characterized in that, The steps to ensure that the rate of change of velocity and rate of change of acceleration of the robot end effector remain within a preset threshold during the transition include: Real-time sensing of the workpiece's material, surface roughness, or viscous fluid environment characteristics to obtain the workpiece's environmental characteristics; Based on the workpiece environment characteristics, the optimal velocity change rate and acceleration change rate threshold of the robot end effector under the current operation are dynamically calculated to obtain the optimal combination of motion thresholds. During the transition, the rate of change of velocity and the rate of change of acceleration of the robot end effector are monitored in real time and compared with the optimal combination of motion thresholds; When the detected rate of change of velocity or acceleration exceeds the optimal motion threshold combination, fine-tuning control is initiated to adjust the joint drive torque and trajectory planning parameters, bringing the rate of change of velocity and acceleration back within the optimal motion threshold combination.

7. A precision motion control system for an industrial robot, used to implement the method as described in claim 1, characterized in that, The system includes: The signal acquisition module is used to acquire multi-source motion characteristic signals of the robot joints, including trajectory deviation signals fed back by the joint encoder, drive current fluctuation signals, and broadband micro-vibration signals. The pattern matching module is used to analyze the dynamic correlation of the multi-source motion feature signals and identify target signal patterns that are strongly correlated with thermal deformation. The decoupling operation module is used to separate thermal deformation features and mechanical wear features from the aliasing spectrum based on the difference in the physical coupling mechanism between the trajectory deviation signal and the broadband micro-vibration signal according to the target signal pattern, wherein the aliasing spectrum is formed by the kinematic deviation signal and the broadband vibration signal; The dynamic compensation module is used to generate motion compensation commands including trajectory compensation amounts based on the thermal deformation intensity for the thermal deformation characteristics; and to trigger a graded maintenance strategy that matches the degree of wear for the mechanical wear characteristics.

Images