Loader differential repair handling automation assistance system

Abstract

The present application relates to the technical field of loader maintenance, and discloses a loader differential maintenance and carrying automation auxiliary system. A maintenance parameter acquisition module of the system acquires current pose deviation data, vibration frequency spectrum characteristics and clamping force distribution parameters of a maintenance tool clamp of a differential assembly in real time, and generates a maintenance parameter set; a pose compensation module generates a dynamic compensation vector sequence according to the pose deviation data, and updates a compensation step length in combination with the vibration frequency spectrum characteristics; a carrying path planning module generates a carrying track control point sequence based on a maintenance process time window constraint and the clamping force distribution parameters; and an execution control module activates the dynamic compensation vector sequence and generates a pose correction instruction when it is detected that the pose deviation data exceeds a first activation threshold, activates the carrying track control point sequence and generates a track correction instruction when the clamping force distribution parameters exceed a second activation threshold, and inputs the instructions to a maintenance mechanical arm execution unit.

Description

Technical Field

[0001] This invention relates to the field of loader maintenance technology, specifically to an automated auxiliary system for the maintenance and handling of loader differentials. Background Technology

[0002] As a key piece of equipment in engineering construction, the differential assembly of a loader plays a crucial role in transmitting power and regulating wheel speed differences, directly affecting the machine's driving stability and operational efficiency. The differential assembly has a precise structure, and its internal gears, bearings, and other components are prone to wear and deformation under long-term high-intensity operation, requiring regular disassembly, inspection, and reassembly. During differential repair, handling and positioning are critical aspects to ensuring repair quality, requiring precise transfer of the differential assembly between different repair stations and accurate docking with repair fixtures and tools.

[0003] Traditional differential repair and handling rely heavily on manual labor or semi-automated equipment. Manual handling is not only labor-intensive but also susceptible to operator experience and physical limitations, making it difficult to guarantee the differential assembly's positional stability during transport and increasing the risk of secondary damage due to collisions or tilting. While semi-automated equipment reduces human intervention, it has significant limitations: First, parameter acquisition is limited, focusing primarily on the differential's positional coordinates while ignoring crucial parameters such as vibration status and clamping force, making it difficult to detect potential positional deviations and clamping instability during repair. Second, fixed positional compensation mechanisms, with preset compensation steps, cannot be dynamically adjusted based on real-time operating conditions, leading to insufficient or excessive compensation when the differential experiences additional displacement due to vibration. Third, the handling path planning often fails to adequately consider the time window requirements of the repair process, frequently resulting in a mismatch between the path and the process rhythm, affecting the continuity of the repair workflow. Fourth, the simple control logic lacks a tiered response mechanism for multiple parameter anomalies, failing to quickly activate corresponding correction strategies when clamping force is abnormal or positional deviation exceeds limits, increasing repair risks.

[0004] With the expanding application of automation technology in the field of construction machinery maintenance, higher requirements are placed on the accuracy and adaptability of differential repair and handling. Existing technologies show significant shortcomings in the comprehensiveness of parameter perception, the dynamism of compensation strategies, the suitability of path planning, and the targeted nature of control response. These shortcomings make it difficult to meet the high-efficiency operation needs in complex maintenance scenarios, thus hindering the improvement of automation levels in loader differential repair. Summary of the Invention

[0005] The purpose of this invention is to provide an automated auxiliary system for the maintenance and handling of differential gears in loaders, so as to solve the problems mentioned in the background art.

[0006] To achieve the above objectives, the present invention provides an automated auxiliary system for the maintenance and handling of loader differentials, the system comprising:

[0007] The maintenance parameter acquisition module is used to collect the current position deviation data, vibration spectrum characteristics and clamping force distribution parameters of the maintenance tooling fixture in real time, and generate a set of maintenance parameters.

[0008] The pose compensation module is used to generate a dynamic compensation vector sequence based on the current pose deviation data in the maintenance parameter set, and update the compensation step size of the dynamic compensation vector sequence based on the vibration spectrum characteristics.

[0009] The transport path planning module is used to generate a sequence of transport trajectory control points based on the maintenance process time window constraints and the clamping force distribution parameters in the maintenance parameter set.

[0010] The execution control module is used to activate the dynamic compensation vector sequence and generate a pose correction command when the current pose deviation data is detected to exceed a first activation threshold; and to activate the transport trajectory control point sequence and generate a trajectory correction command when the clamping force distribution parameter is detected to exceed a second activation threshold.

[0011] The execution control module is also used to input the pose correction command or trajectory correction command to the execution unit of the maintenance robot arm.

[0012] Preferably, the maintenance parameter acquisition module includes:

[0013] A multi-dimensional sensor array is used to synchronously collect the axial offset, radial runout waveform, and pressure feedback values ​​of each contact point of the tooling fixture during the disassembly phase of the differential assembly.

[0014] The material identification unit is used to identify the differential housing material type and match the corresponding reference clamping force safety threshold based on the characteristic frequency range in the radial runout waveform.

[0015] The parameter fusion unit is used to convert the axial offset into a three-dimensional spatial offset vector and calculate the difference between the pressure feedback value of each contact point and the reference clamping force safety threshold to generate a clamping force deviation distribution map.

[0016] The set generation unit is used to integrate the three-dimensional spatial offset vector, radial runout waveform, and clamping force deviation distribution map into the maintenance parameter set.

[0017] Preferably, the pose compensation module includes:

[0018] The dynamic learning rate adjustment unit is used to calculate the current compensation learning rate based on the rate of change of the main frequency amplitude of the vibration spectrum characteristics, wherein the current compensation learning rate is negatively correlated with the rate of change of the main frequency amplitude.

[0019] The vector generation unit is used to generate an initial compensation vector sequence based on the current pose deviation data in the maintenance parameter set;

[0020] The step size optimization unit is used to iteratively update the initial compensation vector sequence using the current compensation learning rate to generate the dynamic compensation vector sequence containing the compensation direction and compensation amount.

[0021] Preferably, the maintenance parameter acquisition module also outputs the radial runout waveform to the pose compensation module;

[0022] The dynamic learning rate adjustment unit is specifically used to extract the energy attenuation slope of a preset frequency band in the radial jitter waveform, and adjust the attenuation coefficient of the current compensation learning rate according to the energy attenuation slope.

[0023] Preferably, the transport path planning module includes:

[0024] The time window parsing unit is used to parse the end time of the disassembly process and the start time of the installation process in the maintenance process flow, and generate a handling time constraint window.

[0025] The trajectory generation unit is used to identify the weak clamping force area and generate an initial handling path by avoiding the weak clamping force area based on the clamping force distribution parameters in the maintenance parameter set.

[0026] The control point optimization unit is used to uniformly divide the initial transport path within the transport time constraint window to generate a sequence of transport trajectory control points containing timestamps.

[0027] Preferably, the maintenance parameter acquisition module also outputs the mass distribution parameters of the differential assembly to the transport path planning module;

[0028] The control point optimization unit is specifically used to calculate the inertial moment of each path segment based on the mass distribution parameters, and to adjust the timestamp interval of the transport trajectory control point sequence based on the inertial moment.

[0029] Preferably, the execution control module includes:

[0030] The priority determination unit is used to prioritize the execution of the pose correction command when the current pose deviation data and the clamping force distribution parameters both exceed the activation threshold.

[0031] The instruction fusion unit is used to add the residual pose deviation to the trajectory correction instruction after the pose correction instruction is executed;

[0032] The execution unit drive unit is used to drive the maintenance robotic arm according to the fused trajectory correction instructions.

[0033] Preferably, the system further includes:

[0034] The feedback calibration module is used to collect the actual motion trajectory data of the maintenance robot arm and compare the deviation with the expected trajectory of the trajectory correction command to generate a trajectory error mapping table.

[0035] The feedback calibration module is also used to feed back the trajectory error mapping table to the transport path planning module;

[0036] The transport path planning module corrects the generation rules of the transport trajectory control point sequence based on the trajectory error mapping table.

[0037] Preferably, the system further includes:

[0038] The collaborative verification module is used to acquire load-bearing deformation data and environmental vibration spectrum of the maintenance platform in real time during the handling process.

[0039] Preferably, the collaborative verification module includes the following functions:

[0040] When the load-bearing deformation data exceeds the deformation threshold, the pose compensation module is triggered to regenerate the dynamic compensation vector sequence.

[0041] When the environmental vibration spectrum resonates with the vibration spectrum characteristics in the maintenance parameter set, the transport path planning module is triggered to replan the transport trajectory control point sequence.

[0042] Compared with the prior art, the beneficial effects of the present invention are:

[0043] This automated auxiliary system for differential repair and handling of loaders provides comprehensive and dynamic technical support for the differential repair and handling process through the collaborative work of multiple modules. The repair parameter acquisition module can capture the current positional deviation data, vibration spectrum characteristics, and clamping force distribution parameters of the differential assembly in real time. The resulting set of repair parameters covers key status information during the repair and handling process, enabling the system to have a more comprehensive perception of the repair scenario and to promptly detect potential positional deviations, vibration interference, and clamping instability.

[0044] The pose compensation module generates a dynamic compensation vector sequence based on pose deviation data and updates the compensation step size by combining vibration spectrum characteristics. This allows the pose correction process to no longer rely on fixed parameters, but to flexibly adjust the compensation rhythm according to the real-time vibration state of the differential. When the differential vibrates at different frequencies and amplitudes due to external interference or its own structural characteristics, the compensation step size can be adapted accordingly, avoiding compensation lag or over-adjustment that may occur with a fixed step size, thus improving the accuracy and timeliness of pose correction.

[0045] The transport path planning module incorporates maintenance process time window constraints and clamping force distribution parameters, resulting in a transport trajectory control point sequence that better matches the actual maintenance process requirements. The time window constraint ensures that the differential's transfer rhythm between workstations matches the maintenance process time nodes, reducing process waiting or overruns. The integration of clamping force distribution parameters allows path planning to avoid trajectory segments that may cause abnormal fluctuations in clamping force, reducing the risk of clamping instability caused by unreasonable paths, and making the transport process more consistent with the differential's structural characteristics and the clamp's stress characteristics.

[0046] The execution control module implements a tiered response to different abnormal states by setting a first activation threshold and a second activation threshold. When the pose deviation data exceeds the first activation threshold, the system quickly activates the dynamic compensation vector sequence and generates a pose correction command to promptly correct the deviation. When the clamping force distribution parameter exceeds the second activation threshold, the system activates the transport trajectory control point sequence and generates a trajectory correction command to adjust the transport path to improve the clamping state. This targeted response mechanism avoids the inadequacy of a single control logic for complex working conditions, enabling the system to take precise intervention measures according to the specific type of abnormality, reducing ineffective operations.

[0047] The synergistic effect of each module enables the entire system to autonomously perceive, dynamically adjust, and precisely control in complex maintenance environments, reducing reliance on human experience and improving the automation and stability of the differential repair and handling process for loaders, thus better adapting to the diverse working conditions required in differential repair. Attached Figure Description

[0048] Figure 1 This is a timing diagram of the automated auxiliary system for repairing and handling the differential of a loader as described in this invention.

[0049] Figure 2 This is a flowchart of the pose compensation module.

[0050] Figure 3 A flowchart illustrating the workflow of dynamic learning rate adjustment in the pose compensation module;

[0051] Figure 4 A flowchart for optimizing control points in the transport path planning module. Detailed Implementation

[0052] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0053] Please see Figure 1 This invention provides an automated auxiliary system for the maintenance and handling of differential gears in loaders, the system comprising:

[0054] The system achieves precise positioning and safe handling during differential repair through multi-module collaboration. It includes a repair parameter acquisition module, a posture compensation module, a handling path planning module, and an execution control module. The repair parameter acquisition module acquires real-time posture deviation, vibration characteristics, and clamp force data of the differential assembly through a sensor array, forming a structured set of repair parameters. The posture compensation module generates a dynamic compensation vector based on the posture deviation data and adaptively adjusts the compensation step size according to vibration characteristics. The handling path planning module combines process time constraints and clamping force distribution to generate a sequence of timestamped handling trajectory control points. The execution control module selectively activates posture correction or trajectory correction commands based on parameter thresholds, driving the repair robotic arm to complete precise operations.

[0055] Example 1: See Figure 2 The maintenance parameter acquisition module employs a modular design for its multi-dimensional sensor array. Laser displacement sensors are mounted on the orthogonal coordinate system of the robotic arm's end effector, with three measuring heads arranged along the X, Y, and Z axes to form a spatial measurement network. Each measuring head uses a 650nm semiconductor laser with a beam diameter controlled within 0.5mm, achieving a measurement resolution of 0.01mm. The sensor array connects to the main control system via industrial Ethernet, and data transmission uses a timestamp synchronization mechanism to ensure the temporal consistency of measurement data across all axes. The triaxial accelerometer utilizes a miniature sensor manufactured using MEMS technology, covering a range of ±50g, with a flat frequency response characteristic within the 5-500Hz range. The accelerometer is mounted on a predetermined measurement reference surface on the differential housing and can be quickly installed and removed via a magnetic base. The signal conditioning circuit incorporates an anti-aliasing filter, with a sampling rate set to 1kHz and an ADC conversion accuracy of 16 bits.

[0056] The material identification unit runs on an embedded processor, and its algorithm library contains characteristic frequency templates for common differential housing materials such as cast iron, cast steel, and aluminum alloy. After windowed Fourier transform, the vibration signal's spectral features in the 50-250Hz frequency band are extracted and pattern matched against the template library. The matching process employs a dynamic time warping algorithm, allowing a frequency offset tolerance of ±5%. The identification result outputs a material type code and a confidence score; a manual review request is triggered when the confidence score falls below 85%. The baseline clamping force safety threshold corresponding to the material is stored in an encrypted database, including static clamping force and correction coefficients under dynamic operating conditions. Data access employs a hierarchical access control mechanism.

[0057] The pressure sensor matrix employs flexible thin-film pressure sensing technology, with 64 sensing units evenly distributed in an 8×8 array on the contact surface of the fixture. Each sensing unit has an effective sensing area of ​​10mm×10mm, a pressure measurement range of 0-5MPa, and nonlinearity error controlled within ±1%FS. The sensing matrix achieves parallel data reading through distributed acquisition nodes, with a sampling period of 10ms. After temperature compensation and zero-point calibration, the pressure data is transmitted to the parameter fusion unit for further processing. The contact pressure distribution is represented using polar coordinates, dividing the fixture circumference into 32 sectors, and calculating the average pressure value for each sector. The data processing flow of the parameter fusion unit includes three main stages. The spatial coordinate transformation stage converts the axial offset measured by the laser displacement sensor into a three-dimensional vector in the robot arm's base coordinate system using a homogeneous transformation matrix, considering dimensional changes caused by fixture installation offset and temperature during the transformation. The pressure data analysis stage uses a sliding window statistical method to calculate the mean and standard deviation of each pressure sensing unit within the most recent 1-second time window; data points exceeding the 3σ range are marked as outliers. During the clamping force assessment phase, the real-time pressure distribution is compared point by point with the safety threshold matched to the material, generating a two-dimensional deviation map represented by pseudo-color coding, with the color range set to -30% to +30% of the safety threshold percentage.

[0058] The data integration process of the collection generation unit adopts a hierarchical data structure. The top layer is a time synchronization index, recording the absolute timestamp of data acquisition and the percentage of progress relative to the maintenance procedure. The middle layer contains three types of core parameters: spatial location data is represented in a right-handed Cartesian coordinate system, with coordinate transformation parameters; vibration characteristic data includes both time-domain waveforms and frequency-domain spectra; and clamping force data retains the original pressure matrix and derived statistical characteristics. The bottom layer contains quality control information, recording the sensor self-test status and data check codes. The final generated maintenance parameter set is encapsulated as a standardized data package and made available for other modules to read via shared memory.

[0059] The dynamic learning rate adjustment unit of the pose compensation module achieves closed-loop coupling between vibration characteristics and compensation parameters. The dominant frequency band identification of the vibration spectrum employs a peak detection algorithm, searching for local amplitude maximums within the 50-150Hz range, requiring a minimum frequency interval of no less than 10Hz between adjacent peaks. The dominant frequency amplitude change rate is calculated using least-squares linear fitting, with the time window length adaptively adjusted according to vibration intensity, ranging from 100ms to 1s. The learning rate adjustment curve uses a piecewise linear function, remaining constant when the amplitude change rate is less than 2% / s, decreasing inversely proportionally when it exceeds 5% / s, and employing smooth interpolation in the intermediate transition region.

[0060] The initial compensation vector calculation of the vector generation unit is based on the robot's kinematic model. The input 3D spatial offset is first normalized and converted into local coordinate system parameters with the end effector of the robotic arm as the origin. Then, the required compensation displacement for each joint is calculated using an inverse kinematics solver, taking into account the current posture of the robotic arm and joint constraints. For redundant degree-of-freedom systems, the gradient projection method is used to optimize the solution space selection. The generated initial vector sequence contains 6 degree-of-freedom components, each with a confidence assessment and error boundary estimation.

[0061] The iterative update process of the step-size optimization unit employs a model predictive control strategy. Before each iteration, the system predicts the possible vibration response after executing the current compensation vector. The prediction model is an ARMA time series model built based on historical data. The residual between the actual vibration spectrum and the prediction result is used to adjust the learning rate; the larger the residual, the larger the adjustment of the learning rate. The compensation direction optimization uses the conjugate gradient method to avoid oscillations while ensuring convergence speed. The compensation amount adjustment introduces a momentum term and retains part of the adjustment amount from the previous iteration to improve stability. The final output dynamic compensation vector sequence includes an execution priority marker and a time validity indicator.

[0062] The calibration and maintenance mechanism for the multi-dimensional sensor array includes both online and offline modes: online calibration utilizes a reference block mounted on a fixture, automatically performing zero-point calibration and sensitivity verification daily; offline calibration is conducted in a professional metrology laboratory, using a standard vibration table and laser interferometer as references, with a calibration cycle not exceeding 6 months. Sensor health status monitoring is continuous, with real-time alarms for abnormalities such as excessive drift and increased noise. The data acquisition software implements a double-buffering mechanism to ensure that no valid data is lost during sensor calibration.

[0063] The material recognition unit's template library supports online updates and maintenance: when a new differential material is introduced, typical vibration spectrum characteristics are collected through expert mode, and then added to the template library after wavelet denoising and feature extraction. The template matching algorithm uses dynamic weight allocation, assigning different feature weights to the low-frequency band (50-100Hz) and the high-frequency band (100-250Hz). The recognition results are output with detailed spectrum comparison charts and similarity analysis reports, supporting visual verification during manual review.

[0064] The temperature compensation algorithm for the pressure sensing matrix is ​​based on temperature sensor data installed next to each sensing unit. The compensation model uses a cubic polynomial fit, and the coefficients are determined through factory calibration experiments. The pressure data acquisition channel is equipped with a programmable gain amplifier, and the range is automatically adjusted according to the current pressure range. The signal conditioning circuit incorporates a hardware filter and an overvoltage protection module to ensure measurement stability in complex electrical environments at maintenance sites. The pressure data in the matrix edge regions is compensated using a spatial interpolation algorithm to improve the continuity of the overall distribution map.

[0065] Example 2: See Figure 3 The radial runout waveform raw signal transmitted from the maintenance parameter acquisition module to the pose compensation module undergoes multi-stage preprocessing. During signal acquisition, an anti-aliasing filter is used, with a cutoff frequency set to 500Hz and a sampling rate fixed at 2kHz to ensure compliance with the Nyquist sampling theorem. The raw signal first undergoes DC component elimination, and then passes through a fourth-order Butterworth bandpass filter to retain the effective frequency band of 50-200Hz. Signal segmentation uses an overlapping window method with a window length of 200ms and an overlap rate of 50%. Each window applies a Hanning window function to reduce spectral leakage. The preprocessed signal is then converted to the frequency domain using a Fast Fourier Transform, achieving a spectral resolution of 5Hz.

[0066] The energy feature extraction of the dynamic learning rate adjustment unit employs complex Morlet wavelet transform, with a center frequency set to 100Hz and a bandwidth parameter of 3. After modulus calculation, the wavelet coefficient matrix is ​​integrated within the 50-200Hz frequency band. The energy attenuation slope is calculated using linear regression, with the time window length adaptively adjusted based on the current vibration intensity, ranging from 100ms to 500ms. When the absolute value of the energy slope exceeds a set threshold, the learning rate adjustment mechanism is activated, following the formula:

[0067] ;

[0068] in: This represents the current compensation learning rate. The initial learning rate baseline value, This is the attenuation sensitivity coefficient (default value 0.25). This represents the energy decay slope. This formula ensures that the system automatically reduces the learning rate to avoid overcompensation when high-frequency vibration energy decays rapidly. The decay coefficient is dynamically adjusted based on historical data, with the average magnitude of the last 10 adjustments serving as a correction reference.

[0069] The time window parsing unit of the material handling path planning module communicates with the maintenance management system via the OPCUA protocol to acquire process time data in real time. The determination of the disassembly process's end time t1 includes dual verification via a robotic arm return completion signal and a vision system confirmation signal. The installation process's start time t2 is synchronized with a warm-up time buffer, initiating the preparation program 30 seconds before the predetermined time. The time constraint window boundaries have safety margins: the window start time is delayed by 200ms, and the end time is advanced by 300ms to prevent conflicts during process transitions.

[0070] The weak region identification algorithm of the trajectory generation unit employs morphological processing. The clamping force deviation distribution map is first Gaussian smoothed to eliminate local noise interference. Then, adaptive threshold segmentation is applied to mark regions with pressure values ​​below 70% of the safety threshold as candidate regions. A region growing algorithm connects adjacent candidate points to form continuous regions, filtering out isolated regions with an area less than 5 cm². The final weak region boundary is represented by the minimum convex hull and extended outward by 3 mm as a safety buffer zone. Path planning uses an improved RRT* algorithm, adding a weak region repulsion field to the traditional random sampling, ensuring the planned path naturally avoids high-risk areas.

[0071] The time-uniform segmentation algorithm for the control point optimization unit considers the dynamic constraints of the robotic arm, dividing the total handling time into several time periods with a length of no less than 50ms. Within each time period, the density of feasible path points is calculated based on the maximum acceleration limits of each joint of the robotic arm. For straight path segments, the spacing between control points remains uniform; in turning areas, the control point density increases as the radius of curvature decreases. The timestamp system uses a dual-track system of relative and absolute times, including both the millisecond offset relative to the start of the process and recording in standard NTP time format.

[0072] The mass distribution parameters transmitted from the maintenance parameter acquisition module to the handling path planning module include the inertia tensor matrix and the centroid coordinates. The inertia parameters are pre-calculated using 3D modeling software and stored in a standardized data format. Changes in mass distribution are monitored in real time during handling, and parameter updates are triggered when loose or missing components are detected. The mass data is coupled with the robotic arm's dynamics model, and the load inertia ratio of each axis is pre-calculated during the trajectory planning phase.

[0073] The inertial torque compensation algorithm of the control point optimization unit is based on the Euler dynamics equations. For each path segment, the equivalent inertia of the robotic arm's end effector in the direction of motion is calculated. Torque demand prediction considers the combined effects of Coriolis force and centripetal force, employing feedforward control to compensate in advance. When the predicted torque exceeds a threshold, the system automatically extends the time allocation for that path segment, with the adjustment magnitude proportional to the degree of exceedance. The adjustment of the timestamp interval follows the principle of motion smoothness, with the time interval change rate between adjacent control points not exceeding 20%.

[0074] The signal synchronization mechanism of the multi-dimensional sensor array adopts a hardware triggering method. The laser displacement sensor and the triaxial accelerometer share the same external clock source, and the clock jitter is controlled within 1μs. The pressure sensor matrix adopts a time-division multiplexing acquisition scheme, with 64 channels sampled in 8 groups, and the delay between channels within the group is less than 10μs. All sensor data packets are appended with a uniform timestamp, and the time reference is derived from the GPS disciplined clock module.

[0075] The spectral feature library of the material identification unit adopts a hierarchical storage structure. The basic layer contains the baseline spectrum of standard materials, while the extended layer stores the variation features collected in the field. The matching algorithm uses dynamic time-warped distance as a similarity metric, allowing the spectrum to elastically expand and contract along the frequency axis. During the identification process, the feature library weights are updated in real time, increasing the matching priority for frequently occurring material types. When a new material appears, the system automatically starts the learning mode, records typical vibration patterns, and requests manual annotation.

[0076] The spatial coordinate transformation of the parameter fusion unit adopts quaternion representation. The offset measured by the laser displacement sensor is first transformed to the fixture coordinate system, and then mapped to the robot arm base coordinate system through a homogeneous transformation matrix. The transformation parameters include installation offset, temperature compensation, and mechanical deformation correction. Pressure data fusion adopts Bayesian estimation method, combining current measurements with historical statistical characteristics to reduce the impact of random errors. The fusion results include uncertainty assessment, with the confidence level of each parameter expressed in the form of a covariance matrix.

[0077] The data encapsulation format of the collection generation unit adopts a hybrid encoding scheme. The structured data part uses JSON format to record parameter names and values, while the binary part stores the raw waveform and image data. Data packets are digitally signed and time-stamped to ensure integrity and immutability during transmission. A partitioned locking mechanism is implemented in the shared memory area to prevent data conflicts caused by simultaneous access from multiple modules. Data updates adopt a publish-subscribe model, and change notifications are broadcast via message queues.

[0078] The dynamic learning rate adjustment unit's state monitoring comprises multiple parallel threads: a vibration characteristic analysis thread continuously tracks changes in the main frequency amplitude, a learning rate calculation thread maintains current compensation parameters, and a system stability assessment thread monitors closed-loop performance metrics. Data is exchanged between threads via a lock-free circular buffer to ensure real-time performance. The learning rate adjustment process employs a gradual strategy, with each adjustment not exceeding 30% of the previous value to avoid drastic changes that could cause system oscillations.

[0079] The inverse kinematics solver of the vector generation unit supports switching between multiple algorithms. It defaults to analytical methods for solving closed-form solutions, automatically switching to numerical iteration near singular configurations. Solution uniqueness is determined based on joint constraints and workspace reachability analysis. For redundant degree-of-freedom systems, the null space optimization objective is set to minimize the sum of squared joint moments. The generated initial vector sequence includes a Jacobian matrix condition number evaluation; recalculation is triggered when numerical stability falls below a threshold.

[0080] The model predictive controller of the step-size optimization unit adopts a rolling time-domain strategy, with the prediction time-domain length dynamically adjusted according to the system response speed, ranging from 0.5s to 2s. The optimization objective function balances pose error and energy consumption, with weighting coefficients adaptively varying with vibration intensity. The compensation vector generated in each iteration undergoes a feasibility check, including joint limit verification, velocity and acceleration constraint verification, and collision detection. The execution results are fed back to update the prediction model parameters, forming a closed-loop learning mechanism.

[0081] Example 3: See Figure 4 The inertial measurement unit of the maintenance parameter acquisition module uses a combination of a high-precision MEMS gyroscope and accelerometer, mounted on the reference measurement plane of the differential assembly. The gyroscope's measurement range covers... The nonlinear error is controlled within 0.1%FS, the accelerometer range is ±10g, and the bandwidth is set to 500Hz. Sensor data is transmitted via an SPI interface with a sampling rate of 1kHz. The raw signal is output after digital filtering and temperature compensation. The measurement process of mass distribution parameters includes both static and dynamic modes. In static mode, the center of mass position is calculated using a multi-point weighing method, while in dynamic mode, the moment of inertia is identified using an excitation response method. The measurement results are stored in a standardized matrix format, containing complete parameters such as mass value, center of mass coordinates, and inertia tensor.

[0082] After receiving the mass distribution parameters, the control point optimization unit of the transport path planning module first performs coordinate system alignment. The inertial parameters of the differential body are transformed to the coordinate system of the robotic arm's end effector, taking into account the added mass and geometric offset of the gripper. The dynamic analysis of the path segment employs a piecewise linearization method, discretizing the continuous trajectory into several characteristic motion states. The inertial torque calculation at each state point includes two components: translation and rotation. The translation component is based on Newton's second law, and the rotation component is based on Euler's equations of motion. The torque demand prediction algorithm uses a forward recursive approach, calculating the load inertia joint by joint starting from the robotic arm base.

[0083] The priority judgment unit of the execution control module is equipped with a multi-level triggering mechanism. Position deviation monitoring uses a sliding window statistical method to calculate the average and standard deviation of the most recent 10 sampling periods. When the deviation exceeds 3σ for three consecutive periods, a level one alarm is triggered, entering a warning state without interrupting the current operation. Clamping force deviation monitoring uses regional aggregation analysis, dividing the clamping contact surface into eight functional areas. A level two alarm is triggered when the average pressure in any area deviates from the safety threshold by 20%. When both alarms occur simultaneously, the system starts an interrupt handler, saves the current status register, and executes on-site protection actions.

[0084] The instruction fusion unit's processing flow comprises two stages: spatial mapping and temporal alignment. Residual pose deviations are first projected onto the robotic arm's workspace and decomposed into components along the trajectory tangent and normal directions. The tangent component is absorbed by adjusting the trajectory velocity distribution, while the normal component is compensated for through path offset. In the temporal alignment stage, correction instructions are inserted into the existing instruction queue, taking into account the motion synchronization of each axis of the robotic arm to avoid time differences in arrival at the target point exceeding allowable limits. The weighting coefficients during the fusion process are dynamically adjusted based on the fixture pressure distribution; the higher the pressure imbalance, the greater the normal direction compensation weight.

[0085] The actuator drive unit adopts a distributed control architecture, with each joint axis configured with an independent servo driver, achieving synchronous control via an EtherCAT bus. Command issuance uses a periodic synchronous position mode with a period time set to 2ms. The trajectory interpolation algorithm employs a seven-segment S-shaped velocity curve to ensure continuous acceleration variation. The actual position feedback of each axis is compared with the commanded position; a protective stop is triggered when the deviation exceeds a safety threshold. Drive parameters, including velocity feedforward gain and acceleration feedforward gain, are automatically adjusted based on the load inertia.

[0086] The dynamic model of the maintenance robotic arm is maintained in the system database, including the mass properties of each link, joint friction characteristics, and motor dynamic parameters. The model update mechanism periodically runs a self-identification program to obtain the latest parameters using the excitation-response method. During online identification, the robotic arm moves along a predefined test trajectory, collecting current, velocity, and position signals from each joint. Data processing employs recursive least squares, and the identification results are fused with the theoretical model to generate a hybrid parameter set. Model validation is achieved by comparing the predicted torque with the actual motor current; a re-identification is triggered when the error exceeds 15%.

[0087] A real-time update mechanism for mass distribution parameters monitors changes in the differential assembly's condition. A vision system periodically scans the differential's exterior to detect missing parts or positional misalignments. Upon detecting an anomaly, a mass reassessment procedure is initiated, invoking the parametric adjustment interface of the 3D model. Dynamic parameter updates caused by mass changes are processed incrementally, recalculating only the affected components. The system maintains a historical change record for analyzing the correlation between mass distribution and vibration characteristics.

[0088] The temperature compensation algorithm for the inertial measurement unit (IMU) is based on temperature probe data distributed around the sensor. Each probe uses a PT100 platinum resistance thermometer, achieving a measurement accuracy of ±0.1℃. The compensation model is established through polynomial fitting, with coefficients determined during factory calibration. The real-time compensation process considers the influence of temperature gradients, performing a weighted average of temperature differences across different parts of the sensor. Calibration data is stored in non-volatile memory, supporting power-off saving and automatic power-on loading.

[0089] The timestamp interval adjustment of the control point optimization unit follows the principle of energy optimization. While ensuring motion accuracy, the control point distribution that minimizes the total energy consumption of the robotic arm is selected. The energy consumption model includes three components: motor copper loss, iron loss, and mechanical friction loss, calculated based on the real-time operating points of each joint. The adjustment algorithm employs a heuristic search method, performing local optimization based on an initial uniform distribution. The rate of change of the interval between adjacent control points is limited to within 20% to avoid sudden acceleration changes.

[0090] The priority judgment unit employs a preemptive strategy for multi-task scheduling, setting pose correction tasks as high priority and allowing them to interrupt lower-priority trajectory correction tasks. The task switching process saves the context of the interrupted task, including the instruction pointer and register state. Resource conflict handling uses a priority inheritance protocol to prevent high-priority tasks from being blocked by low-priority tasks. Task execution time is monitored in real time, and tasks that time out without completion trigger a watchdog reset.

[0091] The spatial mapping algorithm of the command fusion unit is based on the robot's Jacobian matrix, transforming pose deviations in Cartesian space to joint space, and considering the singular characteristics of the current configuration of the robotic arm. Singular region processing employs damped least squares, adding a regularization term during matrix inversion. The fused joint commands undergo amplitude limiting to ensure that the motion parameters of each axis are within allowable ranges. Collision detection is performed before command issuance, and bounding box algorithms are used to verify path safety.

[0092] The fault detection mechanism of the actuator drive unit includes multiple protections. The current loop monitors the balance of motor phase currents and detects phase loss or short circuit faults. The speed loop monitors tracking errors; if the deviation continues to exceed limits, it is judged as mechanical jamming. The position loop uses dual encoder verification: the main encoder is a high-resolution absolute encoder, and the secondary encoder is an incremental encoder as a redundancy backup. The cooling system monitors the driver temperature in real time and automatically derates when it exceeds a safe threshold. All fault events are recorded in the black box memory, supporting post-event analysis.

[0093] The calibration and maintenance of the robotic arm includes daily checks and periodic maintenance. Daily checks are performed automatically each time the system starts, including zero-return operations for each axis and load testing. Periodic maintenance is triggered based on runtime counts and includes harmonic reducer lubrication, cable inspection, and encoder calibration. The remaining life of critical components is predicted based on operating hours and load statistics, providing early warnings of potential failures. Calibration data management uses version control, supporting regression to any historical version.

[0094] Example 4: The optical tracking system of the feedback calibration module adopts a six-degree-of-freedom infrared camera network scheme, with four capture units arranged around the maintenance work area to form a three-dimensional measurement space covering 3m×3m×2m. Each capture unit integrates a 120Hz sampling rate CMOS sensor and an infrared LED array, tracking the reflective spheres on the end effector of the robotic arm through active marker point recognition technology. The marker points are arranged in an asymmetrical geometric pattern, with four 15mm diameter reflective spheres installed around the actuator flange, the center point serving as the main reference point, and the other three points used for attitude calculation. The system calibration process uses standard length rods to verify spatial accuracy at multiple locations within the measurement space, ensuring that the overall spatial positioning error does not exceed 0.1mm.

[0095] The trajectory error mapping table is constructed using a spatiotemporal alignment method: the actual trajectory data collected by the optical system and the expected trajectory issued by the transport path planning module are synchronized on the time axis, with the synchronization signal originating from the PPS pulse of the system master clock. Error analysis is performed in three dimensions: position error is calculated using Euclidean distance, direction error is represented by quaternion angles, and velocity error is obtained through differential position data. Error data is statistically analyzed in 100ms time windows, generating structured records containing multiple indicators. A typical example fragment of the trajectory error mapping table is shown in Table 1.

[0096] Table 1. Example of trajectory error mapping (time window 100-400ms):

[0097]

[0098] After receiving the error mapping table, the transport path planning module initiates the control point sequence correction program. The correction process is divided into offline and online phases. The offline phase analyzes historical error data to identify systematic deviation patterns, such as persistent positive deviations or speed-related errors within a specific path interval. The online phase monitors the current error trend in real time and dynamically fine-tunes subsequent control points during trajectory execution. The correction algorithm employs the error feedforward compensation principle, pre-adding the statistically obtained average error to the target position. For periodically occurring error patterns, the system establishes an error prediction model, calculating the compensation amount in advance based on the current motion state.

[0099] The deformation monitoring system of the collaborative verification module deploys twelve fiber optic grating sensors at key locations on the maintenance platform. These sensors are distributed in a mesh pattern along the main beam of the platform, with a measurement point spacing of 300mm, covering all major load-bearing structures. The center wavelength drift of the gratings is read using a demodulator, achieving a resolution of 1pm, corresponding to a strain measurement accuracy of 1με. The deformation reconstruction algorithm, based on a finite element model, converts discrete-point strain data into a three-dimensional deformation field for the entire platform. Temperature compensation employs a reference grating method, deploying compensation sensors in non-stressed areas of the platform to eliminate the influence of ambient temperature. Deformation threshold settings consider material properties: a warning threshold of 0.1mm for the elastic deformation stage and an emergency threshold of 0.3mm for the plastic deformation stage.

[0100] The environmental vibration monitoring unit employs a triaxial seismic accelerometer array, with four monitoring points located at the four corners of the platform base. The sensors have a range of ±2g and a low-frequency response extending to 0.5Hz, meeting the requirements for quasi-static vibration measurement. Spectrum analysis uses 1 / 3 octave band segmentation, with 31 frequency bands ranging from 1Hz to 250Hz. A resonance detection algorithm calculates the coherence function between the differential vibration spectrum and the environmental vibration spectrum, initiating an early warning in frequency bands where the coherence coefficient exceeds 0.7. Correlation analysis between vibration data and deformation monitoring results establishes an excitation-response relationship model to distinguish between structural vibration and external disturbances.

[0101] The neural network training for the feedback calibration module employs an incremental learning strategy. The input layer receives a 20-dimensional feature vector containing motion state parameters such as current position, velocity, acceleration, and historical error. The hidden layers are designed as a three-layer structure, with 64, 32, and 16 neurons per layer, respectively, and a variant of ReLU activation function. The output layer predicts the error compensation for six degrees of freedom. Training data comes from the long-term accumulation of the error mapping table, and the dataset is automatically updated after each system run. Network parameter optimization uses stochastic gradient descent with momentum, and the learning rate is adaptively adjusted according to the proportion of new data.

[0102] The calibration and maintenance of the optical tracking system includes daily rapid verification and periodic comprehensive calibration. Daily verification uses a reference target fixed in the working area to check the system's zero-point drift and measurement stability. Comprehensive calibration is performed every three months by professional metrologists using a laser tracker as the reference device. Calibration data management employs version control, retaining historical calibration records for error trend analysis. System health is monitored through a built-in self-test program, including LED light source intensity detection and image sensor noise assessment.

[0103] The time alignment algorithm for trajectory error analysis processes multi-source, heterogeneous velocity sampling data. Optical system data is acquired at a fixed frequency of 120Hz, while robotic arm controller data is recorded at 500Hz. The system employs cubic spline interpolation to achieve data rate matching. Clock synchronization is based on the IEEE 1588 precision time protocol, with master-slave clock deviation controlled within 100μs. Data delay compensation considers signal transmission time and processing time, establishing a complete time chain model from acquisition to display.

[0104] The fiber optic sensor network of the deformation monitoring system employs wavelength division multiplexing (WDM) technology, with twelve sensing gratings spaced 0.8 nm apart at their center wavelengths, distributed within the 1525 nm to 1535 nm band. The demodulator's scan rate is set to 1 kHz, and the dynamic strain measurement range is ±5000 nm. The signal conditioning circuit includes a programmable gain amplifier and a digital filter to adapt to the acquisition needs of signals of varying intensities. The sensor is mounted using a special adhesive that withstands the oil and vibration environments encountered during maintenance.

[0105] Coherent analysis of the environmental vibration spectrum employs the sliding window method, with a calculation window length of 1 second and a sliding step size of 200 ms, jointly analyzing vibration characteristics in the time and frequency domains. Resonance frequency band identification combines power spectral density and phase information to eliminate false positives for anti-resonance points. For confirmed resonance frequency bands, the system automatically generates band-stop filter parameters and injects them into the feedforward channel of the control system. Vibration monitoring data is stored in association with maintenance process parameters, establishing a correspondence between vibration characteristics and operation types.

[0106] The iterative optimization process for error compensation employs a closed-loop verification mechanism. The trajectory execution results after each correction are fed back to the training set for updating neural network parameters. The evaluation of the compensation effect considers not only the reduction in the absolute value of the error but also monitors the rate of change of higher-order derivatives to ensure that motion smoothness is not reduced. The compensation amount is verified through simulation before application, with the correction effect pre-tested on a digital twin model. Compensation rules that have not been used for a long time automatically have their weights decayed to prevent outdated parameters from affecting current performance.

[0107] The collaborative verification module's data fusion center integrates multi-source information, correlating and analyzing deformation, vibration, and error data under a unified time reference to establish a multi-dimensional state vector. Anomaly detection employs multi-indicator joint judgment, requiring abnormal readings from at least two independent sensors to trigger an alarm. The data visualization interface simultaneously displays spatial deformation cloud maps and spectral waterfall plots, assisting engineers in understanding the system status. All monitoring data records complete time series, supporting backtracking queries by time point or event type.

[0108] Example 5: The load-bearing deformation monitoring system of the collaborative verification module adopts a distributed fiber optic sensor network, with sixteen strain measurement points arranged on the key support structure of the maintenance platform. Each measurement point is equipped with a fiber Bragg grating sensor, with the center wavelength of the grating uniformly distributed in the range of 1525nm to 1545nm. The demodulator uses tunable laser scanning technology, achieving a wavelength resolution of 1pm, corresponding to a strain measurement accuracy of ±2με. The sensor arrangement scheme has been optimized through finite element analysis, covering the stress concentration areas of the platform's main beam, crossbeams, and connecting nodes. Temperature compensation is achieved through a reference grating set in the non-stressed area, eliminating measurement deviations caused by ambient temperature fluctuations. The deformation data acquisition cycle is set to 50ms. An early warning is triggered when the deformation exceeds 0.05mm for three consecutive sampling cycles, and an event exceeding the deformation threshold is determined when it exceeds 0.1mm.

[0109] The environmental vibration monitoring unit is equipped with four triaxial accelerometer arrays, arranged on the maintenance platform base and at the differential clamp mounting location. The sensors cover ±5g and have a frequency response range of 0.5Hz to 500Hz, meeting the monitoring requirements across the entire frequency band, from low-frequency swaying to high-frequency vibration. The signal conditioning circuit incorporates an anti-aliasing filter and a 24-bit analog-to-digital converter, with a sampling rate set to 2kHz. Spectrum analysis employs a real-time parallel processing architecture; 1 / 3 octave band analysis is performed on a dedicated digital signal processor, and the power spectral density of each frequency band is calculated using the Welch average periodogram method. The resonance detection algorithm compares the differential vibration characteristic frequencies with the coherence function of the environmental vibration spectrum, determining a resonance risk in frequency bands where the coherence coefficient exceeds 0.7 and the phase difference is stable.

[0110] The dynamic compensation vector regeneration process is initiated immediately upon detecting a deformation exceeding the limit event. First, it freezes the currently executing compensation commands and saves the state parameters of each axis of the robotic arm. The deformation field reconstruction algorithm converts discrete-point strain measurement data into a three-dimensional displacement field for the entire platform and calculates the deformation gradient directly related to the differential mounting position. The new compensation vector generation considers deformation development trend prediction, using time series analysis to estimate the deformation increment within the next 500ms. A safety factor is introduced into the compensation calculation, adding a 20% margin to the predicted value. The vector verification process simulates the execution effect using a digital twin model, and only sends the vector to the actuator after confirming there is no collision risk.

[0111] The trajectory replanning mechanism is activated after resonance frequency band detection and confirmation. The system analyzes the coherence characteristics of the vibration spectrum to identify the critical frequency bands that have the greatest impact on differential repair quality. The trajectory optimization objective function is adjusted to minimize vibration sensitivity, adding frequency domain vibration suppression requirements to the traditional path planning constraints. The timestamp distribution of the control point sequence is readjusted according to vibration characteristics to avoid integer multiples of the resonance frequency. After the new trajectory is generated, it enters trial operation mode, with the robotic arm executing the first part of the path at 50% of its rated speed while monitoring vibration response changes. The trajectory update is considered effective only if the vibration amplitude decreases by more than 30%; otherwise, it returns to replanning.

[0112] The strain sensor is installed using a special epoxy resin adhesive, which cures to form a uniformly thick bonding layer. Before installation, the metal surface is sandblasted to improve the adhesion strength of the adhesive layer. Fiber optic cabling is protected with stainless steel flexible conduit, ensuring sufficient slack at moving parts. The sensor network employs a dual-ring topology, with primary and backup channels operating independently, so a single point of failure does not affect overall functionality. Fiber optic cable connectors are waterproof and oil-proofed, suitable for the harsh environment of the maintenance workshop. Initial reference values ​​for each measurement point are collected under no-load conditions and stored in the calibration database for subsequent comparison.

[0113] The vibration monitoring unit's frequency band energy calculation employs a digital filter bank method, dividing the 0.5-500Hz frequency domain into 31 1 / 3 octave bandpass filter banks, each with strict amplitude and phase characteristics. The real-time energy integration window is set to 100ms, with a sliding step of 10ms, ensuring that rapidly changing vibration characteristics are not missed. Frequency band coherence analysis uses complex fast Fourier transform to calculate the cross-power spectral density of the differential vibration signal and the environmental vibration signal. The coherence threshold setting has hysteresis characteristics, with a trigger value of 0.7 and a reset value of 0.5, avoiding frequent state switching.

[0114] The time series analysis of the deformation prediction model employs the ARIMA method. Model parameters are automatically trained based on historical deformation data and include autoregressive, differencing, and moving average terms. The prediction step size is aligned with the control system's sampling period, predicting the deformation development for the next ten sampling points each time. Model confidence is assessed through residual analysis, and model retraining is triggered when the prediction error continues to increase. Deformation data is stored in association with platform load distribution information, establishing a library of typical deformation patterns under different maintenance procedures.

[0115] The trajectory optimization for resonance suppression employs a frequency-domain sensitivity-weighted method, assigning higher optimization weights to vibration-sensitive frequency bands based on frequency response function analysis. Path smoothness constraints are redefined in the frequency domain to limit the energy distribution of the trajectory spectrum within sensitive frequency bands. Timestamp adjustments avoid integer multiples of the resonance period, ensuring that the changes in the robotic arm's motion state are phase-shifted from the vibration peak. The optimization algorithm utilizes a hybrid strategy of genetic algorithm and gradient descent, striking a balance between global search and local fine-tuning.

[0116] The verification process for dynamic compensation includes three-dimensional spatial interference checks, loading the latest platform deformation data into the digital twin model, and simulating the entire process of the robotic arm performing compensation motion. The collision detection algorithm uses a hierarchical bounding box method to quickly screen potential interference areas. Dynamic gap analysis calculates the minimum distance change during motion, ensuring the safety margin is always greater than 10mm. Compensation schemes that fail verification are returned to the correction stage, where the compensation direction is adjusted or the compensation amplitude is reduced before re-verification.

[0117] The signal demodulation of the fiber optic sensor network employs a parallel scheme of wavelength scanning and intensity detection. The wavelength scanning range of the tunable laser covers the reflection bands of all grating sensors, and the scanning rate matches the deformation monitoring requirements. The intensity detection channel monitors the reflectivity changes of each sensor to identify abnormal loss events. The signal processing unit achieves real-time wavelength peak detection and uses cubic spline interpolation to improve positioning accuracy. Network fault diagnosis is achieved through optical temporal reflectance analysis (OTDR) technology to accurately locate breakpoints or abrupt loss changes.

[0118] Time-frequency analysis of vibration monitoring data employs wavelet transform, with continuous wavelet transform providing a joint time-frequency representation of the vibration signal and identifying instantaneous frequency variation characteristics. The Morlet wavelet is selected as the wavelet basis function, with its center frequency matched to the characteristic vibration frequency band of the differential. Energy accumulation zones in the time-frequency plane are detected using image processing techniques, locating significant vibration events through edge detection and region growing algorithms. The analysis results are cross-validated with spectral analysis methods to improve the reliability of resonance detection.

[0119] The spatial interpolation algorithm for generating the compensation vector employs the radial basis function method, fitting discrete-point deformation measurement data into a continuous spatial displacement field. The interpolation function is in the form of multiple quadratic surfaces. The deformation gradient is calculated using the central difference method, estimating the rate of displacement change at the nodes of the three-dimensional mesh. The compensation direction is determined by considering the normal vector of the differential mounting surface, ensuring that the compensation motion proceeds along the effective degrees of freedom. The vector magnitude is determined comprehensively based on the deformation gradient and historical compensation effects, employing fuzzy logic reasoning to handle uncertainties.

[0120] The environmental modeling for trajectory replanning includes dynamic obstacle prediction. Vibration-sensitive regions are treated as virtual obstacles, represented as repulsive potential fields in the configuration space. The potential field strength is proportional to the vibration sensitivity and affects the calculation of the cost function for trajectory optimization. The robotic arm's dynamic constraints are transformed into a velocity-acceleration feasible region, which is directly embedded into the optimization model during trajectory parameterization. The planning algorithm employs a sampling-based fast exploratory random tree variant, improving convergence speed while ensuring probabilistic completeness.

[0121] The collaborative verification module's anomaly event logs are recorded in a structured format, with each event including fields such as timestamp, event type, list of triggering sensors, severity level, and handling measures. Event correlation analysis is implemented using a graph database to establish causal relationship models between alarms from different sensors. Log data visualization displays the spatiotemporal evolution of deformation, vibration, and control commands, assisting engineers in understanding system behavior. Long-term operational data is used for reliability analysis to identify potential improvement points and maintenance needs.

[0122] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0123] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. An automated auxiliary system for the maintenance and handling of a loader differential, characterized in that, include: The maintenance parameter acquisition module is used to collect the current position deviation data, vibration spectrum characteristics and clamping force distribution parameters of the maintenance tooling fixture in real time, and generate a set of maintenance parameters. The pose compensation module is used to generate a dynamic compensation vector sequence based on the current pose deviation data in the maintenance parameter set, and update the compensation step size of the dynamic compensation vector sequence based on the vibration spectrum characteristics. The transport path planning module is used to generate a sequence of transport trajectory control points based on the maintenance process time window constraints and the clamping force distribution parameters in the maintenance parameter set. The execution control module is used to activate the dynamic compensation vector sequence and generate a pose correction command when the current pose deviation data is detected to exceed a first activation threshold; and to activate the transport trajectory control point sequence and generate a trajectory correction command when the clamping force distribution parameter is detected to exceed a second activation threshold. The execution control module is also used to input the pose correction command or trajectory correction command to the execution unit of the maintenance robot arm; The maintenance parameter acquisition module includes: A multi-dimensional sensor array is used to synchronously collect the axial offset, radial runout waveform, and pressure feedback values ​​of each contact point of the tooling fixture during the disassembly phase of the differential assembly. The material identification unit is used to identify the differential housing material type and match the corresponding reference clamping force safety threshold based on the characteristic frequency range in the radial runout waveform. The parameter fusion unit is used to convert the axial offset into a three-dimensional spatial offset vector and calculate the difference between the pressure feedback value of each contact point and the reference clamping force safety threshold to generate a clamping force deviation distribution map. The set generation unit is used to integrate the three-dimensional spatial offset vector, radial runout waveform and clamping force deviation distribution map into the maintenance parameter set; The pose compensation module includes: The dynamic learning rate adjustment unit is used to calculate the current compensation learning rate based on the rate of change of the main frequency amplitude of the vibration spectrum characteristics, wherein the current compensation learning rate is negatively correlated with the rate of change of the main frequency amplitude. The vector generation unit is used to generate an initial compensation vector sequence based on the current pose deviation data in the maintenance parameter set; The step size optimization unit is used to iteratively update the initial compensation vector sequence using the current compensation learning rate to generate the dynamic compensation vector sequence containing the compensation direction and compensation amount.

2. The automated auxiliary system for repairing and handling the differential of a loader according to claim 1, characterized in that: The maintenance parameter acquisition module also outputs the radial runout waveform to the pose compensation module; The dynamic learning rate adjustment unit is specifically used to extract the energy attenuation slope of a preset frequency band in the radial jitter waveform, and adjust the attenuation coefficient of the current compensation learning rate according to the energy attenuation slope.

3. The automated auxiliary system for repairing and handling the differential of a loader according to claim 1, characterized in that, The transport path planning module includes: The time window parsing unit is used to parse the end time of the disassembly process and the start time of the installation process in the maintenance process flow, and generate a handling time constraint window. The trajectory generation unit is used to identify the weak clamping force area and generate an initial handling path by avoiding the weak clamping force area based on the clamping force distribution parameters in the maintenance parameter set. The control point optimization unit is used to uniformly divide the initial transport path within the transport time constraint window to generate a sequence of transport trajectory control points containing timestamps.

4. The automated auxiliary system for repairing and handling the differential of a loader according to claim 3, characterized in that: The maintenance parameter acquisition module also outputs the mass distribution parameters of the differential assembly to the transport path planning module. The control point optimization unit is specifically used to calculate the inertial moment of each path segment based on the mass distribution parameters, and to adjust the timestamp interval of the transport trajectory control point sequence based on the inertial moment.

5. The automated auxiliary system for repairing and handling the differential of a loader according to claim 1, characterized in that, The execution control module includes: The priority determination unit is used to prioritize the execution of the pose correction command when the current pose deviation data and the clamping force distribution parameters both exceed the activation threshold. The instruction fusion unit is used to add the residual pose deviation to the trajectory correction instruction after the pose correction instruction is executed; The execution unit drive unit is used to drive the maintenance robotic arm according to the fused trajectory correction instructions.

6. The loader differential repair and handling automated auxiliary system according to claim 5, characterized in that, Also includes: The feedback calibration module is used to collect the actual motion trajectory data of the maintenance robot arm and compare the deviation with the expected trajectory of the trajectory correction command to generate a trajectory error mapping table. The feedback calibration module is also used to feed back the trajectory error mapping table to the transport path planning module; The transport path planning module corrects the generation rules of the transport trajectory control point sequence based on the trajectory error mapping table.

7. The automated auxiliary system for repairing and handling the differential of a loader according to claim 1, characterized in that, Also includes: The collaborative verification module is used to acquire load-bearing deformation data and environmental vibration spectrum of the maintenance platform in real time during the handling process.

8. The automated auxiliary system for repairing and handling the differential of a loader according to claim 7, characterized in that, The collaborative verification module includes execution: When the load-bearing deformation data exceeds the deformation threshold, the pose compensation module is triggered to regenerate the dynamic compensation vector sequence. When the environmental vibration spectrum resonates with the vibration spectrum characteristics in the maintenance parameter set, the transport path planning module is triggered to replan the transport trajectory control point sequence.

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