A method and system for position control of a spinning end splicing robot for a spinning machine.

Abstract

This invention relates to the field of mechanical intelligent control technology, and in particular to a method and system for posture control of a spinning breakage splicing robot on a ring spinning machine. The method includes the following steps: positioning based on the displacement sensor of the actuator, so that the front and rear shaping servo motors reach the front and rear target positions according to set values; constructing a data acquisition array, and acquiring the posture monitoring parameters of the front and rear shaping servo motors based on the data acquisition array; processing the posture monitoring parameters to obtain the posture monitoring optimization parameters of the front and rear shaping servo motors; acquiring the horizontal center distance of the yarn tube, so that the left and right shaping servo motors reach the left and right target positions; after reaching the front and rear target positions and the left and right target positions, obtaining the servo origin offset to control the robot's posture. This invention, through multi-dimensional collaborative optimization, ensures splice accuracy and stability, reduces manual intervention, and improves the robot's intelligence level.

Description

Technical Field

[0001] This invention relates to the field of mechanical intelligent control technology, and in particular to a method and system for adjusting the posture of a spinning end splicing robot on a spinning frame. Background Technology

[0002] In automated ring spinning production, the automatic splicing robot for yarn breakage on the spinning frame is a key piece of equipment for achieving unmanned spinning. Its shaping mechanism (position adjustment) directly determines the alignment accuracy of the end effector with the yarn tube and ring, thus affecting the splicing success rate and yarn quality. Inaccurate position adjustment can lead to splicing failure and secondary yarn breakage, or even mechanical collisions causing spindle damage, robot shutdown, and other chain reactions, potentially disrupting the entire production line. Traditional guide rails lack a position adjustment mechanism, making it impossible to adjust the splicing robot's position deviation during movement in real time. Furthermore, some adjustment mechanisms rely on manual calibration or inaccurate sensor data, resulting in insufficient positioning accuracy and incomplete mechanism adjustment. Summary of the Invention

[0003] To address the shortcomings of existing technologies, this invention provides a method and system for controlling the pose of a spinning breakage splicing robot on a spinning machine.

[0004] To achieve the above objectives, in a first aspect, the present invention provides a method for pose control of a spinning breakage splicing robot on a spinning frame. The method includes the following steps: positioning based on the displacement sensor of the actuator, so that the front and rear shaping servo motors reach the front and rear target positions according to set values; constructing a data acquisition array, and acquiring pose monitoring parameters of the front and rear shaping servo motors based on the data acquisition array; processing the pose monitoring parameters to obtain optimized pose monitoring parameters of the front and rear shaping servo motors; acquiring the horizontal center distance of the yarn tube, so that the left and right shaping servo motors reach the left and right target positions; and after reaching the front and rear target positions and the left and right target positions, obtaining the servo origin offset to control the robot's pose. This invention significantly improves the accuracy and stability of the joint through multi-dimensional collaborative optimization. First, dual displacement sensors independently control the front and rear servo motors, achieving millimeter-level precise positioning. Second, multiple sensors fuse and collect displacement, acceleration, angular velocity, and three-dimensional coordinates to comprehensively capture pose parameters. Third, regional adaptive filtering and real-time temperature compensation algorithms effectively suppress vibration interference and correct zero-bias errors, improving the stability of pose data. Finally, a vision-based origin offset calibration mechanism ensures the alignment accuracy of the end effector with the yarn tube and steel ring, improving the joint success rate, reducing manual intervention, and enhancing the robot's intelligence.

[0005] Optionally, the positioning based on the displacement sensors of the actuator, so that the front and rear shaping servo motors reach the front and rear target positions according to the set values, includes: the displacement sensors include a left displacement sensor and a right displacement sensor of the actuator; the front and rear shaping servo motors include a first front and rear shaping servo motor and a second front and rear shaping servo motor; acquiring a first set value for the first front and rear shaping servo motor and a second set value for the second front and rear shaping servo motor; obtaining first position information based on the left displacement sensor, and the first front and rear shaping servo motor reaching a first front and rear target position according to the first set value; obtaining second position information based on the right displacement sensor, and the second front and rear shaping servo motor reaching a second front and rear target position according to the second set value. This invention achieves high-precision dynamic adjustment of the robot's front and rear posture through independent closed-loop control of dual displacement sensors and dual servo motors; the left displacement sensor provides real-time feedback on the position of the first motor, and the right sensor synchronously monitors the status of the second motor; independent processing of dual sensor data eliminates mechanical coupling errors and improves the synchronization accuracy of front and rear shaping actions; differentiated set values ​​adapt to different yarn tube specifications, effectively reducing yarn breakage rate and defect rate.

[0006] Optionally, the construction of the data acquisition array, and the acquisition of the pose monitoring parameters of the front and rear shaping servo motors based on the data acquisition array, includes: constructing the data acquisition array by combining a bilinear encoder, a microelectromechanical inertial measurement unit (MEMS), and a binocular camera; acquiring the displacement of the front and rear shaping servo motors based on the bilinear encoder; monitoring the acceleration and angular velocity of the front and rear shaping servo motors based on the MEMS; capturing the three-dimensional coordinates of the yarn tube center through the binocular camera; and using the displacement, acceleration, angular velocity, and three-dimensional coordinates of the yarn tube center as the pose monitoring parameters. This invention significantly improves the robot's pose perception accuracy through multi-dimensional parameter collaborative monitoring; the bilinear encoder provides micron-level displacement feedback, the MEMS captures dynamic acceleration and angular velocity under high-speed motion in real time, and the binocular camera accurately reconstructs the three-dimensional position of the yarn tube; data fusion forms complementary redundancy; it eliminates dynamic measurement blind spots, increases the pose parameter update frequency, reduces measurement errors under vibration environments, and provides a reliable data foundation for subsequent high-precision pose control.

[0007] Optionally, the step of processing the pose monitoring parameters to obtain the pose monitoring optimization parameters for the front and rear shaping servo motors includes: performing anomaly detection on the pose monitoring parameters to obtain data detection results; constructing a kinematic model and a shielding layer algorithm based on the data detection results to obtain historical prediction data; filtering the pose monitoring parameters using a sliding window mean filter in the low-intensity vibration zone and using a Kalman filter algorithm to filter the pose monitoring parameters in the high-intensity vibration zone; establishing a real-time temperature drift compensation algorithm to dynamically correct the zero-bias error of the pose monitoring parameters based on the temperature data of the front and rear shaping servo motors; and combining the anomaly detection, the filtering, and the dynamic correction to obtain stable pose flow data, using the stable pose flow data as the pose monitoring optimization parameters. This invention significantly improves the quality of pose parameters through multi-dimensional optimization; the anomaly detection mechanism combines kinematic models and shielding layer algorithms to identify and eliminate abrupt noise in real time; the regional adaptive filtering strategy dynamically switches algorithms for different vibration environments, improving the smoothness of the low vibration zone and accelerating the response speed in the high vibration zone; the temperature drift compensation algorithm suppresses the zero bias error caused by temperature; the synergistic effect forms a closed-loop optimization, ultimately outputting stable pose flow data, providing a reliable basis for precise robot control.

[0008] Optionally, the kinematic model includes: obtaining a historical position sequence based on the pose monitoring parameters; constructing a general prediction model based on the historical position sequence and combining kinematic relationships; and using the general prediction model as the kinematic model. This invention significantly improves the dynamic response accuracy of the robot through a historical data-driven prediction mechanism; predicts motor motion trajectories in advance based on temporal analysis of pose sequences, compensating for mechanical transmission lag errors; and effectively filters abnormal fluctuations caused by measurement noise by combining physical kinematic constraints, reducing trajectory prediction errors.

[0009] Optionally, the step of constructing a kinematic model and a shielding layer algorithm based on the data detection results to obtain historical prediction data includes: performing Schmitt trigger shaping and filtering on the pose monitoring parameters; establishing a signal quality assessment model and obtaining the signal-to-noise ratio (SNR) of the pose monitoring parameters based on the signal quality assessment model; obtaining an SNR threshold, and when the SNR exceeds the SNR threshold, performing cross-validation on the pose monitoring parameters to obtain a validation result; obtaining a deviation threshold, and if the validation result exceeds the deviation threshold, using the kinematic model to make a prediction to obtain the historical prediction data. This invention improves the reliability of pose prediction through multi-level data quality control; Schmitt trigger shaping eliminates edge jitter noise; the SNR assessment model quantifies data credibility in real time; when signal quality deteriorates, a cross-validation mechanism is triggered, and outliers are eliminated using cross-validation of multi-sensor data; when the deviation exceeds the limit, historical prediction data provided by the kinematic model replaces the abnormal measurement values, ensuring pose prediction accuracy even in strong interference environments.

[0010] Optionally, the step of establishing a real-time temperature drift compensation algorithm to dynamically correct the zero-bias error of the pose monitoring parameters based on the temperature data of the front and rear shaping servo motors includes: establishing a mapping relationship between temperature and the zero-bias error to obtain a temperature-error mapping model; determining compensation coefficients based on the temperature-error mapping model; incorporating the compensation coefficients as state variables into the Kalman filter algorithm for fine-tuning; obtaining a static zero-bias value; obtaining an updated compensation coefficient by combining the predicted value of the temperature-error mapping model; obtaining a zero-bias compensation value based on the temperature data and the updated compensation coefficients; and dynamically correcting the zero-bias error based on the zero-bias compensation value. This invention improves the thermal stability of pose monitoring through multi-dimensional collaborative correction; the temperature-error mapping model enables accurate prediction of the zero-bias error; the dynamic update frequency of the compensation coefficients can track motor temperature changes in real time; the state fusion mechanism under the Kalman filter framework deeply couples temperature compensation with dynamic measurement data, reducing pose errors caused by temperature changes; and the static zero-bias calibration step further eliminates accumulated errors, improving the thermal adaptability of the equipment.

[0011] Optionally, obtaining the horizontal center distance of the yarn tube to enable the left and right shaping servo motors to reach the left and right target positions includes: obtaining the horizontal center distance of the yarn tube through visual imaging; obtaining the mechanical range, and using the mechanical range to enable the left and right shaping servo motors to reach the left and right target positions. This invention achieves precise control of left and right posture through dual visual and mechanical constraints; it can adapt to yarn tube installation deviations, reduce manual calibration workload, and improve the compatibility of the splicing robot with yarn tubes of different specifications.

[0012] Optionally, the step of obtaining the servo origin offset for robot pose control after reaching the front / back target position and the left / right target position includes: taking a visual photograph to obtain the servo origin offset after reaching the front / back target position and the left / right target position; calibrating the robot using the servo origin offset to achieve pose control. This invention achieves closed-loop pose optimization through end-effector visual calibration. After the robot reaches the target position, visual photography accurately captures the servo origin offset, thereby dynamically calibrating mechanical errors. Compared to traditional open-loop control, it eliminates accumulated errors, improves the alignment accuracy and joint success rate of the end effector with the yarn tube and ring, and reduces the impact of mechanical wear caused by long-term operation, thus improving production stability.

[0013] Secondly, this invention provides a pose control system for a spinning machine spinning breakage splicing robot. The system executes the pose control method for the spinning machine spinning breakage splicing robot provided by this invention. The system includes an input device, an output device, a processor, and a memory, which are interconnected. The memory stores a computer program, which includes program instructions, and the processor is configured to call the program instructions. This invention achieves pose control through hardware-coordinated execution; the input / output devices ensure smooth parameter interaction; the processor efficiently calls the program in the memory, enabling real-time processing of multi-sensor data and precise control of servo motors; it improves system response speed, reduces pose control errors, minimizes manual intervention, adapts to various yarn bobbin specifications, and significantly improves production efficiency and yarn splicing quality. Attached Figure Description

[0014] Figure 1 This is a flowchart illustrating the pose control method of a spinning machine yarn breakage splicing robot according to an embodiment of the present invention.

[0015] Figure 2 This is a framework diagram of a spinning machine yarn breakage splicing robot posture control system according to an embodiment of the present invention;

[0016] Figure 3 This is a flowchart illustrating the posture control system of the spinning breakage splicing robot for a spinning machine according to an embodiment of the present invention. Detailed Implementation

[0017] Specific embodiments of the present invention will now be described in detail. It should be noted that the embodiments described herein are for illustrative purposes only and are not intended to limit the invention. In the following description, numerous specific details are set forth in order to provide a thorough understanding of the invention. However, it will be apparent to those skilled in the art that these specific details are not necessary to practice the invention. In other instances, well-known circuits, software, or methods have not been specifically described to avoid obscuring the invention.

[0018] Throughout this specification, references to "an embodiment," "an embodiment," "an example," or "an example" mean that a particular feature, structure, or characteristic described in connection with that embodiment or example is included in at least one embodiment of the invention. Therefore, the phrases "in an embodiment," "in an embodiment," "an example," or "an example" appearing in various places throughout the specification do not necessarily refer to the same embodiment or example. Furthermore, specific features, structures, or characteristics can be combined in one or more embodiments or examples in any suitable combination and / or sub-combination. Moreover, those skilled in the art will understand that the illustrations provided herein are for illustrative purposes and are not necessarily drawn to scale.

[0019] Please see Figure 1 An embodiment of the present invention provides a method for adjusting the pose of a spinning breakage splicing robot on a spinning machine, the method comprising the following steps:

[0020] S1. Positioning is performed based on the displacement sensor of the actuator, so that the front and rear shaping servo motors reach the front and rear target positions according to the set values.

[0021] In this embodiment, the displacement sensor includes a left displacement sensor and a right displacement sensor of the actuator; the front and rear shaping servo motors include a first front and rear shaping servo motor (ZL axis motor) and a second front and rear shaping servo motor (ZR axis motor); a first set value of the first front and rear shaping servo motor and a second set value of the second front and rear shaping servo motor are obtained; based on the first position information obtained from the left displacement sensor, the first front and rear shaping servo motor reaches a first front and rear target position according to the first set value; based on the second position information obtained from the right displacement sensor, the second front and rear shaping servo motor reaches a second front and rear target position according to the second set value.

[0022] Specifically, based on the positioning of the left displacement sensor of the actuator, the ZL axis motor reaches the first forward and backward target position according to the first set value. Before starting the movement, it calculates whether the first forward and backward target position exceeds the first mechanical range. If it exceeds the first mechanical range, an error is triggered, and the positioning display shows a fault. If it does not exceed the range, the ZL axis motor continues to operate. During the motor operation, it continuously identifies and judges whether it touches the limit position of the first mechanical range. If it touches the limit position, an error is triggered, and the positioning display shows a fault. After the motor operation is completed, it is judged whether the ZL axis has reached the first forward and backward target position. If it has not reached the first forward and backward target position, the ZL axis motor continues to be driven until the first forward and backward target position is reached.

[0023] Specifically, based on the positioning of the displacement sensor on the right side of the actuator, the ZR axis motor reaches the second forward / backward target position according to the second set value, the same steps as the ZL axis motor. Before starting the movement, it is calculated whether the second forward / backward target position exceeds the second mechanical range. If it exceeds the second mechanical range, an error is triggered, and the positioning display shows a fault. If it does not exceed the range, the ZR axis motor executes. During the motor execution, it continuously identifies and judges whether it touches the limit position of the second mechanical range. If it touches the limit position, an error is reported, and the positioning display shows a fault. After the motor execution is completed, it is judged whether the ZR axis has reached the second forward / backward target position. If it has not reached the second forward / backward target position, the ZR axis motor continues to be driven until the second forward / backward target position is reached.

[0024] It should be noted that after the position sensor identifies the position, the host computer will provide the motion amount of the front and rear shaping servo motors and determine whether the motion amount has reached the limit position. The set value is preset, and the host computer will determine whether the difference between the distance measured by the position sensor and the set value is within the range. If it is within the range, the front and rear shaping servo motors will move to the target position and align with the host according to the set value.

[0025] In addition, the mechanical range is calculated based on the length of the slide rail, the length of the slider, and the position of the slider in the mechanical structure.

[0026] S2. Construct a data acquisition array and obtain the pose monitoring parameters of the front and rear shaping servo motors based on the data acquisition array.

[0027] In this embodiment, a data acquisition array is constructed by combining a bilinear encoder, a microelectromechanical inertial measurement unit (MEMS), and a binocular camera; the displacement of the front and rear shaping servo motors is acquired based on the bilinear encoder; the acceleration and angular velocity of the front and rear shaping servo motors are monitored based on the MEMS; the three-dimensional coordinates of the yarn tube center are captured by the binocular camera; and the displacement, acceleration, angular velocity, and three-dimensional coordinates of the yarn tube center are used as pose monitoring parameters.

[0028] Specifically, during the movement of the front and rear shaping servo motors, the dual linear encoders on the slide rail collect the displacement in real time (with a main and backup redundancy design), the microelectromechanical inertial measurement unit synchronously monitors the acceleration and angular velocity parameters, and the industrial vision system captures the three-dimensional coordinates of the yarn tube center through a binocular camera; the three sensors achieve strict timing synchronization through hardware-level trigger signals (10ms fixed period).

[0029] S3. Perform data processing on the pose monitoring parameters to obtain the pose monitoring optimization parameters of the front and rear shaping servo motors.

[0030] Specifically, S3 includes the following steps:

[0031] S31. Perform anomaly detection on the pose monitoring parameters to obtain data detection results, and construct a kinematic model and a shielding layer algorithm based on the data detection results to obtain historical prediction data.

[0032] In this embodiment, anomaly detection is performed on the pose monitoring parameters to obtain data detection results. When anomaly is detected in single sensor data (such as vibration causing encoder pulse jump), the system automatically switches to the historical data prediction mode based on the kinematic model to ensure uninterrupted continuous motion. This process is specifically designed with a shielding layer algorithm to address the dust interference and electromagnetic interference commonly found in spinning workshops, effectively avoiding signal distortion.

[0033] In this embodiment, a historical position sequence is obtained based on pose monitoring parameters; a general prediction model is constructed based on the historical position sequence in combination with kinematic relationships; and the general prediction model is used as the kinematic model.

[0034] Specifically, when an anomaly is detected in single-sensor data, a kinematic model is constructed and historical data prediction is achieved through the following technical steps:

[0035] First, the displacement, velocity, and attitude data of the three-axis encoder and the microelectromechanical inertial measurement unit are collected in real time during normal operation. The most recent 20 historical position sequences are stored with a period of 10ms, and the corresponding velocities and accelerations are calculated. The historical position sequences satisfy the following relationship:

[0036]

[0037] in, There are 20 sets of historical location sequences in total.

[0038] The speed satisfies the following relationship:

[0039]

[0040] in, For at any time The instantaneous velocity value, For at any time The actual position value, For at any time The actual position value.

[0041] Acceleration satisfies the following relationship:

[0042]

[0043] in, For at any time The instantaneous acceleration value, For at any time The instantaneous velocity value, For at any time The instantaneous velocity value.

[0044] Next, a general prediction model is established based on fundamental kinematic relationships. A third-order polynomial is used to fit the trajectory as the kinematic model, satisfying the following relationship:

[0045]

[0046] in, For the future The predicted location value, For the current moment, To predict the time, These are the polynomial coefficients.

[0047] It should be noted that the polynomial coefficients The determination is made by fitting historical data using the least squares method.

[0048] When sensor data is abnormal, the most recent 10 sets of valid data are immediately used to extrapolate the position within the next 100ms using a third-order polynomial, and the predicted value is updated every 10ms. Simultaneously, redundant sensor cross-validation is initiated: if the deviation between the primary and backup encoders exceeds 0.1mm or the speed deviation between the MEMS inertial measurement unit and the encoder exceeds 0.2mm / s, weighted fusion is performed. The weighted fusion satisfies the following relationship:

[0049]

[0050] in, The merged location estimate. This is the credibility weighting coefficient (default 0.7). The predicted position value is the value from the kinematic model. Position measurements provided for redundant sensors.

[0051] The prediction process calculates errors in real time. When the error exceeds 0.5mm, a speed reduction is triggered; when it exceeds 1mm, an emergency stop is initiated. The polynomial coefficients are iteratively updated every 500ms with the latest data to ensure the model adapts to changes in mechanical operating conditions. The error satisfies the following relationship:

[0052]

[0053] in, For the prediction error value, The merged location estimate. This represents the actual measured location value.

[0054] In this embodiment, the pose monitoring parameters are shaped and filtered using a Schmitt trigger; a signal quality assessment model is established, and the signal-to-noise ratio (SNR) of the pose monitoring parameters is obtained based on the signal quality assessment model; a SNR threshold is obtained, and when the SNR exceeds the SNR threshold, the pose monitoring parameters are cross-validated to obtain the validation result; a deviation threshold is obtained, and if the validation result exceeds the deviation threshold, historical prediction data is obtained by using a kinematic model for prediction.

[0055] Specifically, when designing the shielding layer algorithm, considering the high-dust environment of the spinning workshop, a 316L stainless steel mesh was wrapped around the encoder and the microelectromechanical inertial measurement unit sensor. A dustproof glass with a miniature air pump was also installed on the vision camera lens. The metal mesh has a cross-sectional area ≥2.5mm². 2The copper braided strap is connected to an independent grounding terminal. For signal transmission, the sensor signal line uses double-shielded twisted-pair cable (inner aluminum foil shielding for high-frequency interference, outer tinned copper mesh shielding for low-frequency magnetic fields), and the connectors use IP65-rated aviation plugs filled with silicone rubber sealant. At the software algorithm level, the encoder pulse signal is first shaped by a hardware Schmitt trigger, then glitches are eliminated using sliding window mean filtering and Kalman filtering; a signal quality assessment model is established to calculate the signal-to-noise ratio in real time. When the signal-to-noise ratio is <20dB, cross-validation of the primary and backup sensor data is triggered. If the deviation exceeds 0.1mm, the system automatically switches to the redundant sensor and enables kinematic model prediction. Furthermore, the system judges the dust accumulation on the shielding layer every 30 minutes based on the encoder zero-point drift. When the threshold is exceeded, a shutdown cleaning prompt is triggered and recorded in the black box log.

[0056] S32. In the low-intensity vibration zone, the pose monitoring parameters are filtered using a sliding window mean filter, and in the high-intensity vibration zone, the pose monitoring parameters are filtered using a Kalman filter algorithm.

[0057] In this embodiment, the system dynamically identifies vibration spectrum characteristics for the high-frequency vibration characteristics of the spinning machine: in the low-intensity vibration zone, a sliding window mean filter is used to smooth instantaneous noise; in the high-intensity vibration zone, a Kalman filter algorithm is activated.

[0058] Sliding window mean filtering satisfies the following relationship:

[0059]

[0060] in, This is the filtered output signal. For the position index in the sequence, For window size, For the summation variable, This indicates rounding down. For in position The input signal at the location.

[0061] It should be noted that, This indicates rounding down, for example At that time, the window range is , i.e., the current point And one point to its left and one point to its right. This formula represents... Centered on, take the front and back together The average value of the points is used as the output signal.

[0062] Kalman filtering includes the following steps:

[0063] The first step is to develop a system model (linear state-space model) that includes state equations and observation equations.

[0064] The state equations satisfy the following relationship:

[0065]

[0066]

[0067] in, For a moment The system state vector, Here is the state transition matrix. For a moment The system state vector, To control the input matrix, For a moment The control input vector, This is the process noise vector. Indicates a normal distribution. Let be the covariance matrix of the process noise.

[0068] The observation equations satisfy the following relationship:

[0069]

[0070]

[0071] in, For a moment The vector of measured values, For the measurement matrix, For a moment The system state vector, To measure the noise vector, Indicates a normal distribution. This is the covariance matrix for measuring noise.

[0072] The second step, algorithm initialization (k=0), includes initial state estimation and initial error covariance matrix; the initial state estimation includes the state estimate of the Kalman filter at the initial time (generally denoted as time 0). The initial guess determines the initial state of the system, which is a priori estimate of the initial state of the system. Initial error covariance matrix ( Used for quantifying initial state estimation The uncertainty is such that a "larger value" reflects the initial lack of understanding of the system state and the high degree of uncertainty. Subsequent iterations and updates will optimize the system through filtering. It is a symmetric positive definite matrix, and its elements reflect the variance and covariance of the estimation errors for different state components, satisfying... .

[0073] The third step, the prediction phase (time update), includes state prediction and error covariance prediction.

[0074] State prediction satisfies the following relationship:

[0075]

[0076] in, for Time based Prior state estimation at time 10:00 Here is the state transition matrix. for Posterior state estimation after time-mapping observations To control the input matrix, For a moment The control input vector.

[0077] Error covariance predictions satisfy the following relationship:

[0078]

[0079] in, for Time based The prior error covariance matrix at time t. Here is the state transition matrix. for The posterior error covariance matrix after time-mapping observations Indicates transpose. Let be the covariance matrix of the process noise.

[0080] The fourth step, the update phase (measurement update), includes Kalman gain calculation, state update, and error covariance update.

[0081] Kalman gain calculations satisfy the following relationship:

[0082]

[0083] in, for The Kalman gain matrix at each time step. for Time based The prior error covariance matrix at time t. For the measurement matrix, Indicates transpose. This is the covariance matrix for measuring noise.

[0084] State updates satisfy the following relationship:

[0085]

[0086] in, for Posterior state estimation after time-fusion measurements for Time based Prior state estimation at time 10:00 for The Kalman gain matrix at each time step. For a moment The vector of measured values, This is the measurement matrix.

[0087] The error covariance update satisfies the following relationship:

[0088]

[0089] in, for The posterior error covariance matrix after time-fusion measurements It is the identity matrix. for The Kalman gain matrix at each time step. For the measurement matrix, for Time based The prior error covariance matrix at time t.

[0090] S33. Establish a real-time temperature drift compensation algorithm to dynamically correct the zero-bias error of the pose monitoring parameters based on the temperature data of the front and rear shaping servo motors.

[0091] In this embodiment, a temperature-error mapping model is obtained by establishing a mapping relationship between temperature and zero bias error. The compensation coefficient is determined based on the temperature-error mapping model. The compensation coefficient is incorporated as a state variable into the Kalman filter algorithm for fine-tuning. The static zero bias value is obtained, and the updated compensation coefficient is obtained by combining the predicted value of the temperature-error mapping model. The zero bias compensation value is obtained based on the temperature data and the updated compensation coefficient. The zero bias error is dynamically corrected based on the zero bias compensation value.

[0092] An integrated real-time temperature drift compensation module dynamically corrects the zero bias error (compensation coefficient 0.008g / ℃) using temperature sensor data embedded in the microelectromechanical inertial measurement unit, thus solving the measurement distortion problem caused by motor heating.

[0093] Specifically, a PT1000 temperature sensor is integrated within the microelectromechanical inertial measurement unit (MEMS) to acquire temperature data in real time at a frequency of 100Hz. The temperature signal is converted into a voltage signal via a constant current source and a Wheatstone bridge, digitized by a 24-bit ADC, and then filtered out for high-frequency noise using a first-order low-pass filter. Based on zero-bias data collected every 5℃ within a temperature range from -20℃ to 80℃ from the MEMS, a mapping relationship between temperature and zero-bias error is established, compensation coefficients are determined, and model accuracy is optimized through quadratic polynomial fitting. Based on the current filtered temperature value, the zero-bias compensation values ​​for the accelerometer and gyroscope are dynamically calculated according to the compensation coefficients. The compensation value is subtracted from the original sensor output data before being input into the Kalman filter algorithm. When the robot is stationary, 100 sets of data are collected to calculate the mean zero-bias value. If the deviation from the model prediction exceeds a certain value, the compensation coefficients are automatically updated. When the temperature change rate exceeds 2℃ / min, the sampling frequency is increased to 200Hz for rapid response. Simultaneously, the temperature-zero-bias curve is recorded using a black box to verify whether the zero-bias fluctuation after compensation is within the acceptable range. On the hardware side, the microelectromechanical inertial measurement unit (MEMS) is connected to the aluminum alloy frame via thermally conductive copper sheets and silicone pads to ensure that the temperature sensor accurately reflects the actual temperature of the chip. The compensation algorithm is integrated into the real-time task of the MEMS to ensure that the calculation delay does not exceed 1ms and is compatible with the 10ms sensor synchronization cycle.

[0094] Furthermore, in the real-time temperature compensation system, the dynamic correction of the compensation coefficients is achieved based on the fusion of adaptive Kalman filtering and a temperature-error mapping model. The system first establishes a quadratic polynomial relationship between the accelerometer's zero bias error and temperature through a full-temperature-range calibration experiment. The initial compensation coefficients are set according to the calibration results. (linear terms) and (Quadratic term). During operation, the system incorporates the compensation coefficients as state variables into the Kalman filter algorithm, driving coefficient updates by comparing the residuals of the actual zero bias in a static state with the model's predicted values. When the robot is stationary for more than 500ms, the system automatically collects 100 sets of data to calculate the actual zero bias. If the deviation from the model's predicted value exceeds 0.02g, the coefficients are fine-tuned using the Kalman gain. A dual-threshold trigger mechanism is also implemented: when the temperature change rate exceeds 2℃ / min, the system temporarily increases the filtering frequency to 200Hz and adds correction weights; when five consecutive updates are in the same direction, a stability check is triggered, and a damping factor is introduced to prevent coefficient divergence. To ensure the physical rationality of the correction, the system imposes constraints on the coefficient update range. Finally, the compensation value is calculated based on the real-time temperature and the updated coefficients, subtracted from the original sensor data, and input into the control algorithm. By upgrading the fixed coefficients to adaptively adjustable dynamic parameters, the system can track performance drift caused by factors such as sensor aging and changes in heat dissipation conditions in real time.

[0095] S34. Combine the anomaly detection, the filtering, and the dynamic correction to obtain stable pose flow data, and use the stable pose flow data as the pose monitoring optimization parameters.

[0096] In this embodiment, after three levels of processing—mechanical vibration suppression, circuit noise elimination, and multi-sensor cross-validation—stable pose flow data is output as pose monitoring optimization parameters.

[0097] In one optional embodiment, a composite model of Coulomb friction and viscous resistance is established, and the compensation current is calculated based on real-time speed feedback. Finally, a three-order safety protection system is constructed: when the position deviation is too high, a servo emergency stop is triggered; when the motor current exceeds 150% of the rated value, the power bus is cut off; and when the vibration sensor detects excessive acceleration, a deceleration program is immediately initiated. All abnormal events are recorded in the black box memory, and the mechanical state change curve can be traced through timestamps.

[0098] Specifically, in constructing the composite model of Coulomb friction and viscous drag, the hardness and surface roughness of the contact surface between the slide rail and the slider are first measured using a material property analyzer to determine the initial Coulomb friction coefficient as 0.15-0.2. Simultaneously, armature current, encoder displacement, and speed data are synchronously collected at a frequency of 100Hz in the motor drive system, and the drag characteristics at different frequency bands are separated using Fourier transform. In the low-speed motion stage, the influence of inertial force is ignored, and the driving force is decomposed into Coulomb friction force and load torque, establishing a linear mapping relationship between the two. In the medium-to-high speed stage, the relationship curve between speed and drag is fitted using the least squares method to determine the viscous drag coefficient. Subsequently, the two drag models are superimposed to form a composite model: the total drag equals the Coulomb friction force (a constant value opposite to the direction of motion) plus the viscous drag (a drag proportional to speed). To adapt to changes in operating conditions, the system collects data every 50ms and updates the friction coefficient and viscous drag coefficient in real time using an extended Kalman filter. When a drag mutation exceeding 15% is detected, an adaptive adjustment mechanism is triggered to refit the model parameters. Meanwhile, a temperature compensation factor is introduced into the model to dynamically correct the friction coefficient based on temperature data fed back from the sensors (the friction coefficient decreases by approximately 3% for every 10°C increase in temperature). Finally, the composite model is embedded into the motor control algorithm, and nonlinear resistance is offset through feedforward compensation, so that the positioning error of the system is controlled within ±0.05mm across the entire speed range.

[0099] S4. Obtain the horizontal center distance of the yarn tube so that the left and right shaping servo motors reach the left and right target positions.

[0100] In this embodiment, the horizontal center distance of the yarn tube is obtained through visual imaging; the mechanical range is obtained, and the left and right shaping servo motors are made to reach the left and right target positions based on the mechanical range.

[0101] Specifically, the horizontal X-axis center distance of the yarn tube is calculated through visual imaging, and the left and right target positions are sent to the Programmable Logic Controller (PLC). The PLC calculates whether the left and right target positions exceed the mechanical range. If they do, an error is reported; otherwise, the left and right shaping servo motors (X-axis motors) execute. During the X-axis motor's execution, it continuously identifies and judges whether it has touched the limit position of the mechanical range. If it touches the limit position, an error is reported, and a fault is displayed. The X-axis motor continues to execute until the X-axis reaches the left and right target positions.

[0102] It should be noted that the horizontal X-direction center distance of the yarn tube is calculated by using a camera mounted in the middle to capture and identify the position of the yarn spindle.

[0103] S5. After reaching the front and rear target positions and the left and right target positions, obtain the servo origin offset to adjust the robot's pose.

[0104] In this embodiment, after reaching the front and rear target positions and the left and right target positions, visual images are taken to obtain the servo origin offset; the robot is calibrated using the servo origin offset to achieve pose control.

[0105] Specifically, after the ZL-axis motor, ZR-axis motor, and X-axis motor all reach the designated position, a visual image is taken, the position difference of the upper structure in the Y direction is calculated, and the target position in the Y direction is sent to the PLC for storage as the Y-direction servo origin offset.

[0106] It should be noted that the ZL-axis motor, ZR-axis motor, and X-axis motor initially align the machine with the host machine and initially align the center position with the spindle. The upper structure adjusts the height and distance based on the offset. Taking the spinning machine as a reference, the uneven ground causes the height of each station of the robot to be different, so calibration is based on the offset of the servo origin in the Y direction.

[0107] Please see Figure 2 In one optional embodiment, the present invention provides a pose control system for a spinning machine spinning breakage splicing robot. The system includes an input device, an output device, a processor, and a memory, all interconnected. The memory stores a computer program comprising program instructions. The processor is configured to invoke the program instructions to execute specific steps as described in the relevant embodiments of the spinning machine spinning breakage splicing robot pose control method provided by the present invention. The spinning machine spinning breakage splicing robot pose control system provided by the present invention has a complete and stable structure, improving the overall applicability and practical application capability of the present invention.

[0108] Please see Figure 3The diagram shows the execution flow chart of the pose control system of the spinning yarn breakage splicing robot on a spinning machine. First, the ZL axis motor is positioned to the target position by the displacement sensor on the left side of the actuator. Second, the ZR axis motor is positioned to the target position by the displacement sensor on the right side of the actuator. Then, the X axis motor is positioned to the target position based on visual imaging. Finally, the servo origin offset is obtained based on the visual imaging.

[0109] In summary, the present invention provides a method and system for controlling the pose of a spinning machine yarn breakage splicing robot. By controlling the movement of a control mechanism along a slide rail, the rotating shaft is passively rotated, achieving in-plane rotational alignment adjustment. This allows for complex motion control with simple operation, simplifying the process and reducing costs. Furthermore, various filtering processes are employed on the sensor data to improve positioning accuracy, laying a solid foundation for subsequent splicing work and solving the problem of insufficient splicing success rate caused by inaccurate yarn breakage location in existing methods. The present invention is easy to understand, computationally simple, requires minimal workload, and is suitable for engineering applications, providing a theoretical basis and technical support for the further development of intelligent mechanical control technology.

[0110] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features therein. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention, and they should all be covered within the scope of the claims and specification of the present invention.

Claims

1. A method for adjusting the pose of a spinning breakage splicing robot on a spinning frame, characterized in that, Includes the following steps: Positioning is achieved using the displacement sensor of the actuator, enabling the front and rear shaping servo motors to reach the target positions according to the set values; Construct a data acquisition array, and obtain the pose monitoring parameters of the front and rear shaping servo motors based on the data acquisition array; The pose monitoring parameters are processed to obtain the pose monitoring optimization parameters of the front and rear shaping servo motors; Obtain the horizontal center distance of the yarn tube so that the left and right shaping servo motors can reach the left and right target positions; After reaching the front and rear target positions and the left and right target positions, the servo origin offset is obtained to adjust the robot's pose.

2. The method for adjusting the pose of a spinning machine yarn breakage splicing robot according to claim 1, characterized in that, The positioning based on the displacement sensor of the actuator, so that the front and rear shaping servo motors reach the front and rear target positions according to the set values, includes: The displacement sensor includes a left displacement sensor and a right displacement sensor of the actuator; The front and rear shaping servo motors include a first front and rear shaping servo motor and a second front and rear shaping servo motor. Obtain the first set value of the first front and rear shaping servo motors and the second set value of the second front and rear shaping servo motors; Based on the first position information obtained from the left displacement sensor, the first front and rear shaping servo motors reach the first front and rear target positions according to the first set value; Based on the second position information obtained from the right displacement sensor, the second front and rear shaping servo motors reach the second front and rear target positions according to the second set value.

3. The method for adjusting the pose of a spinning machine yarn breakage splicing robot according to claim 1, characterized in that, The construction of the data acquisition array, and the acquisition of pose monitoring parameters of the front and rear shaping servo motors based on the data acquisition array, includes: The data acquisition array is constructed by combining a bilinear encoder, a microelectromechanical inertial measurement unit, and a binocular camera; The displacement of the front and rear shaping servo motors is acquired based on the bilinear encoder. The acceleration and angular velocity of the front and rear shaping servo motors are monitored based on the microelectromechanical inertial measurement unit. The binocular camera captures the three-dimensional coordinates of the yarn tube center. The displacement, acceleration, angular velocity, and three-dimensional coordinates of the yarn tube center are used as the pose monitoring parameters.

4. The method for adjusting the pose of a spinning machine yarn breakage splicing robot according to claim 1, characterized in that, The process of processing the pose monitoring parameters to obtain the pose monitoring optimization parameters for the front and rear shaping servo motors includes: Anomaly detection is performed on the pose monitoring parameters to obtain data detection results. Based on the data detection results, a kinematic model and a shielding layer algorithm are constructed to obtain historical prediction data. In the low-intensity vibration zone, the pose monitoring parameters are filtered using a sliding window mean filter, and in the high-intensity vibration zone, the pose monitoring parameters are filtered using a Kalman filter algorithm. A real-time temperature drift compensation algorithm is established to dynamically correct the zero-bias error of the pose monitoring parameters based on the temperature data of the front and rear shaping servo motors. Stable pose flow data is obtained by combining the anomaly detection, the filtering, and the dynamic correction, and the stable pose flow data is used as the pose monitoring optimization parameters.

5. The method for adjusting the pose of a spinning machine yarn breakage splicing robot according to claim 4, characterized in that, The kinematic model includes: The historical position sequence is obtained based on the pose monitoring parameters; A general prediction model is constructed based on the historical position sequence, incorporating kinematic relationships; The general prediction model is used as the kinematic model.

6. The method for adjusting the pose of a spinning machine yarn breakage splicing robot according to claim 4, characterized in that, The process of constructing a kinematic model and a shielding layer algorithm based on the data detection results to obtain historical prediction data includes: The pose monitoring parameters are shaped and filtered using a Schmitt trigger. A signal quality assessment model is established, and the signal-to-noise ratio of the pose monitoring parameters is obtained based on the signal quality assessment model. A signal-to-noise ratio (SNR) threshold is obtained. When the SNR exceeds the SNR threshold, the pose monitoring parameters are cross-validated to obtain a validation result. Obtain a deviation threshold. If the verification result exceeds the deviation threshold, use the kinematic model to make a prediction to obtain the historical prediction data.

7. The method for adjusting the pose of a spinning machine yarn breakage splicing robot according to claim 4, characterized in that, The established real-time temperature drift compensation algorithm dynamically corrects the zero-bias error of the pose monitoring parameters based on the temperature data of the front and rear shaping servo motors, including: A temperature-error mapping model is obtained by establishing a mapping relationship between temperature and the zero bias error, and the compensation coefficient is determined based on the temperature-error mapping model. The compensation coefficients are incorporated as state variables into the Kalman filter algorithm for fine-tuning. Obtain the static zero bias value and combine it with the predicted value of the temperature-error mapping model to obtain the updated compensation coefficient; The zero bias compensation value is obtained based on the temperature data and the updated compensation coefficient, and the zero bias error is dynamically corrected based on the zero bias compensation value.

8. The method for adjusting the pose of a spinning machine yarn breakage splicing robot according to claim 1, characterized in that, The step of obtaining the horizontal center distance of the yarn tube so that the left and right shaping servo motors reach the left and right target positions includes: The horizontal center distance of the yarn tube is obtained by visual imaging; Obtain the mechanical range, and based on the mechanical range, make the left and right shaping servo motors reach the left and right target positions.

9. The method for adjusting the pose of a spinning machine yarn breakage splicing robot according to claim 1, characterized in that, The step of obtaining the servo origin offset after reaching the front and rear target positions and the left and right target positions to perform pose control on the robot includes: After reaching the front and rear target positions and the left and right target positions, visual images are taken to obtain the servo origin offset. The robot is calibrated by the servo origin offset to achieve pose control.

10. A pose control system for a spinning machine spinning end breakage splicing robot, characterized in that, The system includes an input device, an output device, a processor, and a memory, which are interconnected. The memory stores a computer program, which includes program instructions. The processor is configured to invoke the program instructions to execute the pose control method for a spinning machine spinning breakage splicing robot as described in any one of claims 1-9.

Images