Yarn hanging method cooperatively controlled by double composite robots

Abstract

The invention relates to the technical field of robot cooperative control, and discloses a yarn hanging method for cooperative control of double composite robots, and the method comprises the steps: obtaining motion parameters of the double composite robots, and carrying out the preprocessing of the motion parameters to generate a cooperative path reference; identifying action features and determining a synchronization rule; performing collaborative path planning to form a collaborative action sequence and an execution node; and verifying the validity of the execution node, executing the corresponding operation according to the cooperative state (synchronous or asynchronous), positioning the abnormal node during asynchronous, and calculating and compensating the phase offset. According to the method, through the steps of motion parameter processing, motion feature recognition, synchronization rule making, execution node verification, phase compensation and the like, the precision, stability and efficiency of double-robot collaborative operation are improved, and the method is suitable for industrial scenes needing high-precision collaborative operation; and particularly, the accuracy of yarn hanging and the continuity of operation can be ensured in yarn hanging operation.

Description

Technical Field

[0001] The present invention relates to the technical field of robot cooperative control, and specifically to a yarn hanging method for cooperative control of dual composite robots. Background Technique

[0002] In modern industrial production, the application of robot technology is becoming increasingly widespread. Especially in scenarios such as textile and assembly that require high-precision cooperative operations, the dual-robot cooperative control technology has become the key to improving production efficiency and quality. In the face of complex tasks, the traditional single-robot operation mode often has problems such as low efficiency and insufficient flexibility, while dual-robot cooperative operation can significantly improve operation efficiency and accuracy by reasonably allocating tasks and cooperative actions. However, the current dual-robot cooperative control technology still faces many challenges in practical applications.

[0003] Accurate acquisition and preprocessing of motion parameters are required for dual-robot cooperative operation. In the actual production environment, motion parameters such as the pose data, joint angles, and end trajectories of robots are easily affected by factors such as environmental interference and sensor errors, resulting in outliers in the data, and the units and physical meanings of different parameters may be different. If the unprocessed data is directly used for cooperative control, it will seriously affect the accuracy and reliability of cooperative path planning. For example, in the yarn hanging operation, if there is a deviation in the pose data of the robot, it may lead to inaccurate yarn suspension positions, affecting product quality.

[0004] The recognition of action characteristics and the determination of synchronization rules are the core links of dual-robot cooperative control. Different cooperative path benchmarks have different action characteristics, such as continuous characteristics and discrete characteristics. How to accurately identify these characteristics and formulate corresponding synchronization rules is the key to realizing efficient cooperation of dual robots. Traditional methods for formulating synchronization rules often lack in-depth analysis of action characteristics, resulting in problems such as mismatched actions and poor synchronization during the cooperative operation of robots, affecting the smoothness and stability of the operation.

[0005] The effectiveness verification of cooperative path planning and execution nodes is an important guarantee to ensure the smooth progress of dual-robot cooperative operation. During the cooperative path planning process, multiple groups of cooperative action sequences and execution nodes need to be formed, and the effectiveness of these execution nodes is directly related to the success or failure of the entire operation. However, the existing methods for verifying the effectiveness of execution nodes are not perfect, and it is impossible to accurately judge the cooperative state of execution nodes in a timely manner. Once an asynchronous execution state occurs, it is impossible to quickly locate the abnormal execution node and perform effective phase compensation, resulting in operation interruption or quality decline.

[0006] In terms of the calculation of phase offset and phase compensation, the existing technologies have problems such as insufficient calculation accuracy and inflexible compensation strategies. When asynchronous execution occurs during the operation of dual robots, it is necessary to accurately calculate the phase offset and perform corresponding compensation to restore the preset phase difference. However, traditional calculation methods often ignore the importance of historical trajectory backtracking, resulting in inaccurate calculation of the phase offset and poor compensation effect. At the same time, the compensation strategy lacks the ability to dynamically adjust according to factors such as the number of backtracking times and cannot adapt to different degrees of abnormal situations. Summary of the Invention

[0007] The purpose of the present invention is to provide a yarn hanging method for cooperative control of dual composite robots to solve the problems proposed in the above background technology.

[0008] To achieve the above purpose, the present invention provides the following technical solution: A yarn hanging method for cooperative control of dual composite robots, the method includes: Obtain the motion parameters of the dual composite robots, and perform preprocessing to generate a cooperative path benchmark; Identify the action characteristics of each of the cooperative path benchmarks, and determine the synchronization rules of the dual robots based on the action characteristics; Perform cooperative path planning on the dual robots according to the synchronization rules to form multiple groups of cooperative action sequences and the execution nodes of each group of the cooperative action sequences; Verify the effectiveness of each of the execution nodes to obtain the cooperative state of each execution node, where the cooperative state includes a synchronous execution state and an asynchronous execution state; In the synchronous execution state, the cooperative action sequences of the dual robots are executed according to a preset phase difference; In the asynchronous execution state, locate the abnormal execution node and calculate the phase offset of the cooperative action sequence under the abnormal execution node; Based on the phase offset, perform phase compensation on the abnormal execution node until the cooperative action sequences of the dual robots restore the preset phase difference and then terminate.

[0009] Preferably, the obtaining the motion parameters of the dual composite robots and performing preprocessing to generate a cooperative path benchmark includes: Collect the pose data, joint angles and end trajectories of the dual robots through sensors, and store them as independent data sets respectively, and add a unique action identifier to each data set; Eliminate outliers in the data within each of the independent data sets and filter out data items that exceed physical constraints; Perform kinematic decomposition on the processed independent data sets, extract the translation component and the rotation component, and output the component parameters in the same unit as the cooperative path benchmark.

[0010] Preferably, the action features for identifying each of the collaborative path benchmarks include: Extract the trajectory key points under each collaborative path benchmark, and generate an action node sequence according to the execution time sequence; Calculate the spatial distance between adjacent action node sequences, and label it as a synchronization constraint parameter; Set an allowable deviation range, normalize the synchronization constraint parameter according to the allowable deviation range, and generate multiple synchronization intervals; Statistically analyze the distribution density of the synchronization constraint parameters within each synchronization interval, label it as a classification constraint parameter, and respectively determine the action features of the collaborative path benchmark based on the classification constraint parameter; Among them, the action features include continuous features and discrete features, and the synchronization priority of the continuous features is higher than that of the discrete features.

[0011] Preferably, the step of respectively determining the action features of the collaborative path benchmark based on the classification constraint parameter includes: Obtain the classification constraint parameters corresponding to each collaborative path benchmark; Sort the classification constraint parameters under the same collaborative path benchmark according to the density interval from dense to sparse, and calculate the coverage ratio of the maximum density interval; Set a determination threshold, and compare the coverage ratio with the determination threshold; If the coverage ratio is higher than the determination threshold, it is determined that the collaborative path benchmark has continuous features; If the coverage ratio is lower than or equal to the determination threshold, it is determined that the collaborative path benchmark has discrete features.

[0012] Preferably, the step of determining the synchronization rule of the dual robots based on the action features includes: Obtain the action features of each collaborative path benchmark; Take the minimum value of the synchronization interval corresponding to the continuous feature as the synchronization trigger threshold; Extract the synchronization trigger thresholds under all continuous features, and arrange them in ascending order to generate a synchronization trigger sequence; Match the actions of the dual robots according to the synchronization trigger sequence, and after the collaborative path execution under the continuous feature is completed, supplement the execution of the collaborative path under the discrete feature.

[0013] Preferably, the step of validating the effectiveness of each execution node includes: Collect the actual pose data of the dual robots under each execution node, perform coordinate system alignment conversion, and generate multiple verification parameters; Call the verification model, input the verification parameters into the model, and label the matching degree output by the model as the verification result; Set the verification threshold and compare the verification result with the verification threshold; If the verification result is higher than the verification threshold, it is determined that the execution node is in the synchronous execution state; If the verification result is lower than or equal to the verification threshold, it is determined that the execution node is in the asynchronous execution state.

[0014] Preferably, calculating the phase offset of the collaborative action sequence under the abnormal execution node includes: Obtain the target trajectory points where the two robots do not execute synchronously under the abnormal execution node; Calculate the spatial offset between the target trajectory point and the current trajectory point, and mark it as the real-time phase offset; Perform historical trajectory backtracking on the abnormal execution node, and extract the historical phase offset under its previous node; Set the maximum allowable offset, and stop backtracking when the historical phase offset is less than or equal to the maximum allowable offset; Call the compensation algorithm, input the real-time phase offset and the historical phase offset into the algorithm, and mark the algorithm output result as the total phase offset.

[0015] Preferably, performing phase compensation on the abnormal execution node based on the phase offset includes: Obtain the number of backtracking times of the abnormal execution node, and mark it as the compensation priority parameter; Set the compensation threshold, and compare the compensation priority parameter with the compensation threshold; When the compensation priority parameter exceeds the compensation threshold, adjust the trajectory parameters of the abnormal execution node according to the total phase offset to generate an updated execution node; When the compensation priority parameter does not exceed the compensation threshold, continuously monitor the phase offset until the priority parameter reaches the compensation threshold and then trigger compensation.

[0016] Preferably, the steps for generating the synchronization trigger sequence further include: Extract the end-effector velocity curves of the robots corresponding to each synchronization trigger threshold; Perform secondary sorting on the synchronization trigger thresholds according to the smoothness of the velocity curves, and preferably select the synchronization trigger thresholds with a velocity change rate lower than the set value.

[0017] Preferably, the steps for coordinate system alignment transformation include: Map the pose data of the end-effectors of the two robots to the same global coordinate system; Eliminate the relative pose deviation of the base coordinate systems of the two robots through homogeneous matrix transformation and unify them to the same reference benchmark.

[0018] Compared with the prior art, the beneficial effects of the present invention are as follows: In terms of motion parameter processing, motion parameters such as the pose data, joint angles, and end trajectories of the dual robots are collected by sensors and stored as independent data sets respectively, providing a rich and accurate data basis for subsequent processing and analysis. Outliers in the data within each independent data set are removed, and data items beyond physical constraints are filtered, effectively ensuring the reliability and validity of the data and avoiding collaborative control errors caused by data anomalies. The processed independent data sets are kinematically decomposed, the translational component and the rotational component are extracted, and after being unified into the same unit, they are output as the collaborative path benchmark, enabling different types of motion parameters to be analyzed and planned under the same benchmark, and improving the accuracy and consistency of collaborative path planning.

[0019] In terms of action feature recognition and synchronization rule determination, the trajectory key points under each collaborative path benchmark are extracted to generate an action node sequence. By calculating the spatial distance between adjacent action node sequences and performing normalization processing, multiple synchronization intervals are generated, and then the distribution density of the synchronization constraint parameters within the synchronization intervals is statistically analyzed to determine the action features. This method can accurately identify the continuous and discrete features of the collaborative path benchmark, providing a basis for formulating reasonable synchronization rules. Based on the action features, the synchronization rules for the dual robots are determined. The minimum value of the synchronization interval corresponding to the continuous feature is used as the synchronization trigger threshold, and a synchronization trigger sequence is generated in ascending order. At the same time, after the collaborative path under the continuous feature is executed, the collaborative path under the discrete feature is supplemented and executed. This synchronization rule fully considers the priorities of different action features, ensuring that the dual robots can execute actions in a reasonable order and rhythm during the collaborative operation, and improving the fluency and stability of the collaborative operation.

[0020] In terms of collaborative path planning and execution node verification, the dual robots are collaboratively path-planned according to the synchronization rules to form multiple groups of collaborative action sequences and execution nodes, providing a clear execution process for the collaborative operation of the dual robots. The effectiveness of each execution node is verified. By collecting the actual pose data and performing coordinate system alignment transformation, verification parameters are generated, and a verification model is called to judge the synchronization state of the execution node. This verification method can timely and accurately detect abnormal situations of the execution nodes. When the execution nodes are in an asynchronous execution state, the abnormal execution nodes can be quickly located and the phase offset can be calculated, providing strong support for subsequent phase compensation.

[0021] In terms of phase offset calculation and phase compensation, by obtaining the target trajectory points where the two robots do not execute synchronously under abnormal execution nodes, calculating the spatial offset and performing historical trajectory backtracking, extracting the historical phase offset, and calling the compensation algorithm to generate the total phase offset, the accuracy and comprehensiveness of phase offset calculation are ensured. Based on the phase offset, phase compensation is performed on the abnormal execution nodes. The compensation priority parameter is set according to the number of backtracking times. When the compensation priority parameter exceeds the compensation threshold, the trajectory parameters are adjusted to generate the updated execution nodes. When it does not exceed, continuous monitoring is carried out until compensation is triggered. This compensation strategy has flexibility and dynamic adaptability, and can adjust the compensation measures in a timely manner according to the severity of abnormal situations, ensuring that the cooperative action sequence of the two robots can quickly restore the preset phase difference, and guaranteeing the continuity and stability of the operation.

[0022] In addition, during the generation process of the synchronous trigger sequence, the end-effector velocity curves of the robots corresponding to each synchronous trigger threshold are extracted, and the synchronous trigger thresholds are sorted secondarily according to the smoothness of the velocity curves. The synchronous trigger thresholds with a velocity change rate lower than the set value are preferentially selected. This method can further optimize the motion trajectories of the two robots, reduce the impact of velocity mutations on cooperative operations, and improve the smoothness and accuracy of operations. During the coordinate system alignment transformation process, the pose data of the end-effectors of the two robots are mapped to the same global coordinate system, and the relative pose deviation of the base coordinate system is eliminated through homogeneous matrix transformation to be unified into the same reference benchmark, ensuring that the pose data of the two robots in different coordinate systems can be accurately matched, providing a reliable coordinate basis for cooperative control. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Figure 1 is the working principle diagram of the yarn hanging method for cooperative control of the double composite robots described in the present invention; Figure 2 is the design diagram for generating the cooperative path benchmark of the double composite robots; Figure 3 is the design diagram for identifying the action characteristics of the cooperative path benchmark; Figure 4 is the design diagram for determining the synchronization rules of the two robots. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0024] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0025] Please refer to Figures 1-4, A yarn hanging method for cooperative control of a dual composite robot according to the present invention obtains the motion parameters of the dual composite robot and preprocesses them to generate a cooperative path benchmark, identifies action features to determine synchronization rules, performs cooperative path planning, and verifies the effectiveness of execution nodes, so as to achieve precise cooperative control of the two robots in synchronous or asynchronous states. The following is a detailed description in combination with specific steps and embodiments: The pose data, joint angles, and end trajectories of the two robots are collected in real time through sensors. The three types of data are stored as independent data sets respectively, and a unique action identifier is assigned to each data set for subsequent traceability. An outlier removal operation is performed on each data set, and data items that exceed the physical constraints of the robot (such as joint movement range, end velocity limit) are filtered through statistical methods (such as the Z-score algorithm). Subsequently, the processed data sets are kinematically decomposed to separate the translational components (such as the XYZ coordinates in the Cartesian coordinate system) and rotational components (such as Euler angles or quaternions), and all component parameters are unified into the same unit (such as millimeters, radians), and finally output as a cooperative path benchmark.

[0026] Extract the trajectory key points (such as trajectory turning points, speed mutation points) under each cooperative path benchmark, and generate an action node sequence according to the execution timing. Calculate the Euclidean distance between adjacent action node sequences as the synchronization constraint parameter, set the allowable deviation range (such as ±5 mm) to normalize the parameter, and generate multiple synchronization intervals (such as 0-5 mm, 5-10 mm, etc.). Statistically analyze the distribution density of the parameters in each interval, and mark it as the classification constraint parameter. Based on this parameter, determine the action feature: if the coverage ratio of the maximum density interval under a certain benchmark is higher than the determination threshold (such as 70%), it is determined as a continuous feature, otherwise it is a discrete feature. The synchronization priority of the continuous feature is higher than that of the discrete feature. Based on this, determine the synchronization rule: use the minimum value of the synchronization interval corresponding to the continuous feature as the synchronization trigger threshold, generate a synchronization trigger sequence from small to large according to the threshold, give priority to executing the continuous feature path, and supplement the execution of the discrete feature path after completion.

[0027] Perform cooperative path planning for the two robots according to the synchronization rules, combine the continuous and discrete feature paths into multiple groups of cooperative action sequences, and set execution nodes for each group of sequences. The execution node includes the pose, joint angle, and end trajectory parameters of the two robots at a specific moment, which serve as the benchmark for subsequent verification.

[0028] Collect the actual pose data of the dual robots under each execution node, map the data to the same global coordinate system through homogeneous matrix transformation, and eliminate the base coordinate system deviation. Input the processed verification parameters into the verification model (such as the matching degree calculation model based on neural network), and output the matching degree as the verification result. Set the verification threshold (such as 85%). If the matching degree is higher than the threshold, it is determined to be in the synchronous execution state, and the dual robots execute according to the preset phase difference; if it is lower than or equal to the threshold, it is determined to be in the asynchronous execution state, and the abnormal handling process is triggered.

[0029] In the asynchronous state, locate the abnormal execution node, obtain the target trajectory points where the dual robots do not execute synchronously, and calculate the spatial offset between the target point and the current point as the real-time phase offset. Trace back to the previous node and extract the historical phase offset until the historical offset exceeds the maximum allowable offset (such as 10 mm) and then stop tracing back. Call the compensation algorithm (such as PID control algorithm), and calculate the total phase offset by combining the real-time and historical offsets. According to the comparison result of the number of tracebacks (compensation priority parameter) and the compensation threshold (such as 3 times), when the priority parameter exceeds the threshold, adjust the trajectory parameters of the abnormal node (such as extending the execution time, adjusting the motion speed) to generate an updated execution node; if it does not exceed, continue to monitor until the threshold is reached and then trigger compensation until the dual robots restore the preset phase difference.

[0030] The present invention will be further described below in conjunction with Embodiments 1 to 6: Embodiment 1: In the method for hanging yarn with coordinated control of dual composite robots, obtaining the motion parameters of the dual robots and preprocessing them to generate a coordinated path benchmark is the basic link. This embodiment details the specific implementation methods of this link, including steps such as data collection, storage, outlier processing, and kinematic decomposition.

[0031] First, data collection is completed through the sensor system. The sensor adopts a combined scheme of high-precision inertial measurement units (IMUs) and visual cameras. Among them, the IMUs are installed near each joint of the robot to collect data such as joint angular velocity and acceleration in real time, and their sampling frequency is set to 100 Hz to ensure that the subtle changes in joint motion can be captured. The visual camera adopts a binocular vision module, which is arranged above the end effector of the robot, and calculates the three-dimensional coordinates of the end trajectory through the binocular parallax principle. The resolution of the camera is 1920×1080 pixels, and the frame rate is 60 frames per second, which can meet the real-time requirements. For each of the dual robots, a set of sensor systems is independently configured to achieve synchronous collection of their respective motion parameters.

[0032] The collected data needs to be classified and stored. Specifically, the pose data, joint angle data, and end-effector trajectory data of the dual robots are stored as independent data sets respectively. The pose data includes the position (x, y, z) and orientation (roll angle, pitch angle, yaw angle) of the robot base coordinate system in the global coordinate system; the joint angle data is the real-time angle or displacement value of each rotary joint and prismatic joint; the end-effector trajectory data is the sequence of continuous coordinate points of the end-effector during the movement. Each data set is stored in CSV format, and the file naming rule is "robot identifier_data type_collection time_sequence number", for example, "RobotA_Pose_202505281430_001.csv", "RobotB_JointAngles_202505281435_002.csv". Among them, the robot identifier is used to distinguish the dual robots (such as RobotA and RobotB), the data type specifies the stored content (pose, joint angle, or end-effector trajectory), the collection time is accurate to the minute, and the sequence number is used to distinguish different data sets of the same type, ensuring that each data set has a unique action identifier for subsequent data traceability and matching.

[0033] Next, the outlier rejection operation is carried out. For the joint angle data, first set the physical constraint range of each joint according to the mechanical design parameters of the robot. For example, the mechanical limit of a certain rotary joint is -180° to 180°, if the collected angle value exceeds this range, it is determined as an outlier and directly rejected. For the pose data and end-effector trajectory data, the Z-score algorithm in statistical methods is used for processing. Taking the X coordinate of the end-effector trajectory as an example, first calculate the mean μ and standard deviation σ of this data set. If the X value of a certain data point satisfies |X - μ| > 3σ, it is determined as an outlier and rejected. When rejecting outliers, the timestamp information in the original data needs to be retained to ensure the continuity of the subsequent processed data in the time series.

[0034] After completing the outlier rejection, kinematic decomposition is performed on the processed independent data sets. The purpose of kinematic decomposition is to decompose complex motion parameters into translational components and rotational components for subsequent cooperative path planning. For the pose data, directly extract its position coordinates as the translational component (unit: millimeter), and the attitude angles are converted to radians as the rotational component. For the joint angle data, calculate the position and orientation of the end-effector through the forward kinematic model of the robot, so as to obtain the translational component and rotational component. Specifically, assume that the robot has n joints, and the joint angle vector is θ = [θ1, θ2,..., θ n ᵀ, through the forward kinematic formula: Among them, \(R\) is the rotation matrix and \(p\) is the translation vector. The pose of the end effector in the global coordinate system is calculated through this formula, and then the translation component \(p(x, y, z)\) and the Euler angles or quaternions (converted to radian units) corresponding to the rotation matrix \(R\) are separated. For the end trajectory data, the positions of each coordinate point are directly extracted as the translation component. If the trajectory data contains end pose information, the rotation component is also separated; if only position information is included, the rotation component is default set to zero or deduced according to the kinematic model.

[0035] After the extraction of the translation component and the rotation component is completed, all component parameters need to be unified to the same unit. The translation component is uniformly measured in millimeters (mm), and the rotation component is uniformly measured in radians (rad). For the rotation parameters expressed in degrees (°) in the original data, the conversion is carried out through the formula: radian = degree × π / 180. For example, the pitch angle of a certain joint is 30°, and after conversion, it is π / 6 rad. After the unit unification process, each data set will be output as structured data containing a timestamp, a translation component (x, y, z), and a rotation component (α, β, γ), where the timestamp is accurate to milliseconds to ensure the time synchronization of the motion parameters of the two robots.

[0036] The generated collaborative path reference data needs to meet the following requirements: First, the time series of the data is continuous without interruption or jump; second, the accuracy of the translation component and the rotation component meets the requirements of robot collaborative control. For example, the error of the translation component does not exceed ±0.1 mm, and the error of the rotation component does not exceed ±0.001 rad; third, accurate matching is achieved between each data set through a unique action identifier to ensure that the motion parameters of the two robots correspond one by one in terms of time and action.

[0037] In the data processing process, the following details need to be noted: The installation position and calibration accuracy of the sensor directly affect the accuracy of the data. Therefore, strict calibration is required after installing the sensor. For example, the internal and external parameters of the vision camera are calibrated through a calibration board, and the zero bias calibration and scale factor calibration of the IMU are carried out through standard parts; the parameter settings of the outlier rejection algorithm need to be adjusted according to the actual physical performance and motion characteristics of the robot to avoid mis-rejecting normal data or retaining abnormal data; the kinematic decomposition model needs to accurately reflect the mechanical structure of the robot. For different types of robots (such as serial robots, parallel robots), corresponding kinematic formulas need to be used for calculation.

[0038] Example 2: In the yarn hanging method of dual composite robot collaborative control, identifying the action characteristics of the collaborative path benchmark is a key step in determining the synchronization rules for the dual robots. This embodiment details the specific implementation of action characteristic identification, including processes such as trajectory key point extraction, synchronization constraint parameter calculation, synchronization interval generation, classification constraint parameter statistics, and action characteristic determination.

[0039] The extraction of trajectory key points uses the curvature detection algorithm. Curvature is an important parameter describing the degree of trajectory bending. The greater the curvature, the higher the degree of trajectory bending, usually corresponding to the turning points of action nodes. For the end trajectory curve of each collaborative path benchmark, it is represented in the form of discrete points as , where each point is a three-dimensional space coordinate. When calculating the curvature of each point, the difference method of adjacent three points is used for approximate solution: where, "×" represents vector cross product, the numerator is the modulus of the cross product result, and the denominator is the cube of the distance between adjacent two points. When the curvature of a certain point exceeds the set threshold (such as 0.1 / mm), that point is determined as a trajectory key point. For example, if the calculated curvature value of a certain point in a certain section of the trajectory is 0.15 / mm, exceeding the threshold of 0.1 / mm, then that point is marked as a key point and becomes part of the action node sequence.

[0040] After extracting the key points, an action node sequence is generated according to the execution time sequence , where each node contains the timestamp, translation component, and rotation component of this key point. For continuous trajectories, the action node sequence shows dense and continuous characteristics; for discrete trajectories, the node sequence is relatively sparse and has irregular intervals.

[0041] Calculate the spatial distance between adjacent action node sequences as the synchronization constraint parameter. For the action node sequences and of the dual robots, assuming that the currently processed is the -th node pair , its spatial distance is calculated using the three-dimensional Euclidean distance formula: This distance value reflects the synchronization error of the end positions of the dual robots at the same action node. For example, if the coordinate of is (100, 200, 300)mm, and the coordinate of is (102, 201, 303)mm, then the spatial distance is

[0042] After calculating the spatial distances of all adjacent node pairs, it is necessary to normalize the synchronization constraint parameters. First, set the allowable deviation range, for example, ±5mm, that is, the maximum actual allowable synchronization error is 5mm. During the normalization process, each distance value is mapped to the interval [0, 1], and the mapping formula is: where is the minimum value among all distance values, is the maximum value. If mm, mm, then in the above example . After normalization, the parameters are divided into multiple synchronization intervals according to the numerical range. For example, they are divided into three intervals: [0, 0.3), [0.3, 0.6), and [0.6, 1.0], corresponding to different synchronization constraint strengths. The smaller the interval value, the higher the synchronization requirement.

[0043] Statistical the distribution density of the synchronization constraint parameters within each synchronization interval, which is marked as the classification constraint parameter. The calculation of the distribution density uses the Kernel Density Estimation (KDE) method. By setting the kernel function (such as the Gaussian kernel) and bandwidth, calculate the probability density value of the parameters within each interval. For example, for a set of synchronization constraint parameters of a certain collaborative path benchmark, after kernel density estimation, the density value of the interval [0, 0.3) is 0.8, the density value of the interval [0.3, 0.6) is 0.2, and the density value of the interval [0.6, 1.0] is 0.05. Then the classification constraint parameter of this benchmark is the density value array [0.8, 0.2, 0.05] of each interval.

[0044] Based on the classification constraint parameter, determine the action characteristics of the collaborative path benchmark. The specific steps are as follows: First, obtain the classification constraint parameter corresponding to this benchmark, and sort the intervals according to the density value from dense to sparse. For example, in the above example, the sorting is [0, 0.3), [0.3, 0.6), [0.6, 1.0]. Then calculate the coverage ratio of the maximum density interval, that is, the ratio of the maximum density value to the sum of all interval density values. In the example, the sum of the density values is 0.8 + 0.2 + 0.05 = 1.05, and the coverage ratio of the maximum density interval [0, 0.3) is 0.8 / 1.05 ≈ 76.19%. Set the determination threshold to 70%. If the coverage ratio is higher than the threshold, it is determined that this collaborative path benchmark has continuous characteristics, indicating that the actions of the two robots need to be closely synchronized under this path; if the coverage ratio is lower than or equal to the threshold, it is determined as discrete characteristics, allowing a certain degree of asynchrony in action execution.

[0045] In practical applications, the following details need to be noted: The curvature threshold for extracting key trajectory points needs to be adjusted according to the precision requirements of the yarn hanging process. For example, in a high-precision process, the threshold can be set to 0.05 / mm, and in a low-precision process, it can be relaxed to 0.2 / mm. The allowable deviation range of the synchronous constraint parameters needs to be determined in combination with the motion precision of the robot. For example, for a robot with a repeat positioning accuracy of ±0.1mm, the allowable deviation can be set to ±0.5mm to reserve a safety margin. The selection of the bandwidth for kernel density estimation will affect the density calculation result, and usually the optimal bandwidth is determined by the cross-validation method to avoid overfitting or underfitting.

[0046] The determination result of the action characteristics directly affects the formulation of the synchronization rules. The synchronization priority of continuous characteristics is higher than that of discrete characteristics. Therefore, during collaborative path planning, the action node sequence corresponding to continuous characteristics needs to be processed first to ensure that the two robots execute strictly according to the preset phase difference in the links with high synchronization requirements. For example, during the yarn hanging process, the starting clamping and ending releasing actions of the yarn usually have continuous characteristics and need to be precisely synchronized; while the intermediate transportation actions may have discrete characteristics and allow a certain degree of asynchronous adjustment.

[0047] Example 3: In the yarn hanging method for collaborative control of dual composite robots, determining the synchronization rules of the two robots based on action characteristics is the core link to achieve precise collaboration. This example details the determination process of the synchronization rules, including steps such as action characteristic acquisition, synchronization trigger threshold setting, synchronization trigger sequence generation, and action matching strategy, and focuses on the description in combination with the priority differences between continuous characteristics and discrete characteristics.

[0048] Obtain the action characteristics of each collaborative path benchmark. The action characteristics are determined through the curvature detection, spatial distance calculation, and classification constraint parameter statistics processes described in Example 2, and each collaborative path benchmark is marked as a continuous characteristic or a discrete characteristic. Continuous characteristics indicate that the actions of the two robots need to be closely synchronized under this path, such as key actions like yarn clamping and trajectory tracking; discrete characteristics indicate that the actions allow a certain degree of asynchronous execution, such as adjustments or waiting actions on non-critical paths.

[0049] For the collaborative path benchmark corresponding to continuous characteristics, extract the minimum value of its synchronization interval as the synchronization trigger threshold. The synchronization interval is the parameter interval after normalization in Example 2. For example, if the synchronization constraint parameters of a certain continuous characteristic are distributed in the interval [0, 0.4) after normalization, then the minimum value of this interval is 0 (corresponding to the minimum value of the actual spatial distance). It should be noted that the synchronization trigger threshold needs to be mapped back to the actual physical unit. For example, the normalized interval [0, 0.4) corresponds to the actual spatial distance of 0 - 4mm, then the threshold is 0mm (indicating that the end positions of the two robots need to be completely synchronized).

[0050] After extracting the synchronization trigger thresholds for all continuous features, the thresholds need to be sorted to generate a synchronization trigger sequence. In the first stage of sorting, they are arranged in ascending order of the thresholds. For example, if the thresholds for three continuous features are 1.5 mm, 2 mm, and 3 mm respectively, the initial sequence [1.5 mm, 2 mm, 3 mm] is obtained after sorting. In the second stage, a secondary sorting needs to be performed in combination with the smoothness of the end-effector velocity curve of the robot to avoid mechanical shock or synchronization error caused by sudden velocity changes. Specifically, the end-effector velocity curves corresponding to each synchronization trigger threshold are extracted. The velocity curve is calculated through the translational component after kinematic decomposition, and the formula is: where, represents the end-effector velocity (unit: mm / s), represents the change in translational component within an adjacent time interval (unit: mm), represents the time interval (unit: s). The smoothness of the velocity curve is measured by the velocity change rate (unit: mm / s²). The smaller the change rate, the smoother the velocity. Set the threshold of the velocity change rate as (such as 50 mm / s²), and preferentially select the synchronization trigger thresholds with a velocity change rate lower than this threshold. For example, if the velocity change rate corresponding to the threshold of 1.5 mm is 40 mm / s² and the change rate corresponding to the threshold of 2 mm is 60 mm / s², then the former is preferentially ranked at the front of the sequence due to meeting the smoothness requirement. The adjusted synchronization trigger sequence is [1.5 mm, 2 mm, 3 mm] (assuming the change rate of the threshold of 3 mm is 45 mm / s², still lower than the threshold).

[0051] After generating the synchronization trigger sequence, the actions of the two robots are matched according to this sequence. The core of action matching is to ensure that the continuous feature paths are executed in sequence according to the synchronization trigger sequence, and each trigger threshold corresponds to the action node that the two robots need to reach synchronously. For example, when the synchronization trigger threshold is 1.5 mm, the ends of the two robots need to reach the spatial position corresponding to this threshold simultaneously, and the error needs to be controlled within the allowable deviation range (such as ±0.5 mm). When executing the continuous feature path, by real-time monitoring the pose data of the two robots (such as the sensor acquisition data described in Embodiment 1), it is judged whether the synchronization trigger condition is met. If the threshold is reached and the pose error of the two robots is within the allowable range, the next node action is triggered; if the error exceeds the range, an asynchronous processing flow (such as the phase compensation mechanism described in Embodiment 1) is triggered.

[0052] After completing the execution of all continuous feature paths, supplementarily execute the collaborative paths under discrete features. The execution order of discrete feature paths is determined according to the distribution density of their classification constraint parameters. The paths with higher density (indicating stronger synchronization constraints) are executed first. For example, if the density of the synchronization constraint parameters of a certain discrete feature in the interval [0.3, 0.6) is 0.6 and the density in the interval [0.6, 1.0] is 0.4, then the priority of this path is higher than that of the discrete path with a density distribution of [0.5, 0.5]. When a discrete path is executed, a certain phase difference is allowed between the two robots. For example, the preset phase difference is ±50 ms, that is, it is allowed that there is a delay or advance of no more than 5 ms in the action execution time of the two robots to meet the adjustment requirements of non-critical actions.

[0053] In the practical application of the synchronization rule, the following details need to be noted: The physical meaning of the synchronization trigger threshold needs to be strictly matched with the precision requirements of the yarn hanging process. For example, for the yarn docking action with a micron-level precision, the threshold may be set to 0.01 mm, while for the roving transportation action, the threshold can be relaxed to 10 mm; The smoothness analysis of the speed curve needs to consider the dynamic characteristics of the robot to avoid motor overload or trajectory deviation caused by sudden speed changes; The phase difference tolerance of the discrete feature path needs to be verified through robot kinematic simulation to ensure that asynchronous execution will not cause mechanical interference or process failure.

[0054] The generation of the synchronization trigger sequence needs to dynamically adapt to the real-time working conditions. For example, when the two robots detect an external disturbance (such as a sudden change in yarn tension) during the execution of a certain continuous path, resulting in the current synchronization trigger threshold not being met, the system needs to automatically pause the sequence execution, trigger error compensation, and then re-evaluate the validity of the threshold. This dynamic adjustment mechanism ensures the robustness of the synchronization rule in a complex environment.

[0055] Embodiment 4: In the yarn hanging method for the collaborative control of double composite robots, verifying the effectiveness of execution nodes is a key link in judging the collaborative state of the two robots. Taking the actual process of the two robots, RobotA and RobotB, collaborating to hang yarn as an example in this embodiment, the specific implementation methods of verifying the effectiveness of execution nodes and judging the asynchronous state are described in detail.

[0056] Suppose in a certain yarn hanging task, the two robots need to jointly complete the transfer of yarn from the yarn reel to the loom. The collaborative action sequence is divided into three stages: RobotA holds the yarn from the yarn reel (node 1), RobotB moves to the designated position to receive the yarn (node 2), and both move synchronously to the yarn feeding port of the loom to release the yarn (node 3). Each stage corresponds to an execution node, and the system needs to verify the effectiveness of these three nodes in sequence.

[0057] First, data acquisition and coordinate system alignment transformation are carried out. After Node 1 is executed, the sensor collects the end pose data of RobotA and RobotB in real time. The end effector pose data of RobotA is based on its base coordinate system (denoted as Coordinate System A), and the coordinate values are (100mm, 200mm, 300mm, 0°, 0°, 0°) (representing the X, Y, Z coordinates and roll angle, pitch angle, yaw angle respectively); the end pose data of RobotB is based on its base coordinate system (denoted as Coordinate System B), and the coordinate values are (150mm, 250mm, 350mm, 0°, 0°, 0°). Since the positions of the bases of the two robots in the global coordinate system are different, the pose data of both needs to be mapped to the same global coordinate system.

[0058] The coordinate system alignment transformation is achieved through homogeneous matrix transformation. First, measure the pose parameters of Coordinate System B relative to the global coordinate system: Assume that the position of the origin of Coordinate System B in the global coordinate system is (50mm, 50mm, 0mm), and the attitude is the same as the global coordinate system (i.e., the rotation matrix is the identity matrix). Through the homogeneous matrix transform the end pose of RobotB from Coordinate System B to the global coordinate system G. The transformed coordinates are: The attitude remains unchanged (0°, 0°, 0°). At this time, the end poses of RobotA and RobotB in the global coordinate system are (100, 200, 300, 0°, 0°, 0°) and (200, 300, 350, 0°, 0°, 0°) respectively, completing the unification of the coordinate system.

[0059] Next, verification parameters are generated. The verification parameters include the end position deviation, attitude deviation, and time synchronization error of the two robots. The position deviation is calculated as the absolute value of the difference in X, Y, and Z coordinates in the global coordinate system, that is: Since the attitude deviation of both is 0°, the deviation is 0. The time synchronization error is the time difference between the two robots arriving at Node 1. Assume that RobotA arrives at t = 1.0s and RobotB arrives at t = 1.2s, and the error is 0.2s.

[0060] Input the above verification parameters into the verification model. The verification model is a machine learning model (such as a support vector machine) trained based on historical synchronization data. The model outputs a matching degree value (range 0 - 100%). The higher the value, the better the coordination. Assume that the verification result of Node 1 is a matching degree of 90%, and the set verification threshold is 85%. Since 90% is higher than the threshold, it is determined that Node 1 is in the synchronous execution state, and the two robots continue to execute the next node according to the preset phase difference.

[0061] When Node 2 is executed, assume that RobotA needs to move to (150mm, 220mm, 320mm) and RobotB needs to move to (180mm, 280mm, 330mm). After collecting the actual pose data and performing coordinate system transformation, the actual coordinates of RobotA are (155mm, 225mm, 325mm), and the actual coordinates of RobotB are (190mm, 290mm, 340mm). Calculate the position deviation: The time synchronization error is 0.1s (RobotA arrives at t = 2.5s and RobotB arrives at t = 2.6s). After inputting into the verification model, the output matching degree is 82%, which is lower than the verification threshold of 85%. It is determined that Node 2 is in an asynchronous execution state, and an exception handling process is triggered.

[0062] After the positioning exception execution node, the system first obtains the target trajectory points of the two robots that are not synchronously executed under this node. The target trajectory points of Node 2 are RobotA (150mm, 220mm, 320mm) and RobotB (180mm, 280mm, 330mm), and the actual trajectory points are the above-collected (155mm, 225mm, 325mm) and (190mm, 290mm, 340mm). When calculating the spatial offset, taking the target points as the reference respectively, the offset of RobotA is √[(155 - 150)²+(225 - 220)²+(325 - 320)²]=√(25 + 25 + 25)=√75≈8.66mm, and the offset of RobotB is √[(190 - 180)²+(290 - 280)²+(340 - 330)²]=√(100 + 100 + 100)=√300≈17.32mm. Take the larger value of the two, 17.32mm, as the real-time phase offset.

[0063] Subsequently, perform historical trajectory backtracking to extract the historical phase offset of the previous node (Node 1) of Node 2. The offset of Node 1 has been controlled within the allowable range in the synchronous state (for example, the preset maximum allowable offset is 20mm). Therefore, the number of backtracking times is 1 time, which does not exceed the maximum allowable offset, and the backtracking stops. At this time, the system records the real-time phase offset and the historical offset, providing a data basis for subsequent phase compensation.

[0064] During the entire verification process, the following details need to be noted: The accuracy of the coordinate system alignment transformation depends on the initial calibration of the robot base. If there are errors in the calibration, it will lead to deviations in the verification parameters. Therefore, it is necessary to regularly calibrate the base pose through equipment such as laser trackers; the training data of the verification model needs to cover normal synchronization samples under different process scenarios to ensure the accurate identification of abnormal states by the model; the calculation of the real-time phase offset needs to be based on the pose data with the same timestamp to avoid error amplification caused by asynchronous sampling.

[0065] Embodiment 5: In the yarn hanging method for the cooperative control of dual composite robots, phase compensation for abnormal execution nodes in the asynchronous execution state is a key link to ensure the restoration of cooperation between the two robots. In this embodiment, taking the asynchronous state of dual robots RobotA and RobotB in the yarn hanging task as an example, the specific implementation process of phase offset calculation and compensation is described in detail.

[0066] Suppose when node 4 of a certain yarn hanging task is executed, RobotA and RobotB need to synchronously move the yarn to the specified release position. The preset execution time of node 4 is t = 4.0s, and the target trajectory points of the two robots are RobotA (300mm, 400mm, 500mm) and RobotB (350mm, 450mm, 550mm) respectively, and the preset phase difference is 0ms (i.e., they need to arrive simultaneously). When the system determines that node 4 is in the asynchronous execution state (such as when the verification matching degree is lower than the threshold), the phase offset calculation and compensation process is started.

[0067] First, locate the abnormal execution node and obtain the unsynchronized target trajectory points. The actual pose data of RobotA and RobotB at t = 4.0s are collected in real time. After coordinate system alignment transformation, the actual coordinates of RobotA are (310mm, 415mm, 505mm), and the actual coordinates of RobotB are (365mm, 470mm, 560mm). The unsynchronized target trajectory points are the preset (300mm, 400mm, 500mm) and (350mm, 450mm, 550mm). Calculate the spatial offset between the actual point and the target point: The offset of RobotA is the square root of the sum of the squares of the coordinate differences of each axis, that is, √[(310 - 300)²+(415 - 400)²+(505 - 500)²]=√(100 + 225 + 25)=√350≈18.71mm; The offset of RobotB is √[(365 - 350)²+(470 - 450)²+(560 - 550)²]=√(225 + 400 + 100)=√725≈26.93mm. Take the larger value of the two, 26.93mm, as the real-time phase offset, which reflects the spatial deviation between the current execution state and the target state of the dual robots.

[0068] Next, perform historical trajectory backtracking to extract the historical phase offsets of the previous nodes. Assume that the previous node of Node 4 is Node 3. When Node 3 was executed, an asynchronous state occurred due to slight yarn winding, and the recorded historical phase offset was 15mm (not exceeding the maximum allowable offset of 20mm). Continue to backtrack to Node 2, with a historical offset of 8mm (both within the allowable range), until backtracking to Node 1 (the initial node, with an offset of 0mm) and then stop backtracking. At this time, the cumulative number of backtracking times is 3 times (Node 3, Node 2, Node 1), which is used as the compensation priority parameter.

[0069] Set the compensation threshold to 3 times. When the compensation priority parameter (3 times) is equal to the compensation threshold, trigger the phase compensation operation. The compensation strategy adjusts the trajectory parameters of the abnormal execution node according to the total phase offset. The total phase offset comprehensively considers the real-time offset and the historical offset. Here, take the superposition value of the real-time offset of 26.93mm and the historical maximum offset of 15mm (assuming the system uses an accumulation algorithm), and the total offset is 41.93mm. The system re-plans the execution path of Node 4 according to this offset, and the specific adjustment method is as follows: Extend the execution time of RobotB to reduce its moving speed and reduce the displacement deviation per unit time; Fine-tune the trajectory of RobotA by adding intermediate transition points to gradually reduce the offset between the two in subsequent movements.

[0070] The adjusted execution node parameters include the new pose coordinates and timestamps. For example, the target trajectory point of RobotB is adjusted to reach in stages: first move to (357.5mm, 457.5mm, 555mm) at t = 4.2s, and then reach the final target point (350mm, 450mm, 550mm) at t = 4.4s, compensating for the phase difference by means of segmented deceleration. RobotA maintains its original speed, but pauses for 0.2s at t = 4.2s to wait for RobotB to adjust, ensuring that the two reach the adjusted target point synchronously at t = 4.4s.

[0071] During the compensation process, the system continuously monitors the phase offset. If the offset is detected again to exceed the allowable range (e.g., 20 mm) after compensation, the number of backtracking times increases to 4 times, exceeding the compensation threshold 3 times. At this time, a deeper compensation is triggered: recalculate the inverse kinematic solutions of the two robots, adjust the joint angle parameters, and generate new execution nodes. For example, the joint angle of RobotB is adjusted from 60° to 65° to change the curvature of the end trajectory, shorten the actual path length, and accelerate the speed of reaching the target point.

[0072] The application of homogeneous matrix transformation in coordinate system alignment runs through the entire process. For example, after each acquisition of pose data, the data of RobotB is converted to the base coordinate system of RobotA through the pre-calibrated relative pose matrix of the robot base, ensuring that the offset calculation is based on the same reference benchmark. If an error is found in the base calibration parameters during the compensation process (such as the base displacement caused by mechanical vibration), the system will automatically trigger the re-calibration process, measure the real-time distance between the bases through a laser rangefinder, and update the transformation matrix parameters to avoid compensation failure caused by reference deviation.

[0073] The following details need to be noted in practical applications: The setting of the compensation threshold needs to balance the system response speed and stability. Too low a threshold may lead to frequent compensation and affect efficiency, while too high a threshold may lead to error accumulation; The adjustment of trajectory parameters needs to avoid exceeding the physical limits of the robot (such as maximum speed, acceleration) to prevent mechanical overload; The time interval of the segmented compensation strategy needs to be determined according to the motion control cycle of the robot, usually set as an integer multiple of the control cycle (e.g., 100 ms) to ensure the synchronization of instruction execution.

[0074] This embodiment demonstrates the complete phase compensation process from real-time offset calculation, historical trajectory backtracking to trajectory parameter adjustment through a specific asynchronous node compensation case. Through the dynamic matching of the priority parameter and the compensation threshold, the system can flexibly select the compensation strategy according to the error degree. Combined with the precise calibration of the coordinate system reference, it ensures that the two robots quickly resume the cooperative state under complex working conditions, and guarantees the continuity and accuracy requirements of the yarn hanging task. [[ID=IO]]

[0075] Embodiment 6: In a specific implementation scenario, there are two composite robots that need to cooperate to complete the yarn hanging task. Both of these composite robots are composed of an AGV mobile platform and a collaborative robotic arm, and are equipped with a laser SLAM navigation system, a vision sensor, and a force sensor.

[0076] First, motion parameters are acquired and preprocessed. A laser SLAM navigation system collects the AGV's position and posture information in real time. Encoders are used to obtain angle data for each joint of the collaborative robot arm. The trajectory of the end effector is collected using both visual and force sensors. This data is stored in separate datasets, each with a unique action identifier, such as "AGV moves to point A" or "Robot arm grasps yarn." The data is then preprocessed to remove outliers. For example, joint angles outside of physical limits are identified as outliers and removed. The processed data is then kinematically decomposed to unify the data into collaborative path benchmarks in meters and radians.

[0077] Then, the motion features of the collaborative path benchmark are identified and synchronization rules are determined. Key points on the trajectory, such as the path's starting point, end point, and turning point, are extracted, and a sequence of motion nodes is generated in the order of execution. The spatial distance between adjacent motion nodes is calculated and used as a synchronization constraint parameter. A tolerance range is set, and the synchronization constraint parameters are normalized to form multiple synchronization intervals. The distribution density of the synchronization constraint parameters within each synchronization interval is calculated. If the distribution density of an interval is high, it is marked as a continuous feature; otherwise, it is marked as a discrete feature. Continuous features have a higher synchronization priority than discrete features. Based on this, the minimum value of the synchronization interval corresponding to the continuous feature is used as the synchronization trigger threshold. These thresholds are arranged in ascending order to generate a synchronization trigger sequence. When executing the collaborative path, the path corresponding to the continuous feature is executed first, followed by the path corresponding to the discrete feature.

[0078] Next, collaborative path planning and execution node verification are performed. Based on the synchronous trigger sequence, multiple collaborative action sequences and the execution nodes for each sequence are planned. During execution, the actual pose data of the two robots is collected in real time. This data is mapped to the same global coordinate system through a homogeneous matrix transformation, eliminating the relative pose deviation between the two robots' base coordinate systems. The processed data is input into the verification model, and the matching degree is obtained as the verification result. If the verification result exceeds the set verification threshold, the execution node is in the synchronous execution state; if it is below or equal to the verification threshold, it is in the asynchronous execution state.

[0079] When the asynchronous execution state appears, calculate the phase offset and perform compensation. Obtain the target trajectory points that have not been synchronously executed under the abnormal execution node, calculate the spatial offset between this point and the current trajectory point, and obtain the real-time phase offset. Conduct historical trajectory backtracking on the previous nodes of the abnormal execution node and extract the historical phase offset. Stop backtracking when the historical phase offset is less than or equal to the maximum allowable offset. Input the real-time phase offset and the historical phase offset into the compensation algorithm to obtain the total phase offset. Determine the compensation priority parameter according to the backtracking times of the abnormal execution node. When this parameter exceeds the compensation threshold, adjust the trajectory parameters of the abnormal execution node based on the total phase offset to generate an updated execution node; when it does not exceed the compensation threshold, continuously monitor the phase offset until the compensation threshold is reached and then trigger compensation. Through the above steps, the dual composite robot can efficiently and stably cooperate to complete the yarn hanging task.

[0080] It should be noted that in this article, relational terms such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variant thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements not only includes those elements, but also includes other elements not expressly listed, or also includes elements inherent to such process, method, article or device.

[0081] Although the embodiments of the present invention have been shown and described, for those of ordinary skill in the art, it can be understood that various changes, modifications, substitutions and variations can be made to these embodiments without departing from the principles and spirit of the present invention. The scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A yarn hanging method for collaborative control of a double composite robot, characterized in that: Including: Obtain the motion parameters of the dual composite robot, perform preprocessing, and generate a collaborative path benchmark; Identify the action features of each of the collaborative path benchmarks, and determine the synchronization rules for the dual robots based on the action features; Perform collaborative path planning for the dual robots according to the synchronization rules to form multiple groups of collaborative action sequences and the execution nodes of each group of the collaborative action sequences; Verify the effectiveness of each of the execution nodes to obtain the collaborative state of each execution node, where the collaborative state includes a synchronous execution state and an asynchronous execution state; In the synchronous execution state, the collaborative action sequences of the dual robots are executed with a preset phase difference; In the asynchronous execution state, locate the abnormal execution nodes and calculate the phase offset of the collaborative action sequences under the abnormal execution nodes; Based on the phase offset, perform phase compensation on the abnormal execution nodes until the collaborative action sequences of the dual robots resume the preset phase difference and then terminate.

2. The yarn hanging method for collaborative control of a double composite robot according to claim 1, characterized in that: The obtaining the motion parameters of the dual composite robot, performing preprocessing, and generating a collaborative path benchmark includes: Collect the pose data, joint angles, and end trajectories of the dual robots through sensors, store them as independent data sets respectively, and add a unique action identifier to each data set; Eliminate the outliers in the data within each of the independent data sets, and filter out the data items that exceed the physical constraints; Perform kinematic decomposition on the processed independent data sets, extract the translational component and the rotational component, and output them as a collaborative path benchmark after unifying the component parameters into the same unit.

3. A yarn hanging method for collaborative control of a double composite robot according to claim 1, characterized in that: The identifying the action features of each of the collaborative path benchmarks includes: Extract the trajectory key points under each collaborative path benchmark, and generate an action node sequence according to the execution timing; Calculate the spatial distance between adjacent action node sequences and calibrate it as a synchronization constraint parameter; Set an allowable deviation range, and perform normalization processing on the synchronization constraint parameters according to the allowable deviation range to generate multiple synchronization intervals; Statistically analyze the distribution density of the synchronization constraint parameters within each synchronization interval, mark it as a classification constraint parameter, and respectively determine the action features of the collaborative path benchmarks based on the classification constraint parameters; Wherein, the action features include continuous features and discrete features, and the synchronization priority of the continuous features is higher than that of the discrete features.

4. A yarn hanging method for cooperative control of a double composite robot according to claim 3, characterized in that: The respectively determining the action features of the collaborative path benchmarks based on the classification constraint parameters includes: Obtain the classification constraint parameters corresponding to each collaborative path benchmark; Sort the classification constraint parameters under the same collaborative path benchmark in the density intervals from dense to sparse, and calculate the coverage ratio of the maximum density interval; Set a determination threshold, and compare the coverage ratio with the determination threshold; If the coverage ratio is higher than the determination threshold, determine that the collaborative path benchmark has continuous features; If the coverage ratio is lower than or equal to the determination threshold, determine that the collaborative path benchmark has discrete features.

5. A yarn hanging method for collaborative control of a double composite robot according to claim 3, characterized in that: The determining the synchronization rules for the dual robots based on the action features includes: Obtain the action features of each collaborative path benchmark; Take the minimum value of the synchronization interval corresponding to the continuous feature as the synchronization trigger threshold; Extract the synchronous trigger thresholds under all continuous features, arrange them in ascending order, and generate a synchronous trigger sequence; Match the actions of the two robots according to the synchronous trigger sequence, and after the collaborative path under the continuous features is completed, supplement the execution of the collaborative path under the discrete features.

6. A yarn hanging method for collaborative control of a double composite robot according to claim 1, characterized in that: The validity verification of each of the execution nodes includes: Collect the actual pose data of the two robots under each execution node, perform coordinate system alignment transformation, and generate multiple verification parameters; Call the verification model, input the verification parameters into the model, and mark the matching degree output by the model as the verification result; Set a verification threshold, and compare the verification result with the verification threshold; If the verification result is higher than the verification threshold, it is determined that the execution node is in a synchronous execution state; If the verification result is lower than or equal to the verification threshold, it is determined that the execution node is in an asynchronous execution state.

7. A yarn hanging method for cooperative control of a double composite robot according to claim 1, characterized in that: The calculation of the phase offset of the collaborative action sequence under the abnormal execution node includes: Obtain the target trajectory points where the two robots under the abnormal execution node are not synchronously executed; Calculate the spatial offset between the target trajectory point and the current trajectory point, and mark it as the real-time phase offset; Perform historical trajectory backtracking on the abnormal execution node, and extract the historical phase offset under its previous node; Set a maximum allowable offset, and stop backtracking when the historical phase offset is less than or equal to the maximum allowable offset; Call the compensation algorithm, input the real-time phase offset and the historical phase offset into the algorithm, and mark the output result of the algorithm as the total phase offset.

8. A yarn hanging method for cooperative control of a double composite robot according to claim 7, characterized in that: Based on the phase offset, perform phase compensation on the abnormal execution node, including: Obtain the number of backtracking times of the abnormal execution node, and mark it as the compensation priority parameter; Set a compensation threshold, and compare the compensation priority parameter with the compensation threshold; When the compensation priority parameter exceeds the compensation threshold, adjust the trajectory parameters of the abnormal execution node according to the total phase offset to generate an updated execution node; When the compensation priority parameter does not exceed the compensation threshold, continuously monitor the phase offset until the priority parameter reaches the compensation threshold and then trigger compensation.

9. A yarn hanging method for collaborative control of a double composite robot according to claim 5, characterized in that: The generation steps of the synchronous trigger sequence further include: Extract the robot end velocity curves corresponding to each synchronous trigger threshold; Perform secondary sorting on the synchronous trigger thresholds according to the smoothness of the velocity curves, and preferentially select the synchronous trigger thresholds with a velocity change rate lower than the set value.

10. A yarn hanging method for collaborative control of a double composite robot according to claim 6, characterized in that: The steps of the coordinate system alignment transformation include: Map the pose data of the end effectors of the two robots to the same global coordinate system; Eliminate the relative pose deviation of the base coordinate systems of the two robots through homogeneous matrix transformation and unify them to the same reference benchmark.

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