Three-dimensional knitting optimization cooperative control method and system applied to bionic robot
By dividing the three-dimensional model of the bionic robot into regions and constructing a stress simulation model, and combining real-time acquisition and dynamic optimization control, the adaptability and accuracy problems of traditional three-dimensional weaving technology in bionic robots have been solved, and an efficient and stable weaving process has been achieved.
Patent Information
- Application Number
- CN202610364538.3
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2026-03-24
- Publication Date
- 2026-05-15
AI Technical Summary
Traditional 3D weaving technology is difficult to adapt to the complex curved surfaces, multi-joint transitions, and differentiated mechanical function requirements of biomimetic robots, resulting in deviations between the woven structure and the designed mechanical properties. Furthermore, the control method cannot adaptively optimize tension and traction speed, affecting weaving accuracy and stability.
By dividing the three-dimensional model of the bionic robot into functional areas, setting differentiated basic weaving parameters, constructing a standard force simulation model and performing multi-module initialization parameter calculation, collecting the yarn position and mechanical parameters in real time during the weaving process, performing trajectory fusion calculation and dynamic optimization control, the coordinated movement of the yarn carrier and the robotic arm is realized.
It improves the mechanical adaptability and structural rationality of soft tissue weaving by bionic robots, ensures the stability and precision of the weaving process, realizes efficient collaborative control between the yarn carrier and the robotic arm, and improves the weaving quality and automation level.
Smart Images
Figure CN122033966A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of weaving control technology, specifically a three-dimensional weaving optimization and collaborative control method and system applied to biomimetic robots. Background Technology
[0002] As bionic robots develop towards softness, integration, and high bionic mechanical performance, three-dimensional weaving technology has gradually become an important process for manufacturing robot soft tissue and bionic structures. Traditional three-dimensional weaving methods are mostly used for regular components, which are difficult to adapt to the complex curved surfaces, multi-joint transitions, and differentiated mechanical function requirements of bionic robots. When weaving in sections for bionic structures, it is impossible to match the corresponding weaving parameters according to the biomechanical characteristics of different parts, resulting in deviations between the woven structure and the designed mechanical performance. Traditional control methods struggle to adaptively optimize tension and traction speed based on the actual weaving state, failing to guarantee the accuracy, stability, and structural consistency of three-dimensional soft tissue weaving in bionic robots. This hinders the large-scale application of three-dimensional weaving technology in the manufacturing of high-performance bionic robots.
[0003] Therefore, a three-dimensional weaving optimization collaborative control method and system for biomimetic robots is provided. Summary of the Invention
[0004] To address the aforementioned technical problems, the present invention aims to provide a three-dimensional weaving optimization and collaborative control method and system for biomimetic robots.
[0005] To achieve the above objectives, the present invention provides the following technical solution: a three-dimensional weaving optimization and collaborative control method applied to biomimetic robots, the method comprising: The standard bionic robot's 3D model is divided into regions, and then the basic weaving parameters for each region are set. Construct a standard stress simulation model; input the basic weaving parameters of the bionic robot to be woven and the mechanical parameters of the yarn into the standard stress simulation model in real time, and then obtain the initial yarn carrier motion matrix, the initial robotic arm spatial trajectory matrix and the multi-module collaborative timing table; Based on the initial yarn carrier motion matrix, the robotic arm spatial trajectory matrix, and the multi-module collaborative timing table, the trajectory fusion calculation of the chassis planar motion of the yarn carrier and the spatial posture motion of the robotic arm is performed to generate collaborative motion commands. Real-time acquisition of the spatial position of the yarn during the weaving process of executing cooperative motion commands; if yarn interference or bridging risk is detected, slight corrections are made to the motion trajectory of the yarn carrier and the posture of the robotic arm to generate the optimal obstacle avoidance path. The actual tension value of the yarn and the local curvature value of the skeleton core mold are collected in real time, and the data are verified and compared to obtain dynamic optimization instructions. Based on the correlation algorithm between yarn crimp and weaving angle and dynamic optimization instructions, the tension of the yarn carrier and the traction speed of the robotic arm are optimized and coordinated for control, thereby precisely controlling the tension of the yarn carrier and the traction speed of the robotic arm to ensure that the soft tissue weaving of the bionic robot meets the design requirements.
[0006] Furthermore, the process of dividing the 3D model of a standard bionic robot into regions includes: The standard biomimetic robot's 3D model is divided into regions based on differences in mechanical function, including joint areas and skeletal areas. The connecting parts are divided into transitional connection areas and marked as such. Based on the biomimetic biomechanical requirements of each region, basic weaving parameters are set for the corresponding regions. These basic weaving parameters include the initial weaving angle, yarn tension threshold, layer thickness, functional device embedding position, and online curing power.
[0007] Furthermore, the process of constructing a standard stress simulation model includes: Using the marked area corresponding to the standard bionic robot as the spatial reference, the basic weaving parameters and mechanical parameters of the yarn in each area are obtained; and based on the three-dimensional modeling and simulation platform, the overall framework of the standard force simulation model is built; the overall framework includes a yarn carrier unit, a robotic arm motion unit, a yarn force unit, and a collaborative control unit. Using preset standard weaving scene parameters, the yarn carrier unit, robotic arm motion unit, yarn force unit, and collaborative control unit are calibrated under no-load and load conditions. The results of the no-load and load calibrations are then evaluated. If the results meet the requirements, the model construction is complete; otherwise, the correlation parameters between modules are adjusted for further evaluation.
[0008] Furthermore, the process of trajectory fusion calculation for the planar motion of the yarn carrier chassis and the spatial attitude motion of the robotic arm includes: Extract the chassis planar motion parameters from the initial yarn carrier motion matrix and the spatial attitude parameters from the robotic arm spatial trajectory matrix; establish a trajectory fusion coordinate system using a multi-module collaborative timing table as the time reference; and use a Kalman filter algorithm to denoise the chassis planar motion parameters and spatial attitude parameters. Based on the forward kinematics principle, the planar motion of the chassis and the spatial posture motion of the robotic arm are coupled and solved to construct a fused motion equation. The cooperative motion parameters of the yarn carrier and the robotic arm are obtained by solving the equation. According to the cooperative motion parameters, corresponding cooperative motion commands are generated, including the movement speed of the yarn carrier, the traction speed of the robotic arm, the posture adjustment angle, and the action execution sequence.
[0009] Furthermore, the process of making minor corrections to the motion trajectory of the yarn carrier and the posture of the robotic arm includes: The spatial coordinate data of the yarn during the weaving process is collected in real time by visual and position sensors to execute coordinated motion commands; the spatial coordinate data is then compared and analyzed to determine whether there is a risk of yarn interference or bridging. If there are risks of yarn interference and bridging, the displacement correction of the yarn carrier and the attitude correction of the robotic arm are calculated based on the initial motion matrix of the yarn carrier and the initial spatial trajectory matrix of the robotic arm, using the obstacle avoidance optimization algorithm as a benchmark; if there are no such risks, no micro-correction is required. Based on the multi-module collaborative timing table, displacement correction amount, and attitude correction amount, an optimal obstacle avoidance path is generated, driving the yarn carrier and robotic arm to perform weaving actions according to the corrected optimal obstacle avoidance path.
[0010] Furthermore, the process of verifying and comparing the real-time acquired actual tension values and local curvature values includes: The system collects the actual tension and local curvature values during the yarn weaving process in real time; it presets the yarn tension threshold range and the standard value of the local curvature of the skeleton core mold; it compares the collected actual tension value with the preset threshold range, and if the actual tension value exceeds the threshold range, it is marked as tension abnormal; it compares the collected local curvature value with the standard value and calculates the curvature deviation value, and if the deviation value exceeds the preset allowable range, it is marked as curvature abnormal. Based on the tension anomaly and curvature anomaly, a corresponding dynamic optimization instruction is generated; the dynamic optimization instruction includes a tension adjustment coefficient and a curvature compensation parameter.
[0011] Furthermore, based on the correlation algorithm between yarn crimp and knitting angle, and dynamic optimization instructions, the process of optimizing and coordinating the tension of the yarn carrier and the traction speed of the robotic arm includes: Based on experimental calibration of yarn crimp benchmark values under different knitting angles, a nonlinear correlation equation between yarn crimp and knitting angle is derived. The correlation equation has knitting angle as independent variable and yarn crimp as dependent variable, and is corrected according to the mechanical parameters of the yarn. Substitute the tension adjustment coefficient and curvature compensation parameter from the dynamic optimization command into the correlation equation to obtain the target weaving angle and target crimp; based on the target weaving angle, calculate the adjustment amount of the yarn carrier tension and the adjustment amount of the robotic arm traction speed; Through a closed-loop control algorithm, the tension output of the yarn carrier and the traction speed of the robotic arm are adjusted in real time to make the yarn crimp conform to the target crimp. The actual tension value and local curvature value after adjustment are fed back in real time, and then verified and compared again. In this way, the adjustment amount of the yarn carrier tension and the adjustment amount of the robotic arm traction speed are calculated in real time.
[0012] A second aspect of the present invention also provides a three-dimensional weaving optimization and collaborative control system for bionic robots, the system comprising a model partitioning parameter configuration module, a simulation modeling initialization module, a trajectory fusion module, an obstacle avoidance trajectory correction module, a parameter verification dynamic optimization module, and a precise collaborative control module; The model partitioning parameter configuration module is used to divide the three-dimensional model of the bionic robot into regions and then set the basic weaving parameters for each region. The simulation modeling initialization module is used to construct a real-time stress simulation model. The basic weaving parameters and the mechanical parameters of the yarn are input into the real-time stress simulation model to obtain the initial yarn carrier motion matrix, the robotic arm spatial trajectory matrix, and the multi-module collaborative timing table. The trajectory fusion module performs trajectory fusion calculation on the chassis planar motion of the yarn carrier and the spatial attitude motion of the robotic arm based on the initialized yarn carrier motion matrix, the robotic arm spatial trajectory matrix and the multi-module collaborative timing table, and generates collaborative motion commands. The obstacle avoidance trajectory correction module is used to collect the spatial position of the yarn in real time during the weaving process of executing cooperative motion commands. If yarn interference and bridging risks are detected, the module makes slight corrections to the motion trajectory of the yarn carrier and the posture of the robotic arm to generate the optimal obstacle avoidance path. The parameter verification and dynamic optimization module is used to collect the actual tension value of the yarn and the local curvature value of the skeleton core mold in real time, and perform verification and comparison to obtain dynamic optimization instructions. The precision collaborative control module, based on the correlation algorithm between yarn crimp and weaving angle and dynamic optimization instructions, optimizes and coordinates the tension of the yarn carrier and the traction speed of the robotic arm, thereby precisely controlling the tension of the yarn carrier and the traction speed of the robotic arm to ensure that the soft tissue weaving of the bionic robot meets the design requirements.
[0013] Compared with existing technologies, the beneficial effects of this invention are as follows: By dividing the three-dimensional model of the bionic robot into functional areas and matching differentiated basic weaving parameters, this invention enables the weaving structure to better fit the biomechanical requirements of the bionic parts, improving the mechanical adaptability and structural rationality of the soft tissue of the bionic robot. By constructing a standard force simulation model and completing the initialization parameter calculation of multiple modules, the pre-planning of the yarn carrier and the robotic arm's movement can be realized, providing a reliable data foundation for collaborative control. By adopting trajectory fusion calculation and temporal collaborative control, the planar motion of the yarn carrier and the spatial posture motion of the robotic arm can be efficiently coordinated, improving the continuity and synchronization of the weaving action. By detecting the spatial position of the yarn in real time during the weaving process and making micro-corrections, the risks of yarn interference and bridging can be effectively avoided, ensuring the stability and reliability of the weaving process. By collecting tension and curvature parameters in real time and combining them with the yarn crimp and weaving angle correlation algorithm for dynamic optimization, precise closed-loop control of yarn tension and robotic arm traction speed can be achieved, ensuring that the actual weaving effect continuously meets the design requirements, and improving the overall forming quality, control accuracy and automation level of the three-dimensional weaving of the bionic robot. Attached Figure Description
[0014] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in this invention. For those skilled in the art, other drawings can be obtained based on these drawings.
[0015] Figure 1 This is a schematic diagram illustrating the steps of a three-dimensional weaving optimization collaborative control method applied to biomimetic robots.
[0016] Figure 2 A schematic diagram illustrating the steps involved in constructing a standard stress simulation model.
[0017] Figure 3 This is a schematic diagram illustrating the steps involved in trajectory fusion calculation of the planar motion of the yarn carrier's chassis and the spatial posture motion of the robotic arm.
[0018] Figure 4 A schematic diagram illustrating the steps for making minor corrections to the motion trajectory of the yarn carrier and the posture of the robotic arm.
[0019] Figure 5 This diagram illustrates the steps for real-time acquisition and verification of actual yarn tension values and local curvature values of the skeleton core mold. Detailed Implementation
[0020] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be described in detail below. Obviously, the described embodiments are merely some embodiments of this invention, and not all embodiments. Based on the embodiments of this invention, all other implementation methods obtained by those skilled in the art without creative effort are within the scope of protection of this invention.
[0021] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.
[0022] like Figure 1 As shown, a three-dimensional weaving optimization cooperative control method applied to biomimetic robots includes the following steps: Step S1: Divide the standard bionic robot's 3D model into regions, and then set the basic weaving parameters for each region.
[0023] It should be noted that by dividing the three-dimensional model of the bionic robot into functional regions and matching differentiated basic weaving parameters, the weaving structure can conform to the biomechanical requirements of the bionic parts, providing a reliable data foundation and model support for subsequent collaborative control.
[0024] In this embodiment, the standard bionic robot's three-dimensional model is divided into regions based on differences in mechanical function, including joint regions and skeletal regions, and the connecting parts are divided into transitional connection regions, which are marked as labeled regions. According to the bionic biomechanical requirements of each region, the basic weaving parameters of the corresponding regions are set. The basic weaving parameters include the initial weaving angle, yarn tension threshold, layer thickness, functional device embedding position, and online curing power.
[0025] To further explain, the standard 3D model of a bionic robot covers various soft and bionic structures, including the whole robot and core soft tissue component models, such as bionic manipulators, bionic limbs, and bionic torsos; the basic weaving parameters are the core quantitative indicators for adapting the mechanical functions of each bionic region.
[0026] In this embodiment, the process of dividing the standard bionic robot's 3D model into regions and setting the basic weaving parameters for each region includes: Step S100: Import the standard bionic robot 3D model into the 3D modeling and simulation platform, divide the model into regions according to the differences in mechanical functions, clarify the joint area (high mobility and high flexibility bionic mechanical requirements) and the skeletal area (high rigidity and high load-bearing bionic mechanical requirements), and divide the connection between the joint and the skeletal area into a transitional connection area. All divided areas are uniquely marked and recorded as marked areas, thus realizing the functional zoning of the bionic robot 3D model.
[0027] Step S101: Based on the biomimetic biomechanical requirements of each marked area, basic weaving parameters are set accordingly. The basic weaving parameters include initial weaving angle, yarn tension threshold, layer thickness, functional device embedding position, and online curing power. Among them, the joint area is set with a smaller initial weaving angle, a lower yarn tension threshold, and a thinner layer thickness, while the backbone area is set with a larger initial weaving angle, a higher yarn tension threshold, and a thicker layer thickness. The parameters of the transition area are taken as the median value of the adjacent functional areas to achieve a smooth transition of mechanical properties.
[0028] The yarn tension threshold is calculated based on the yarn's mechanical properties, using the following formula: ;in, This is the yarn tension threshold. For safety factors (within the range of 0.3 to 0.7, such as 0.3 for joint areas and 0.7 for skeletal areas); The elastic modulus of the yarn; The cross-sectional area of the yarn; Allowable strain for the yarn.
[0029] To further clarify, other parameters of the basic knitting parameters will not be further explained.
[0030] Step S2: Construct a standard force simulation model; input the basic weaving parameters of the bionic robot to be woven in real time and the mechanical parameters of the yarn into the standard force simulation model, thereby obtaining the initial yarn carrier motion matrix, the initial robotic arm spatial trajectory matrix and the multi-module collaborative timing table; It should be noted that constructing a standard force simulation model and completing the initialization parameter calculation for multiple modules enables the preliminary planning of the motion of the yarn carrier and the robotic arm, providing a reliable data foundation and model support for subsequent collaborative control. The initialization of the yarn carrier motion matrix and the robotic arm spatial trajectory matrix are structured parameter sets describing the motion state of the equipment, and the multi-module collaborative timing table is the time reference for realizing the linkage of the yarn carrier and the robotic arm's actions.
[0031] In this embodiment, the process of constructing a standard stress simulation model and completing the initialization parameter calculation for multiple modules includes: Step S200: Using the marked area as the spatial reference, extract the basic weaving parameters configured in each area, and at the same time collect the mechanical parameters of the yarn to be used, including the yarn elastic modulus, breaking strength, linear density, Poisson's ratio, etc. Organize the above parameters into a standardized parameter dataset to provide data support for the construction of the simulation model.
[0032] Step S201: Based on the 3D modeling and simulation platform, build the overall framework of the standard force simulation model. The overall framework includes a yarn carrier unit, a robotic arm motion unit, a yarn force unit, and a collaborative control unit. Each unit corresponds to the core functions of yarn conveying motion control, spatial traction posture control, yarn mechanical state detection, and multi-device action collaborative scheduling. Data interaction and linkage control interfaces are established between the units to realize the modular construction of the model framework.
[0033] The core of the yarn stress unit is the yarn constitutive equation, which reflects the stress-strain relationship of the yarn: ;in, For yarn stress; For yarn deformation; This is the original length of the yarn.
[0034] Step S202: Preset standard weaving scenario parameters, including weaving environment temperature, equipment motion accuracy, yarn conveying resistance, core mold support stiffness, etc., and perform no-load calibration and load calibration on the yarn carrier unit, robotic arm motion unit, yarn force unit and collaborative control unit in sequence; The formula for determining the accuracy error of no-load calibration is as follows: ; Load calibration collaborative response delay determination: ; in, These are the actual motion parameters of the equipment; These are the theoretical motion parameters of the equipment; The load is calibrated for coordinated response time. If the calibration results meet the preset accuracy and stability requirements, the model construction is complete. If not, the correlation parameters between modules are adjusted, such as the signal transmission delay coefficient between units, the motion linkage ratio, and the coupling coefficient of mechanical parameters, and the calibration is repeated until the requirements are met.
[0035] Step S203: Input the basic weaving parameters of the bionic robot to be woven and the mechanical parameters of the yarn into the standard force simulation model that has been built. Through the simulation calculation and parameter solution of the model, obtain the initial yarn carrier motion matrix and the initial robotic arm spatial trajectory matrix. The motion matrix of the yarn carrier is: ;in, , The speed of the yarn carrier moving along the X / Y axes; , The acceleration of the yarn carrier along the X / Y axes; , These are the initial position coordinates of the yarn carrier.
[0036] The spatial trajectory matrix of the robotic arm is constructed using the DH parameter method as follows: ; in, For the first robotic arm The angle of joint rotation; For the first robotic arm The torsion angle of the joint link; For the first robotic arm The length of the connecting rod in the joint; For the first robotic arm Linkage offset of the joint.
[0037] The formula for the collaborative timing trigger condition of the multi-module collaborative timing table is as follows: ;in, This refers to the action trigger time; This is the initial time; For the displacement of the equipment; To determine the average movement speed of the equipment, the execution sequence, time nodes, linkage triggering conditions, and duration of each action of the yarn carrier and the robotic arm are defined, and the initialization parameters and timing table of multiple modules are output.
[0038] In an exemplary example, the three-dimensional weaving of soft tissue in a bionic robotic hand involves dividing the three-dimensional model of the bionic robotic hand into three regions based on mechanical function: the finger joint region (high flexibility), the palm bone region (high rigidity), and the finger-palm transition region, thus completing region marking. Initial weaving angles of 30°, yarn tension thresholds of 5N (k=0.3), and layer thickness of 0.8mm are set for the finger joint region; initial weaving angles of 60°, yarn tension thresholds of 15N (k=0.7), and layer thickness of 2mm are set for the palm bone region; and intermediate values are used for the finger-palm transition region, completing the basic weaving parameter configuration. Mechanical parameters of the aramid yarn (elastic modulus 70GPa, breaking strength 3.5GPa, linear density) are collected. A standard force simulation model (200D) was built based on the marked area, including a yarn carrier unit, a robotic arm motion unit, a yarn force unit, and a collaborative control unit. The model was constructed through no-load and load calibration. The basic weaving parameters and yarn mechanical parameters were input into the model to calculate the initial yarn carrier planar motion matrix (X / Y axis motion speed 0.5m / s, motion trajectory is a contour curve similar to that of a robotic arm) and the robotic arm 6-axis spatial trajectory matrix (rotation angle range of each joint 0°~180°, end-effector traction speed 0.4m / s). A multi-module collaborative timing table was generated to clarify that after the yarn carrier moves to the designated position, the robotic arm triggers the traction action, and the action linkage delay is ≤10ms.
[0039] Step S3: Based on the initialized yarn carrier motion matrix, robotic arm spatial trajectory matrix, and multi-module collaborative timing table, perform trajectory fusion calculation on the chassis planar motion of the yarn carrier and the spatial attitude motion of the robotic arm to generate collaborative motion commands; It should be noted that the efficient coupling and temporal coordination of the planar motion of the yarn carrier and the spatial posture motion of the robotic arm are achieved through trajectory fusion calculation. The generated coordinated motion commands serve as the direct basis for the equipment to execute weaving actions.
[0040] In this embodiment, the trajectory fusion solution is based on the initialization of the motion matrix and the cooperative timing table. It achieves the fusion solution of the motion states of the two devices through coordinate system one, parameter denoising, and kinematic coupling. The cooperative motion command is a standardized execution command that includes the core motion parameters of the yarn carrier and the robotic arm, covering motion speed, attitude angle, execution timing, etc.
[0041] In this embodiment, the process of performing trajectory fusion calculation and generating cooperative motion commands for the chassis planar motion of the yarn carrier and the spatial posture motion of the robotic arm includes: Step S300: Extract the chassis planar motion parameters from the initialized yarn carrier motion matrix, extract the spatial attitude parameters from the initialized robotic arm spatial trajectory matrix, and establish a trajectory fusion coordinate system in the 3D modeling and simulation platform using the multi-module collaborative timing table as the time reference. Unify the planar motion parameters and spatial attitude parameters to the same coordinate system to achieve spatial normalization of motion parameters.
[0042] Step S301: Use the Kalman filter algorithm to denoise the normalized chassis planar motion parameters and spatial attitude parameters to eliminate parameter noise caused by equipment motion error, sensor detection error and environmental interference. Step S302: Based on the forward kinematics principle, the planar motion of the yarn carrier's chassis and the spatial attitude motion of the robotic arm are coupled and calculated to construct the trajectory fusion motion equation: ; in, , For the planar motion coordinates of the yarn carrier; , , The coordinates for the robotic arm's spatial motion; , , The coordinates for the fused collaborative motion are obtained by solving the fused motion equations. The collaborative motion parameters of the yarn carrier and the robotic arm are obtained, including the linkage motion speed, the attitude synchronization adjustment angle, the action execution interval, and the spatial position matching parameters.
[0043] Step S303: Based on the obtained cooperative motion parameters, generate corresponding cooperative motion commands. These commands include the movement speed of the yarn carrier, the traction speed of the robotic arm, the attitude adjustment angles of each joint of the robotic arm, and the execution sequence of each device's actions. The cooperative speed constraint formula is as follows: ;in, The speed of coordinated movement of the equipment; This is the maximum speed of movement of the yarn carrier; The maximum traction speed of the robotic arm is set; the coordinated motion command is sent to the execution control system of the yarn carrier and the robotic arm to drive the equipment to perform the weaving action.
[0044] Step S4: Real-time acquisition of the spatial position of the yarn during the weaving process of executing the cooperative motion command. If yarn interference and bridging risk are detected, the motion trajectory of the yarn carrier and the posture of the robotic arm are slightly corrected to generate the optimal obstacle avoidance path. It should be noted that by monitoring the spatial position of the yarn in real time and assessing the risks, the trajectory and posture can be slightly corrected, effectively avoiding the risks of yarn interference and bridging, and ensuring the stability and continuity of the weaving process.
[0045] In this embodiment, yarn interference and bridging risks are common process problems in three-dimensional weaving. Through real-time acquisition and analysis by vision and position sensors, accurate risk identification and dynamic obstacle avoidance can be achieved.
[0046] In this embodiment, the process of real-time acquisition of the spatial position of the yarn, minor correction, and generation of the optimal obstacle avoidance path includes: Step S400: Deploy vision sensors and position sensors in the weaving operation area. The detection range of the sensors covers the entire yarn movement trajectory. According to the preset sampling frequency, the spatial coordinate data of the yarn during the weaving process of executing coordinated motion commands are collected in real time. This ensures the real-time nature and completeness of the collected data.
[0047] Step S401: Compare the collected actual spatial coordinate data of the yarn with the preset theoretical spatial coordinate data of the yarn in the standard stress simulation model. Perform coordinate comparison analysis and calculate the three-dimensional Euclidean distance deviation: ; Determine the existence of yarn interference risk and bridging risk based on the three-dimensional Euclidean distance deviation value: Yarn interference risk assessment: ; The diameter of the yarn; Bridge construction risk assessment: ; Z-axis allowable deviation; If the deviation between the actual and theoretical coordinates of the yarn is within the preset allowable range, there is no risk as mentioned above, and the original coordinated motion command continues to be executed; if the deviation exceeds the allowable range, it is determined that there is a risk of interference or bridging, and trajectory and attitude correction is initiated.
[0048] Step S402: If there are risks of yarn interference and bridging, based on the initialization of the yarn carrier motion matrix and the initialization of the robotic arm spatial trajectory matrix, and using the obstacle avoidance optimization algorithm, the displacement correction of the yarn carrier and the attitude correction of the robotic arm are calculated using a PID correction algorithm according to the deviation between the actual and theoretical coordinates of the yarn. The core formula is: ; in, This is the correction amount for yarn carrier displacement; This is the amount of posture correction for the robotic arm; This is the proportionality coefficient; These are the differential coefficients; This refers to the theoretical posture angle of the robotic arm; This refers to the actual posture angle of the robotic arm; the correction should be made in small increments to avoid deformation of the woven structure due to large adjustments.
[0049] Step S403: Based on the multi-module collaborative timing table, calculate the displacement correction amount of the yarn carrier and the posture correction amount of the robotic arm, and then generate the optimal obstacle avoidance path. Convert the optimal obstacle avoidance path into a corrected collaborative motion command and send it to the execution control system to drive the yarn carrier and the robotic arm to perform weaving actions according to the corrected optimal obstacle avoidance path until the sensor detects that the yarn spatial coordinates have returned to the preset allowable range, and resume the execution of the original collaborative motion command.
[0050] In an exemplary case, during the weaving of bionic limb soft tissue, X / Y axis displacement and velocity parameters are extracted from the yarn carrier motion matrix, and rotation angles of each joint and Z-axis position parameters of the end effector are extracted from the robotic arm trajectory matrix. A trajectory fusion coordinate system is established based on a coordinated timing table. After removing parameter noise using a Kalman filter algorithm, a fusion motion equation is constructed based on the forward kinematics principle. The coordinated motion parameters are then calculated, generating coordinated motion commands for a yarn carrier velocity of 0.3 m / s, a robotic arm traction speed of 0.25 m / s, and a robotic arm shoulder joint adjustment angle of 5°. These commands are then used for visual and positional analysis. The sensor collects the yarn's spatial coordinates in real time and finds that the actual Y-axis coordinate of the yarn deviates from the theoretical coordinate by 8mm. The three-dimensional Euclidean distance deviation d = 9mm > 1.5 × 5mm = 7.5mm (yarn diameter 5mm) is calculated, indicating a risk of yarn interference. Based on the initial motion matrix, the PID algorithm calculates the yarn carrier's Y-axis displacement correction of -5mm and the robotic arm's elbow joint posture correction of 3°. Combined with the collaborative timing table, the optimal obstacle avoidance path is generated, and the drive device executes according to the corrected path. After 500ms, the yarn coordinates return to the allowable range, and the original command execution resumes, successfully avoiding the interference risk.
[0051] Step S5: Real-time acquisition of the actual tension value of the yarn and the local curvature value of the skeleton core mold, verification and comparison, and then obtaining dynamic optimization instructions; It should be noted that by collecting the actual values of yarn tension and local curvature of the skeleton core mold in real time, comparing them with standard values, and generating dynamic optimization instructions, the forming quality and control precision of the soft tissue weaving of the bionic robot can be improved.
[0052] In this embodiment, the actual yarn tension value and the local curvature value of the skeleton core mold are the core process parameters reflecting the state of the weaving process. Their deviation from the standard value directly affects the mechanical properties of the weaving structure. The dynamic optimization instruction is a standardized adjustment instruction generated based on the parameter deviation, which includes tension adjustment coefficient and curvature compensation parameter.
[0053] In this embodiment, the process of acquiring and verifying the actual yarn tension value and the local curvature value of the skeleton core mold in real time to obtain dynamic optimization instructions includes: Step S500: Deploy high-precision tension sensors along the yarn feeding path and curvature sensors in key biomimetic areas of the skeleton core mold. Establish real-time data interaction between the sensors and the execution control system, and collect the actual tension value of the yarn during the weaving process in real time at a frequency synchronized with the acquisition of the yarn's spatial position. and the local curvature value of the skeleton core mold .
[0054] Step S501: Based on the preset yarn tension threshold range Standard value of local curvature of skeleton core mold A parameter verification benchmark system is constructed; the parameter verification benchmark system includes: 1. Determination of abnormal tension: If so, it is marked as tension abnormality; 2. Judgment of curvature deviation anomalies: ,like , If the curvature deviation is within the allowable range, it is marked as a curvature anomaly.
[0055] Step S502: Based on the determination results of tension anomaly and curvature anomaly, generate corresponding dynamic optimization instructions; The formula for calculating the tension adjustment coefficient is as follows: ; The formula for calculating curvature compensation parameters is as follows: ; in, This is the tension adjustment coefficient; Adjust the gain for tension; This is the theoretical value of tension; For curvature compensation parameters; For curvature compensation gain; It should be noted that if only tension anomaly exists, the instruction includes a targeted tension adjustment coefficient; if only curvature anomaly exists, the instruction includes a targeted curvature compensation parameter; if both types of anomalies exist simultaneously, the instruction includes both tension adjustment coefficient and curvature compensation parameter. The values of the coefficient and parameter are quantified according to the degree of anomaly. The higher the degree of anomaly, the greater the adjustment / compensation range, thus completing the generation of dynamic optimization instructions.
[0056] Step S6: Based on the correlation algorithm between yarn crimp and weaving angle and dynamic optimization instructions, optimize and coordinate the tension of the yarn carrier and the traction speed of the robotic arm to accurately control the tension of the yarn carrier and the traction speed of the robotic arm, so as to ensure that the soft tissue weaving of the bionic robot meets the design requirements.
[0057] It should be noted that by combining the correlation algorithm between yarn crimp and weaving angle with dynamic optimization instructions, real-time and precise closed-loop control of yarn carrier tension and robotic arm traction speed is achieved, ensuring that the actual weaving effect continuously meets the design requirements.
[0058] In this embodiment, the correlation algorithm between yarn crimp and knitting angle is the core theoretical basis for optimizing tension and traction speed. Through the nonlinear correlation model between the two, precise control of knitting process parameters can be achieved.
[0059] In this embodiment, the process of optimizing and coordinating the control of yarn carrier tension and robotic arm traction speed based on the correlation algorithm between yarn crimp and knitting angle and dynamic optimization instructions includes: Step S600: Calibrate the yarn crimp reference values at different weaving angles. The calibration range of the weaving angle covers the initial weaving angles of each region of the bionic robot, forming a weaving angle-creep reference dataset; yarn crimp definition formula: ;in, Yarn crimp; This refers to the actual weaving length of the yarn; Yarn straightening length; Furthermore, using the knitting angle as the independent variable and yarn crimp as the dependent variable, and combining the yarn's mechanical parameters for correction, a nonlinear correlation equation between yarn crimp and knitting angle was derived through data fitting, and yarn tension was introduced for correction. ;in, , , These are the fitting coefficients; This is the tension correction factor; This represents the maximum breaking tension of the yarn. Yarn tension; For weaving corners.
[0060] Step S601: Based on the tension adjustment coefficient in the dynamic optimization command curvature compensation parameters And the nonlinear correlation equation between yarn crimp and knitting angle is used to obtain the target knitting angle under the current knitting state. and target curl The process includes: Step S610: Adjust according to the tension coefficient Calculate the target tension: ; Step S611: Based on curvature compensation parameters Calculate the target curvature, and then obtain the target weave angle through the mapping relationship between curvature and weave angle: ;in, The function represents the mapping relationship between curvature and weaving angle; Step S612: Target weaving angle With target tension Substituting the nonlinear relationship between yarn crimp and knitting angle into the equation, we obtain the target crimp: .
[0061] Step S602: According to the target weaving angle Compared with the current actual knitting angle The difference By coupling kinematics and mechanics calculations, the adjustment amount of yarn carrier tension is obtained. Adjustment amount of the robotic arm traction speed ;in, This represents the current traction speed of the robotic arm. The adjustment amount for the yarn carrier tension will be calculated. Adjustment amount of the robotic arm traction speed Converted into standardized control parameters that the device can recognize.
[0062] Step S603: The standardized control parameters are substituted into the PID closed-loop control algorithm to convert the standardized control parameters into control signals, thereby adjusting the tension output of the yarn carrier and the traction speed of the robotic arm in real time. The core formula of the PID closed-loop control algorithm is as follows: ;in, To control the output; This is the proportionality coefficient; The integral coefficient; These are the differential coefficients; This represents the deviation between the actual curl and the target curl.
[0063] It should be noted that the yarn crimp is gradually brought closer to the target crimp through real-time adjustments; closed-loop convergence condition: After the tension and traction speed are adjusted, the sensor continues to collect new actual tension values and local curvature values in real time, and compares them with the standard values again. If there are still abnormalities, the above steps are repeated to recalculate the adjustment amount and make real-time adjustments. If there are no abnormalities, the current tension and traction speed are maintained until the weaving operation of the entire bionic robot soft tissue is completed, ensuring that the weaving result meets the design requirements.
[0064] Optionally, in this embodiment, those skilled in the art will understand that all or part of the steps in the various methods of the above embodiments can be implemented by a program instructing the hardware related to the terminal device. The program can be stored in a computer-readable storage medium, which may include: flash drive, read-only memory (ROM), random access memory (RAM), disk or optical disk, etc.
[0065] The sequence numbers of the embodiments in this application are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.
[0066] If the integrated units in the above embodiments are implemented as software functional units and sold or used as independent products, they can be stored in the aforementioned computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause one or more electronic devices to execute all or part of the steps of the methods described in the various embodiments of this application.
[0067] In the above embodiments of this application, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions of other embodiments.
[0068] In the several embodiments provided in this application, it should be understood that the disclosed application can be implemented in other ways. The device embodiments described above are merely illustrative; for example, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection of units or modules may be electrical or other forms.
[0069] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.
[0070] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.
[0071] The above description is only a preferred embodiment of this application. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of this application, and these improvements and modifications should also be considered within the scope of protection of this application.
Claims
1. A three-dimensional weaving optimization cooperative control method applied to biomimetic robots, characterized in that, The method includes: The standard bionic robot's 3D model is divided into regions, and then the basic weaving parameters for each region are set. Construct a standard stress simulation model; input the basic weaving parameters of the bionic robot to be woven and the mechanical parameters of the yarn into the standard stress simulation model in real time, and then obtain the initial yarn carrier motion matrix, the initial robotic arm spatial trajectory matrix and the multi-module collaborative timing table; Based on the initial yarn carrier motion matrix, the robotic arm spatial trajectory matrix, and the multi-module collaborative timing table, the trajectory fusion calculation of the chassis planar motion of the yarn carrier and the spatial posture motion of the robotic arm is performed to generate collaborative motion commands. Real-time acquisition of the spatial position of the yarn during the weaving process of executing cooperative motion commands; if yarn interference or bridging risk is detected, slight corrections are made to the motion trajectory of the yarn carrier and the posture of the robotic arm to generate the optimal obstacle avoidance path. The actual tension value of the yarn and the local curvature value of the skeleton core mold are collected in real time, and the data are verified and compared to obtain dynamic optimization instructions. Based on the correlation algorithm between yarn crimp and weaving angle and dynamic optimization instructions, the tension of the yarn carrier and the traction speed of the robotic arm are optimized and coordinated for control, thereby precisely controlling the tension of the yarn carrier and the traction speed of the robotic arm to ensure that the soft tissue weaving of the bionic robot meets the design requirements.
2. The three-dimensional weaving optimization and cooperative control method for biomimetic robots according to claim 1, characterized in that, The process of dividing a standard biomimetic robot's 3D model into regions includes: The standard biomimetic robot's 3D model is divided into regions based on differences in mechanical function, including joint areas and skeletal areas. The connecting parts are divided into transitional connection areas and marked as such. Based on the biomimetic biomechanical requirements of each region, basic weaving parameters are set for the corresponding regions. These basic weaving parameters include the initial weaving angle, yarn tension threshold, layer thickness, functional device embedding position, and online curing power.
3. The three-dimensional weaving optimization and collaborative control method for biomimetic robots according to claim 2, characterized in that, The process of constructing a standard stress simulation model includes: Using the marked area corresponding to the standard bionic robot as the spatial reference, the basic weaving parameters and the mechanical parameters of the yarn in each area are obtained. Based on a 3D modeling and simulation platform, a standard stress simulation model framework is built; the overall framework includes a yarn carrier unit, a robotic arm motion unit, a yarn stress unit, and a collaborative control unit. Using preset standard weaving scene parameters, the yarn carrier unit, robotic arm motion unit, yarn force unit, and collaborative control unit are calibrated under no-load and load conditions. The results of the no-load and load calibrations are then evaluated. If the results meet the requirements, the model construction is complete; otherwise, the correlation parameters between modules are adjusted for further evaluation.
4. The three-dimensional weaving optimization and cooperative control method for biomimetic robots according to claim 3, characterized in that, The process of trajectory fusion calculation for the planar motion of the yarn carrier chassis and the spatial attitude motion of the robotic arm includes: Extract the chassis planar motion parameters and the robotic arm spatial trajectory matrix from the initial yarn carrier motion matrix; A trajectory fusion coordinate system is established using a multi-module collaborative timing table as the time reference; the Kalman filter algorithm is used to denoise the chassis planar motion parameters and spatial attitude parameters. Based on the forward kinematics principle, the planar motion of the chassis and the spatial posture motion of the robotic arm are coupled and solved to construct a fused motion equation. The cooperative motion parameters of the yarn carrier and the robotic arm are obtained by solving the equation. According to the cooperative motion parameters, corresponding cooperative motion commands are generated, including the movement speed of the yarn carrier, the traction speed of the robotic arm, the posture adjustment angle, and the action execution sequence.
5. The three-dimensional weaving optimization and cooperative control method for biomimetic robots according to claim 4, characterized in that, The process of making minor corrections to the motion trajectory of the yarn carrier and the posture of the robotic arm includes: The spatial coordinate data of the yarn during the weaving process is collected in real time by visual and position sensors to execute coordinated motion commands. The spatial coordinate data is compared and analyzed to determine whether there is a risk of yarn interference and bridging. If there are risks of yarn interference and bridging, the displacement correction of the yarn carrier and the attitude correction of the robotic arm are calculated based on the initial motion matrix of the yarn carrier and the initial spatial trajectory matrix of the robotic arm, using the obstacle avoidance optimization algorithm as a benchmark. If it does not exist, then no minor correction is needed; Based on the multi-module collaborative timing table, displacement correction amount, and attitude correction amount, an optimal obstacle avoidance path is generated, driving the yarn carrier and robotic arm to perform weaving actions according to the corrected optimal obstacle avoidance path.
6. The three-dimensional weaving optimization and cooperative control method for biomimetic robots according to claim 5, characterized in that, The process of verifying and comparing the real-time acquired actual tension values and local curvature values includes: Real-time acquisition of actual tension and local curvature values during yarn weaving process; Preset yarn tension threshold range and standard value of local curvature of skeleton core mold; The actual tension value collected is compared with a preset threshold range. If the actual tension value exceeds the threshold range, it is marked as an abnormal tension. The collected local curvature values are compared with the standard values to calculate the curvature deviation value. If the deviation value exceeds the preset allowable range, it is marked as curvature abnormality. Based on the tension anomaly and curvature anomaly, a corresponding dynamic optimization instruction is generated; the dynamic optimization instruction includes a tension adjustment coefficient and a curvature compensation parameter.
7. The three-dimensional weaving optimization and cooperative control method for biomimetic robots according to claim 6, characterized in that, The process of optimizing and coordinating the tension of the yarn carrier and the traction speed of the robotic arm based on the correlation algorithm between yarn crimp and knitting angle and dynamic optimization instructions includes: Based on experimental calibration of yarn crimp benchmark values under different knitting angles, a nonlinear correlation equation between yarn crimp and knitting angle is derived. The correlation equation has knitting angle as independent variable and yarn crimp as dependent variable, and is corrected according to the mechanical parameters of the yarn. Substitute the tension adjustment coefficient and curvature compensation parameter from the dynamic optimization command into the correlation equation to obtain the target weaving angle and target crimp; based on the target weaving angle, calculate the adjustment amount of the yarn carrier tension and the adjustment amount of the robotic arm traction speed; Through a closed-loop control algorithm, the tension output of the yarn carrier and the traction speed of the robotic arm are adjusted in real time to make the yarn crimp conform to the target crimp. The system provides real-time feedback on the actual tension value and local curvature value after adjustment, performs verification and comparison again, and then calculates the adjustment amount of the yarn carrier tension and the adjustment amount of the robotic arm traction speed in real time.
8. A three-dimensional weaving optimization and cooperative control system for biomimetic robots, implementing the three-dimensional weaving optimization and cooperative control method for biomimetic robots as described in any one of claims 1 to 7, characterized in that, It includes a model partitioning parameter configuration module, a simulation modeling initialization module, a trajectory fusion module, an obstacle avoidance trajectory correction module, a parameter verification and dynamic optimization module, and a precise collaborative control module; The model partitioning parameter configuration module is used to divide the three-dimensional model of the bionic robot into regions and then set the basic weaving parameters for each region. The simulation modeling initialization module is used to construct a real-time stress simulation model. The basic weaving parameters and the mechanical parameters of the yarn are input into the real-time stress simulation model to obtain the initial yarn carrier motion matrix, the robotic arm spatial trajectory matrix, and the multi-module collaborative timing table. The trajectory fusion module performs trajectory fusion calculation on the chassis planar motion of the yarn carrier and the spatial attitude motion of the robotic arm based on the initialized yarn carrier motion matrix, the robotic arm spatial trajectory matrix and the multi-module collaborative timing table, and generates collaborative motion commands. The obstacle avoidance trajectory correction module is used to collect the spatial position of the yarn in real time during the weaving process of executing cooperative motion commands. If yarn interference and bridging risks are detected, the module makes slight corrections to the motion trajectory of the yarn carrier and the posture of the robotic arm to generate the optimal obstacle avoidance path. The parameter verification and dynamic optimization module is used to collect the actual tension value of the yarn and the local curvature value of the skeleton core mold in real time, and perform verification and comparison to obtain dynamic optimization instructions. The precision collaborative control module, based on the correlation algorithm between yarn crimp and weaving angle and dynamic optimization instructions, optimizes and coordinates the tension of the yarn carrier and the traction speed of the robotic arm, thereby precisely controlling the tension of the yarn carrier and the traction speed of the robotic arm to ensure that the soft tissue weaving of the bionic robot meets the design requirements.