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CN-122008163-A - Real-time synchronous optimization method and system for multi-axis cooperative data controlled by robot

CN122008163ACN 122008163 ACN122008163 ACN 122008163ACN-122008163-A

Abstract

The invention provides a real-time synchronous optimization method and a system for multi-axis collaborative data controlled by a robot, and relates to the technical field of robots, wherein the method comprises the steps of establishing a collaborative motion control network based on a unified global clock source, synchronously acquiring pose, moment and process data of each axis of the robot, and adding a global time stamp to obtain a data set with the time stamp; and based on a preset extended kinematic model containing an external axis, carrying out fusion calculation on the space pose data in the multi-axis synchronous original data stream, and carrying out real-time filtering on the moment and the process data to obtain a collaborative state data set containing an expected state. According to the invention, through a full-link cooperative control method from data acquisition, processing and transmission to optimization, the motion precision, the self-adaptability and the reliability of the multi-axis robot are improved.

Inventors

  • XU XIWEN
  • LIU MENG

Assignees

  • 清川(南通)智能科技有限公司

Dates

Publication Date
20260512
Application Date
20260327

Claims (9)

  1. 1. The robot control multi-axis cooperative data real-time synchronous optimization method is characterized by comprising the following steps of: Step 100, establishing a cooperative motion control network based on a unified global clock source, synchronously acquiring pose, moment and process data of each axis of a robot, and adding a global time stamp to obtain a data set with a time stamp; Step 200, based on a preset extended kinematic model containing an external shaft, carrying out fusion calculation on space pose data in a multi-axis synchronous original data stream, and carrying out real-time filtering on moment and process data to obtain a collaborative state data set containing an expected state; Step 300, synchronously resolving control parameters of each axis based on a collaborative state data set, extracting key point load and pose information to obtain a preliminary control parameter set, carrying out spatial domain load distribution analysis by combining the key point load and the pose information to obtain load uniformity characteristics, obtaining load self-adaptive feedforward gain based on the load uniformity characteristics, and fusing the preliminary control parameter set and the load self-adaptive feedforward gain to obtain a self-adaptive multi-axis collaborative motion instruction set with a sequence identifier; Step 400, issuing a self-adaptive multi-axis cooperative motion instruction set to each axis driver through a main and standby redundant communication link, monitoring the link state and switching the link when abnormal, and executing breakpoint continuous transmission according to the sequence identification to realize reliable issuing of the instruction set; And 500, acquiring actual operation feedback data based on a reliably issued instruction set, comparing the actual operation feedback data with an expected state, calculating synchronous deviation and response delay, performing monitoring alarm, and taking the synchronous deviation and the response delay as the next period input to drive a new round of collaborative processing flow.
  2. 2. The method for real-time synchronous optimization of robot-controlled multi-axis collaborative data according to claim 1, wherein step 100 comprises: constructing a unified global clock source, establishing a cooperative motion control network based on the global clock source, and configuring a synchronous clock reference and a unique shaft identifier for a sensor data channel corresponding to each shaft of a robot in the cooperative motion control network; synchronously acquiring real-time pose, moment and welding process data of each joint shaft, an external track walking shaft and a servo positioner shaft of the robot through the cooperative motion control network to obtain a multi-axis original data set; Based on the global clock source, adding a global time stamp to the multi-axis original data set to obtain a data set with the time stamp; uploading the data set with the time stamp to a data receiving area of the robot control cabinet in real time through the cooperative motion control network; receiving data sets with time stamps in the data receiving area, and performing multi-source time sequence alignment processing according to global time stamps carried by each data set to obtain a time-synchronous data sequence; and integrating the time-synchronous data sequences according to a time axis to obtain a time-space unified multi-axis synchronous original data stream.
  3. 3. The method of real-time synchronous optimization of robot-controlled multi-axis collaborative data according to claim 2, wherein step 200 includes: receiving the space-time unified multi-axis synchronous original data stream; Based on a preset extended kinematic model containing an external axis, performing kinematic positive solution fusion calculation on the spatial pose data in the multi-axis synchronous original data stream to obtain the accurate pose of the tail end of the robot under a global coordinate system; Based on the accurate pose of the tail end of the robot under the global coordinate system, taking the precise pose as a center to determine a space neighborhood with a preset radius, executing geometric mean filtering on moment data in multi-axis synchronous original data streams falling into the space neighborhood, filtering high-frequency fluctuation caused by pose micro-variation and noise to obtain stable load state parameters; And integrating the precise pose, the stable load state parameter and the stable process parameter set of the robot end effector under the global coordinate system to obtain a collaborative state data set containing the expected state.
  4. 4. The method of real-time synchronization optimization of robot-controlled multi-axis collaborative data according to claim 3, comprising: receiving the collaborative state data set, calculating Cartesian space difference values of actual and target pose of each axis, and converting the Cartesian space difference values into track tracking errors of each axis through an inverse kinematics model of the extended kinematics model; inputting the stable load state parameters into a load moment observer, and calculating to obtain a load dynamic compensation quantity; Inquiring a process calibration mapping table according to the stable process parameter set, and matching to obtain a process parameter matching value; finally, the track tracking error, the load dynamic compensation quantity and the process parameter matching value of each axis are packaged into a unified data structure, and a primary control parameter set is generated in an integrated mode; Extracting real-time load and pose information of a plurality of pre-calibrated key position points in a robot working space from the collaborative state data set to obtain key point load-pose data pairs; Based on load data in the key point load-pose data pair, constructing a spatial distribution vector of a load value, carrying out statistical analysis on the spatial distribution vector of the load value, and calculating to obtain a statistical variance and a statistical kurtosis; Based on a preset uniformity evaluation rule, the statistical variance and the statistical kurtosis are used as input, a composite index which comprehensively characterizes the uniformity degree of spatial load distribution is obtained by calculating a weighted fusion value of the statistical variance and the statistical kurtosis under normalized calibration and is used as a load uniformity characteristic, and meanwhile, the value of the statistical variance is recorded, and a load mutation area is identified according to a spatial section of which the statistical kurtosis exceeds a preset kurtosis threshold value.
  5. 5. The method of real-time synchronization optimization of robot-controlled multi-axis collaborative data according to claim 4, further comprising: Generating a load self-adaptive feedforward gain according to a preset load-gain mapping rule based on the load uniformity characteristic, wherein a track feedforward gain for a smooth speed curve is generated for a non-uniform area with a statistical variance value exceeding a preset variance threshold value; Superposing and fusing the preliminary control parameter set, the track feedforward gain and the moment compensation gain to obtain optimized control instructions of each axis; And (3) adding a unique sequence identifier generated based on the global time stamp and the shaft identifier corresponding to each shaft to each optimized shaft control instruction, and finally integrating the unique sequence identifier into an adaptive multi-shaft cooperative motion instruction set with the sequence identifier.
  6. 6. The method for real-time synchronization optimization of robot-controlled multi-axis collaborative data according to claim 5, wherein step 400 includes: Receiving the self-adaptive multi-axis cooperative motion instruction set, and starting the issuing flow of the self-adaptive multi-axis cooperative motion instruction set to each axis driver through a main redundant communication link to obtain an issuing task instance borne by the main link; Based on the issued task instance borne by the main link, synchronously monitoring real-time communication quality parameters of the main link, wherein the real-time communication quality parameters at least comprise data packet round trip delay and packet loss rate; Based on the real-time communication quality parameters, calling a link state evaluation function to calculate to obtain a link health evaluation value, and judging that the main link communication is abnormal and generating a link switching instruction when the link health evaluation value is lower than a preset health threshold value; And responding to the link switching instruction, stopping the instruction issuing through the main link, and switching the communication path to the standby redundant communication link to obtain a link switching completion state.
  7. 7. The method of real-time synchronization optimization of robot-controlled multi-axis collaborative data according to claim 6, further comprising step 400: Receiving a link switching completion state, and inquiring an instruction packet response log which is recorded before the interruption of a main link and fed back by each axis driver based on a sequence identifier carried in the self-adaptive multi-axis cooperative motion instruction set to obtain the last successfully responded instruction packet identifier; Determining a subsequent instruction packet in the instruction sequence as a continuous transmission starting point based on the identification of the last successfully responded instruction packet; starting an instruction packet continuous transmission issuing task starting from a continuous transmission starting point through a standby redundant communication link based on the continuous transmission starting point; the state of the continuous transmission issuing task is monitored in real time, and the count of successfully issued instruction packets is obtained; And when the instruction packet count reaches the total packet number of the instruction sequence, judging that the transmission of the instruction set is finished, generating a confirmation state that the instruction set is completely issued, and realizing the reliable issuing of the instruction set.
  8. 8. The method for real-time synchronization optimization of robot-controlled multi-axis collaborative data according to claim 7, comprising: Triggering and generating a feedback data acquisition instruction based on the reliably issued instruction set; Responding to and executing the feedback data acquisition instruction, and acquiring real-time position, speed and moment feedback data of each shaft driver after executing the corresponding instruction to obtain an actual operation feedback data set; The actual operation feedback data set is compared with the expected state data set point by point in real time, the instantaneous difference value of each axis in the position, speed and moment dimension is calculated, and the state difference data set is generated in a summarizing way; performing time sequence analysis on the state difference data set, and calculating to obtain a synchronous deviation value and an instruction response delay; comparing the synchronous deviation value and the instruction response delay with preset thresholds respectively, generating corresponding overrun state marks, and generating visual alarm signals and grades according to the overrun state marks; and taking the synchronous deviation value and the instruction response delay as feedforward calibration input of the next control period, and driving and starting a new round of complete cooperative processing flow to realize real-time synchronous optimization of the multi-axis cooperative data.
  9. 9. A robot-controlled multi-axis collaborative data real-time synchronization optimization system implementing the method of any one of claims 1-8, comprising: the acquisition module is used for establishing a cooperative motion control network based on a unified global clock source, synchronously acquiring the pose, moment and process data of each axis of the robot and adding a global time stamp to obtain a data set with the time stamp; The data processing module is used for carrying out fusion calculation on the space pose data in the multi-axis synchronous original data stream based on a preset extended kinematic model comprising an external axis, and carrying out real-time filtering on the moment and the process data to obtain a collaborative state data set comprising an expected state; The control module is used for synchronously resolving control parameters of each axis based on the collaborative state data set and extracting key point load and pose information to obtain a preliminary control parameter set, carrying out spatial domain load distribution analysis by combining the key point load and the pose information to obtain load uniformity characteristics; The switching module is used for transmitting the self-adaptive multi-axis cooperative motion instruction set to each axis driver through the main and standby redundant communication links, monitoring the link state and switching the links when the link is abnormal, and executing breakpoint continuous transmission according to the sequence identification so as to realize reliable transmission of the instruction set; and the closed loop verification module is used for acquiring actual operation feedback data based on a reliably issued instruction set, comparing the actual operation feedback data with an expected state, calculating synchronous deviation and response delay, performing monitoring alarm, and taking the synchronous deviation and the response delay as the next period input to drive a new round of collaborative processing flow.

Description

Real-time synchronous optimization method and system for multi-axis cooperative data controlled by robot Technical Field The invention relates to the technical field of robots, in particular to a real-time synchronous optimization method and system for multi-axis collaborative data controlled by a robot. Background Along with the development of high-end manufacture to intellectualization and flexibility, industrial robot application has evolved from single-machine operation to a multi-axis (joint axis, external track axis, deflection axis and the like) compact collaborative operation mode, in such tasks, real-time synchronization and collaborative control of pose, moment and process data of each axis is a core for guaranteeing operation precision and process quality, and a main current scheme adopts a centralized control architecture, and a main controller uniformly processes each axis data and issues instructions. However, as the complexity of the system increases, the architecture has drawbacks at the data processing level, which restricts further improvement of the collaborative performance. Firstly, in the data analysis and state estimation links, the existing method processes the data of each axis in isolation, lacks a space global view angle, for example, the load filtering parameters are fixed, the real-time correlation analysis is not carried out on the multi-axis pose and the load information, the uneven load distribution and the abrupt change area caused by the geometric and physical characteristics in the working space cannot be dynamically identified, so that the self-adaptive capacity of the control parameters is insufficient, vibration or tracking errors can be caused at key process points, secondly, in the control instruction transmission link, an optimized instruction set depends on a single communication link to be issued, in the complex industrial site, the multi-axis cooperative synchronization relationship can be damaged due to link interruption or data packet loss, and the existing system lacks a high-reliability instruction stream continuity guarantee mechanism, so that the uninterrupted cooperative severity requirement of the continuous process can not be met, and finally, in the feedback data utilization link, the system cannot carry out the closed-loop feedback of depth characteristics such as synchronous deviation, response delay and the like to the data fusion and parameter optimization flow of the next period to enable the system to have no capacity of carrying out the feedforward self-calibration on the basis of the historical performance, and the continuous optimization of the cooperative performance can not be realized. Disclosure of Invention The technical problem to be solved by the invention is to provide the real-time synchronous optimization method and system for the multi-axis cooperative data controlled by the robot, and the motion precision, the self-adaptability and the reliability of the multi-axis robot are improved through a full-link cooperative control method from data acquisition, processing and transmission to optimization. In order to solve the technical problems, the technical scheme of the invention is as follows: in a first aspect, a method for real-time synchronous optimization of multi-axis collaborative data controlled by a robot, the method comprising: Establishing a cooperative motion control network based on a unified global clock source, synchronously acquiring pose, moment and process data of each axis of the robot, and adding a global time stamp to obtain a data set with a time stamp; based on a preset extended kinematic model containing an external shaft, carrying out fusion calculation on space pose data in a multi-axis synchronous original data stream, and carrying out real-time filtering on moment and process data to obtain a collaborative state data set containing an expected state; Based on the collaborative state data set, synchronously resolving control parameters of each axis and extracting key point load and pose information to obtain a preliminary control parameter set, combining the key point load and pose information to carry out spatial domain load distribution analysis to obtain load uniformity characteristics, obtaining load self-adaptive feedforward gain based on the load uniformity characteristics, and fusing the preliminary control parameter set and the load self-adaptive feedforward gain to obtain a self-adaptive multi-axis collaborative motion instruction set with a sequence identifier; The self-adaptive multi-axis cooperative motion instruction set is issued to each axis driver through the main and standby redundant communication links, the link state is monitored, the links are switched when the link state is abnormal, breakpoint continuous transmission is executed according to the sequence identification, and reliable issuing of the instruction set is realized; Based on the reliably issued instruction set, the actual operati