Industrial robot scheduling method and system based on multi-agent cooperation

Abstract

The application discloses a multi-Agent cooperation-based industrial robot scheduling method and system, relates to coordination scheduling technology, and is used for solving the problems that high-precision dense operation workstations are long-term concentrated on the same batch of robots, invalid energy consumption is continuously accumulated, and a servo system is accelerated in aging, and the method comprises the following steps: setting an energy efficiency evaluation time detection component to generate an energy efficiency braking feature of a servo drive voltage of a robot, calling an operation scheduling level, comprehensively screening dense operation workstations and marking the robot, analyzing joint travel and trajectory correction pulse to generate an energy efficiency imbalance state, judging whether to perform scheduling change based on the energy efficiency imbalance state, obtaining an operation trajectory correction reference and a motion path rearrangement record to generate a path stability index when scheduling change is performed, re-screening operation workstations and performing scheduling change on the robot after adjusting the operation scheduling level, so that long-term invalid energy consumption accumulation caused by high-precision dense operation is avoided, and scheduling adaptive optimization based on the energy efficiency state is realized.

Description

Technical Field

[0001] This invention relates to the field of coordination and scheduling technology, and more specifically, to a method and system for scheduling industrial robots based on multi-agent collaboration. Background Technology

[0002] With the development of intelligent manufacturing and the industrial internet, multi-agent collaborative scheduling has become a key technology in industrial robot production lines. In scenarios such as automotive parts assembly, precision assembly of 3C products and equipment manufacturing, the collaborative work of perception agents, scheduling agents, execution agents and energy efficiency management agents is usually used to uniformly schedule the parts assembly robots in order to achieve work cycle optimization, workstation load balancing and path conflict avoidance.

[0003] The existing technology has the following shortcomings:

[0004] Currently, existing technologies mainly rely on work cycle time, workstation priority, and load balancing for scheduling decisions. They lack a mechanism for perceiving and modeling the energy efficiency status and long-term operational degradation characteristics of robot servo drives. This makes it difficult to achieve adaptive optimization of the scheduling relationship between workstations and robots based on energy efficiency status. Consequently, high-precision, densely scheduled workstations are often concentrated in the same batch of robots, leading to continuous accumulation of ineffective energy consumption and accelerated aging of the servo system. Therefore, this paper proposes an industrial robot scheduling method and system based on multi-agent collaboration.

[0005] The information disclosed in the background section is only intended to enhance the understanding of the background of this disclosure, and therefore may include information that does not constitute prior art known to those skilled in the art. Summary of the Invention

[0006] To overcome the aforementioned deficiencies of the prior art, embodiments of the present invention provide an industrial robot scheduling method and system based on multi-agent collaboration. By employing energy efficiency assessment and energy efficiency imbalance state discrimination mechanisms, servo drive energy efficiency perception, joint travel, and trajectory correction behavior analysis are introduced into multi-agent scheduling decisions. Furthermore, a collaborative optimization mechanism based on path stability index and work trajectory correction benchmark is implemented to achieve adaptive adjustment of scheduling levels, thereby solving the problems mentioned in the background art.

[0007] To achieve the above objectives, the present invention provides the following technical solution: an industrial robot scheduling method based on multi-agent collaboration, comprising the following steps:

[0008] Step S1: Set the energy efficiency assessment time, detect the servo drive voltage data of the component assembly robot on the work station to be tested within the energy efficiency assessment time and generate energy efficiency braking characteristics, and retrieve the work scheduling level of the work station to be tested.

[0009] Step S2: Select densely tuned workstations and mark component assembly robots by comprehensively considering the job scheduling level and energy efficiency braking characteristics. Detect the joint stroke data of the marked component assembly robots and analyze the energy consumption degradation trend. Count the number of trajectory correction pulses of the marked component assembly robots. Combine the energy consumption degradation trend to generate an energy efficiency imbalance state.

[0010] Step S3: Use the energy efficiency imbalance state to determine whether to make scheduling changes to the marking component assembly robot. When making scheduling changes, access the historical database to obtain the work trajectory correction benchmark of the dense adjustment work station, and collect the motion path rearrangement record information of the marking robot.

[0011] Step S4: Generate a path stability index based on motion path rearrangement record information, adjust the job scheduling level in conjunction with the job trajectory correction benchmark, select and mark the job stations to be tested, and make scheduling changes to the component assembly robot based on the marked job stations to be tested.

[0012] In a preferred embodiment, in step S1, the servo drive voltage data of the component assembly robot at the work station to be tested includes the servo drive voltage.

[0013] The energy efficiency assessment time is set by the workstation monitoring agent and divided into multiple collection times. The servo drive voltage is obtained by the robot servo drive detection unit and integrated into a servo drive voltage set according to the collection order.

[0014] The average value of the servo drive voltage set is calculated to obtain the servo drive voltage reference value;

[0015] Traverse the set of servo drive voltages, calculate the difference between each servo drive voltage and the servo drive voltage reference value, and obtain the voltage deviation set.

[0016] The average value of the voltage deviation set is calculated and the absolute value is taken as the energy efficiency braking characteristic.

[0017] Match the job station number of the job station to be tested with the job scheduling management database to obtain the job scheduling level of the job station to be tested.

[0018] In a preferred embodiment, in step S2, the job scheduling level of the job station to be tested is matched with the job scheduling level database to obtain the job scheduling level sequence value.

[0019] The preset job scheduling level sequence value and energy efficiency braking characteristics corresponding to the job scheduling level of the work station under test are standardized to obtain the scheduling level factor and braking characteristic factor.

[0020] The density adjustment characteristic value of the workstation under test is calculated by combining the scheduling level factor and the braking characteristic factor.

[0021] If the density feature value of the work station to be tested is greater than or equal to the preset density feature threshold, the work station to be tested is determined to be a density work station, and the component assembly robot corresponding to the density work station is marked.

[0022] Conversely, if the result is not found, the work station to be tested is determined not to be a close-fitting work station.

[0023] In a preferred embodiment, in step S2, the joint stroke data of the marker component assembly robot includes the cumulative joint displacement and the frequency of joint stroke changes;

[0024] Set a preset acquisition cycle and obtain the displacement of each joint of the marked component assembly robot through the joint position encoding detection unit;

[0025] The total displacement of the joints of the robot is obtained by adding up the displacements of the marked parts.

[0026] The number of stroke changes of each joint of the marking component assembly robot is obtained through the servo drive joint feedback device in the servo drive unit;

[0027] The stroke change frequency of each joint of the robot assembling the marked parts is obtained by summing the stroke changes of each joint and dividing by the preset acquisition period.

[0028] The cumulative joint displacement and the frequency of joint stroke changes are standardized to obtain the displacement factor and frequency factor.

[0029] The energy consumption degradation trend is calculated by combining displacement factor and frequency factor.

[0030] In a preferred embodiment, in step S2, the trajectory correction control command received by the marker component assembly robot is obtained through the motion control command monitoring unit;

[0031] The trajectory correction control commands received by the marking component assembly robot are analyzed one by one, and the valid trajectory correction commands among the trajectory correction control commands received by the marking component assembly robot are counted as the trajectory correction pulse count of the marking component assembly robot.

[0032] The number of trajectory correction pulses and the energy consumption degradation trend of the marking component assembly robot are standardized to obtain the correction pulse factor and the energy consumption degradation factor.

[0033] The energy efficiency imbalance state is calculated by combining the corrected pulse factor and the energy consumption degradation factor.

[0034] In a preferred embodiment, in step S3, the energy efficiency imbalance state is compared with a preset energy efficiency imbalance threshold for determination:

[0035] If the energy efficiency imbalance is greater than or equal to the preset energy efficiency imbalance threshold, it is determined that the scheduling of the robot assembling the marked parts should be changed.

[0036] Conversely, if the condition is not met, it is determined that no scheduling changes will be made to the robot assembling the marked parts;

[0037] Match the work station number of the intensive survey work station with the historical database to obtain the work trajectory correction benchmark for the intensive survey work station.

[0038] In a preferred embodiment, in step S3, the motion path rearrangement record information of the marked robot includes the number of path rearrangements and the path offset magnitude.

[0039] The number of path rearrangements is obtained through the path control instruction recording unit;

[0040] The actual joint position coordinates of each joint of the marked component assembly robot are obtained through the joint position encoding and detection unit.

[0041] The target position coordinates of each joint of the robot assembling the marked parts are obtained through the trajectory caching unit;

[0042] The Euclidean distance between the actual joint position coordinates and the corresponding target position coordinates of each joint of the marking component assembly robot is calculated to obtain the position deviation of each joint of the marking component assembly robot.

[0043] The path offset magnitude is obtained by summing the positional deviations of each joint of the robot assembling the marked parts.

[0044] In a preferred embodiment, in step S4, the number of path rearrangements and the path offset magnitude are standardized to obtain the rearrangement factor and the offset factor.

[0045] The path stability index is calculated by combining the rearrangement factor and the offset factor.

[0046] The path stability index and the operation trajectory correction benchmark are standardized to obtain the stability factor and correction factor.

[0047] The scheduling level adjustment coefficient is calculated by combining the stability factor and the correction factor.

[0048] Multiply the scheduling level adjustment coefficient by the job scheduling level ordinal value to obtain the adjusted job scheduling level ordinal value;

[0049] The adjusted job scheduling hierarchy position value is matched with the job scheduling hierarchy database to obtain the adjusted job scheduling hierarchy.

[0050] In a preferred embodiment, in step S4, if the adjusted job scheduling level is lower than the current job scheduling level, it is determined that the marked component assembly robot has the risk of insufficient path stability and unbalanced energy efficiency utilization in the original job station, and the job station to be tested is marked.

[0051] Conversely, if the condition is not met, the work station to be tested will not be marked.

[0052] Match the marked workstations to be tested with the job scheduling management database to obtain the component assembly robot that meets the job scheduling level of the marked workstations to be tested;

[0053] The scheduling of the component assembly robot is changed based on the job scheduling level that meets the requirements of the marked workstation.

[0054] The industrial robot scheduling system based on multi-agent collaboration includes an energy efficiency sensing module, an imbalance detection module, a scheduling decision module, and a hierarchical correction module.

[0055] The energy efficiency sensing module is used to set the energy efficiency assessment time, detect the servo drive voltage data of the component assembly robot on the work station under test during the energy efficiency assessment time, generate energy efficiency braking characteristics, and retrieve the work scheduling level of the work station under test.

[0056] The imbalance judgment module is used to screen densely adjusted work stations and mark component assembly robots by comprehensively considering the operation scheduling level and energy efficiency braking characteristics. The component assembly robot is the component assembly robot corresponding to the densely adjusted work station. The module detects the joint stroke data of the marked component assembly robot and analyzes the energy consumption degradation trend. It counts the number of trajectory correction pulses of the marked component assembly robot and generates an energy efficiency imbalance state based on the energy consumption degradation trend.

[0057] The scheduling decision module is used to determine whether to make scheduling changes to the marked component assembly robot based on the energy efficiency imbalance state, access the historical database to obtain the work trajectory correction benchmark of the dense adjustment work station, and collect the motion path rearrangement record information of the marked component assembly robot.

[0058] The hierarchical correction module generates a path stability index based on motion path rearrangement record information, adjusts the job scheduling level in conjunction with the job trajectory correction benchmark, filters and marks the job stations to be tested, and makes scheduling changes to the component assembly robot based on the marked job stations to be tested.

[0059] The technical effects and advantages of this invention are as follows:

[0060] This invention sets an energy efficiency assessment time, during which servo drive voltage data of the component assembly robot at the workstation under test is detected and energy efficiency braking characteristics are generated. Simultaneously, the work scheduling level of the workstation under test is retrieved. The work scheduling level and energy efficiency braking characteristics are combined to select closely tuned workstations and mark the component assembly robot. Joint stroke data is detected and energy consumption degradation trends are analyzed. The number of trajectory correction pulses is counted. An energy efficiency imbalance state is generated based on the energy consumption degradation trend. Based on the energy efficiency imbalance state, it is determined whether to make scheduling changes. When scheduling changes are made, the historical database is accessed to obtain the work trajectory correction benchmark for the closely tuned workstation and motion path rearrangement record information is collected. A path stability index is generated based on the motion path rearrangement record information. The work scheduling level is adjusted in conjunction with the work trajectory correction benchmark, and the workstation under test is selected and marked. Based on this, the scheduling of the component assembly robot is changed. This avoids long-term ineffective energy consumption accumulation in robots caused by high-precision closely tuned operations, and achieves adaptive optimization of the workstation and robot scheduling relationship based on energy efficiency status. Attached Figure Description

[0061] Figure 1 This is a flowchart of the industrial robot scheduling method based on multi-agent collaboration of the present invention.

[0062] Figure 2 This is a schematic diagram of the modules of the industrial robot scheduling system based on multi-agent collaboration of the present invention. Detailed Implementation

[0063] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0064] This invention sets an energy efficiency assessment time, during which servo drive voltage data of the component assembly robot at the workstation under test is detected and energy efficiency braking characteristics are generated. Simultaneously, the work scheduling level of the workstation under test is retrieved. The work scheduling level and energy efficiency braking characteristics are combined to screen closely tuned workstations and mark the component assembly robot. Its joint stroke data is detected and energy consumption degradation trend is analyzed. The number of trajectory correction pulses is counted. Combined with the energy consumption degradation trend, an energy efficiency imbalance state is generated. Based on the energy efficiency imbalance state, it is determined whether to make a scheduling change. When a scheduling change is made, the historical database is accessed to obtain the work trajectory correction benchmark of the closely tuned workstation and motion path rearrangement record information is collected. Based on the motion path rearrangement record information, a path stability index is generated. After adjusting the work scheduling level in combination with the work trajectory correction benchmark, the workstation under test is screened and marked, and the scheduling of the component assembly robot is changed accordingly.

[0065] Example 1, such as Figures 1 to 2 As shown, the industrial robot scheduling method based on multi-agent collaboration includes the following steps:

[0066] Step S1: Set the energy efficiency assessment time, detect the servo drive voltage data of the component assembly robot on the work station to be tested within the energy efficiency assessment time and generate energy efficiency braking characteristics, and retrieve the work scheduling level of the work station to be tested.

[0067] Step S2: Select densely tuned workstations and mark component assembly robots (component assembly robots corresponding to densely tuned workstations) by combining the comprehensive operation scheduling level and energy efficiency braking characteristics. Detect the joint stroke data of the marked component assembly robots and analyze the energy consumption degradation trend. Count the number of trajectory correction pulses of the marked component assembly robots. Combine the energy consumption degradation trend to generate an energy efficiency imbalance state.

[0068] Step S3: Use the energy efficiency imbalance state to determine whether to make scheduling changes to the marking component assembly robot. When making scheduling changes, access the historical database to obtain the work trajectory correction benchmark of the dense adjustment work station, and collect the motion path rearrangement record information of the marking robot.

[0069] Step S4: Generate a path stability index based on motion path rearrangement record information, adjust the job scheduling level in conjunction with the job trajectory correction benchmark, select and mark the job stations to be tested, and make scheduling changes to the component assembly robot based on the marked job stations to be tested.

[0070] The specific implementation is as follows:

[0071] In step S1, the servo drive voltage data of the component assembly robot at the work station to be tested refers to the dynamic voltage feedback data, including servo drive voltage, which is output and recorded in real time by the servo drive control unit of the component assembly robot during the robot's execution of the assembly task at the corresponding work station.

[0072] The energy efficiency assessment time is set by the workstation monitoring agent and divided into multiple collection times. The servo drive voltage is obtained by the robot servo drive detection unit and integrated into a servo drive voltage set according to the collection order.

[0073] The average value of the servo drive voltage set is calculated to obtain the servo drive voltage reference value;

[0074] Traverse the set of servo drive voltages, calculate the difference between each servo drive voltage and the servo drive voltage reference value, and obtain the voltage deviation set.

[0075] The average value of the voltage deviation set is calculated and the absolute value is taken as the energy efficiency braking characteristic.

[0076] Match the job station number of the job station to be tested with the job scheduling management database to obtain the job scheduling level of the job station to be tested.

[0077] It needs to be explained that the workstation monitoring agent is an intelligent software agent in a multi-agent system, specifically responsible for monitoring the status and preprocessing data of one or more workstations. One agent is deployed for each workstation, responsible for collecting energy efficiency data and querying the scheduling level. The energy efficiency assessment time is set according to the industrial robot's work cycle, system scheduling response requirements, and the effective sampling window of servo drive data. The robot servo drive detection unit is an operating status acquisition unit set in the robot control system, used to obtain servo drive voltage. The workstation number refers to the unique identifier assigned to each specific physical workstation on the production line that can perform assembly, processing, or inspection operations. The job scheduling management database is a structured data system that stores and manages the scheduling priority, task queue, resource allocation status, and related configuration parameters of each workstation in the industrial production line, used to obtain the job scheduling level of the workstation under test. The energy efficiency braking characteristics reflect the additional voltage output level required by the servo drive during braking adjustment, fine-tuning control, and disturbance suppression. The job scheduling level is used to characterize the scheduling control level and resource response strategy level of the workstation in the overall production scheduling system. The lower the load of the workstation under test, the higher the job scheduling level.

[0078] By using a workstation monitoring agent, parallel data collection and energy efficiency feature extraction are achieved across multiple workstations. This establishes a dynamic correlation between workstation scheduling levels and real-time energy efficiency status, providing a fusion decision-making basis that combines static priority and dynamic energy efficiency features for subsequent collaborative scheduling. It also supports early perception and accurate location of energy efficiency fluctuations on the production line.

[0079] In step S2, in a multi-robot collaborative assembly system, due to differences in task complexity, equipment status, and scheduling strategies, various workstations often experience problems such as uneven local load and significant energy efficiency fluctuations, leading to a decrease in overall production efficiency and resource utilization. To accurately identify workstations with low actual load but requiring stable movements and frequent fine-tuning and to implement targeted scheduling, after obtaining the basic status of the workstations, it is necessary to further conduct a comprehensive evaluation by combining scheduling levels and energy efficiency characteristics, select the workstations that require key monitoring and dynamic adjustment, and conduct in-depth analysis of the operating status of their corresponding robots to quantify their energy consumption degradation trend and trajectory control stability, providing a reliable basis for subsequent scheduling decisions.

[0080] The scheduling and coordination agent matches the job scheduling level of the job to be tested with the job scheduling level database to obtain the job scheduling level ordinal value;

[0081] The preset job scheduling level sequence value and energy efficiency braking characteristics corresponding to the job scheduling level of the work station under test are standardized to obtain the scheduling level factor and braking characteristic factor.

[0082] The density adjustment characteristic value of the workstation under test is calculated by combining the scheduling level factor and the braking characteristic factor. The calculation formula is as follows:

[0083] ,in, As a scheduling level factor, As a braking characteristic factor, The value represents the close-tuning characteristic value of the workstation to be tested;

[0084] The intensive adjustment characteristic value of the workstation under test reflects the overall urgency of the workstation's adjustment in the scheduling system; the larger the intensive adjustment characteristic value, the more likely the workstation has both a high scheduling priority and significant energy efficiency fluctuations, requiring it to enter the intensive monitoring and dynamic scheduling queue; the smaller the intensive adjustment characteristic value, the more likely the workstation's scheduling demand is stable or its energy efficiency status is stable, allowing the current scheduling strategy to be maintained.

[0085] The scheduling and coordination agent compares the density feature value of the workstation to be tested with the preset density feature threshold to make a judgment:

[0086] If the density adjustment feature value of the work station to be tested is greater than or equal to the preset density adjustment feature threshold, the scheduling and coordination agent determines that the work station to be tested is a density adjustment work station and marks the component assembly robot corresponding to the density adjustment work station.

[0087] If the density adjustment characteristic value of the work station to be tested is less than the preset density adjustment characteristic threshold, the scheduling coordination agent determines that the work station to be tested is not a density adjustment work station.

[0088] It should be noted that the scheduling and coordination agent is the central coordinating and decision-making intelligent body in the multi-agent collaborative scheduling system, responsible for global information integration, core algorithm execution, and final scheduling decision; the job scheduling hierarchy database refers to a structured data set that stores the scheduling priority values ​​of each job station in the production line, used to obtain the job scheduling hierarchy values; the standardization processing methods include, but are not limited to, standard linear transformation based on interval scaling, Z-Score standardization based on statistics, or normalization based on nonlinear mapping functions. The application methods of standardization processing will not be elaborated here; the preset dense adjustment feature threshold is set according to the production line scheduling sensitivity, the robot operation stability tolerance range, and historical energy efficiency fluctuation data; the densely adjusted job station refers to the job station identified in the scheduling process as needing close monitoring and frequent adjustment. Such job stations usually exhibit high scheduling urgency and obvious energy efficiency instability characteristics.

[0089] The joint stroke data of the component assembly robot refers to the set of stroke parameter data of the actual cumulative movement of each joint during the execution of the task, including the cumulative displacement of the joint and the frequency of joint stroke changes;

[0090] The robot body Agent is set with a preset collection cycle, and the displacement of each joint of the robot assembly component is obtained through the joint position encoding detection unit;

[0091] The total displacement of the joints of the robot is obtained by adding up the displacements of the marked parts.

[0092] The number of stroke changes of each joint of the marking component assembly robot is obtained by setting the servo drive joint feedback device in the servo drive unit corresponding to each joint.

[0093] The stroke change frequency of each joint of the robot assembling the marked parts is obtained by summing the stroke changes of each joint and dividing by the preset acquisition period.

[0094] The cumulative joint displacement and the frequency of joint stroke changes are standardized to obtain the displacement factor and frequency factor.

[0095] The energy consumption degradation trend is calculated by combining the displacement factor and the frequency factor. The calculation formula is as follows: ,in, It is the displacement factor. For frequency factor, This reflects a trend of energy consumption degradation.

[0096] The energy consumption degradation trend reflects the rate of energy efficiency decay of industrial robots within a preset collection period and their relative energy consumption level in similar tasks. The larger the energy consumption degradation trend value, the higher the cumulative load and frequency of the robot's joint movements, and the more significant the degradation of its energy efficiency in similar tasks. The smaller the energy consumption degradation trend value, the more stable the robot's motion load or the better its energy efficiency, and the more stable its energy consumption.

[0097] The motion control command monitoring unit obtains the trajectory correction control commands received by the marked component assembly robot.

[0098] The trajectory correction control commands received by the robot assembling the marked parts are analyzed one by one;

[0099] The specific analysis process is as follows:

[0100] Read the joint target position correction field from the trajectory correction control command;

[0101] When a non-zero correction value is detected in the joint target position correction field, the corresponding trajectory correction control command is determined to be a valid trajectory correction command.

[0102] The number of valid trajectory correction commands received by the trajectory correction control commands of the component assembly robot is taken as the number of trajectory correction pulses of the component assembly robot.

[0103] The number of trajectory correction pulses and the energy consumption degradation trend of the marking component assembly robot are standardized to obtain the correction pulse factor and the energy consumption degradation factor.

[0104] The energy efficiency imbalance state is calculated by combining the corrected pulse factor and the energy consumption degradation factor. The calculation formula is as follows: ,in, To correct the pulse factor, As an energy consumption degradation factor, This indicates an energy efficiency imbalance.

[0105] The energy efficiency imbalance reflects the degree of matching between the energy efficiency stability and trajectory correction requirements of an industrial robot in the current working cycle. The larger the energy efficiency imbalance value, the higher the robot's energy consumption degradation trend and trajectory correction frequency, and the significant risk of imbalance between its energy efficiency utilization and motion control. The smaller the energy efficiency imbalance value, the better the robot's energy efficiency and motion control are coordinated, and the relatively balanced system operation status.

[0106] It needs to be explained that the robot agent refers to the embedded intelligent agent deployed in each industrial robot control unit, serving as the autonomous decision-making and state management node of the robot in the multi-agent scheduling system; the preset data acquisition cycle can be set according to the industrial robot's work cycle, joint motion control accuracy, and energy consumption analysis granularity requirements; the joint position encoding and detection unit refers to the high-precision position sensing device integrated into the transmission system of each joint of the industrial robot, usually an encoder or rotary transformer, used to acquire the displacement of each joint of the marking component assembly robot; the servo drive joint feedback device refers to the real-time state monitoring device integrated into the servo drive unit of the industrial robot, used to acquire the number of stroke changes of each joint of the marking component assembly robot; the motion control command monitoring unit refers to the software unit deployed in the industrial robot control system, which acquires all motion control data, including trajectory correction commands, by monitoring the output interface of the control bus, command queue, or motion interpolator, and is used to obtain the trajectory correction control commands received by the marking component assembly robot.

[0107] By integrating multi-dimensional factors, the system achieves intelligent identification of densely-tuned workstations and quantitative assessment of robot energy efficiency imbalance. It also constructs a correlation analysis model from joint motion load to trajectory correction requirements, providing the system with risk warnings and key monitoring targets to support predictive scheduling and energy efficiency optimization decisions.

[0108] In step S3, after identifying the workstations and their corresponding robots with high energy efficiency imbalance risk in the multi-robot collaborative assembly system, it is necessary to further determine whether to implement dynamic scheduling in order to avoid affecting the overall assembly quality and efficiency due to local equipment performance degradation or control instability.

[0109] The scheduling and coordination agent compares the energy efficiency imbalance status with a preset energy efficiency imbalance threshold to make a judgment:

[0110] If the energy efficiency imbalance is greater than or equal to the preset energy efficiency imbalance threshold, the scheduling coordination agent will determine to make scheduling changes to the marked component assembly robot.

[0111] If the energy efficiency imbalance is less than the preset energy efficiency imbalance threshold, the scheduling and coordination agent will decide not to make scheduling changes to the robot assembling the marked parts.

[0112] The scheduling and coordination agent matches the work station number of the intensive survey work station with the historical database to obtain the work trajectory correction benchmark for the intensive survey work station.

[0113] The motion path rearrangement record information of the marking robot refers to the set of operation record information used to reflect the replanning, adjustment or reorganization of the robot's operation motion path during the process of changing the job scheduling or optimizing the path of the marking component assembly robot. It includes the number of path rearrangements and the path offset magnitude.

[0114] The number of path rearrangements is obtained through the path control instruction recording unit;

[0115] The actual joint position coordinates of each joint of the marked component assembly robot are obtained through the joint position encoding and detection unit.

[0116] The target position coordinates of each joint of the robot assembling the marked parts are obtained through the trajectory caching unit;

[0117] The Euclidean distance between the actual joint position coordinates and the corresponding target position coordinates of each joint of the marking component assembly robot is calculated to obtain the position deviation of each joint of the marking component assembly robot.

[0118] The path offset magnitude is obtained by summing the positional deviations of each joint of the robot assembling the marked parts.

[0119] It should be explained that the preset energy efficiency imbalance threshold is set based on the energy efficiency stability requirements of industrial robots, the tolerance of trajectory control accuracy, and the distribution of historical operating data; the historical database refers to a structured data warehouse that stores trajectory corrections, energy efficiency status, scheduling records, and related performance indicators accumulated during the past operation of industrial robots and workstations, used to obtain the work trajectory correction benchmark for densely tuned workstations; the path control instruction recording unit refers to the instruction log module integrated into the industrial robot motion control system, which records all control instructions related to path replanning and trajectory adjustment in real time, used to obtain the number of path rearrangements; the joint position encoding detection unit refers to the high-precision position sensing unit integrated into the servo motors or transmission mechanisms of each joint of the industrial robot, used to obtain the actual joint position coordinates of each joint of the marked component assembly robot; the trajectory cache unit refers to the storage area in the industrial robot control system or motion planning module used to temporarily store target trajectory data, usually implemented in the form of a queue or buffer, used to obtain the target position coordinates of each joint of the marked component assembly robot; Euclidean distance calculation refers to the mathematical method of calculating the straight-line distance between two points in multi-dimensional space, used to obtain the position deviation of each joint of the marked component assembly robot.

[0120] An adaptive scheduling triggering mechanism based on threshold judgment is constructed, and historical trajectory correction benchmarks and real-time path stability data are integrated to provide multi-time-dimensional decision-making basis for scheduling decisions, realize a smooth transition from anomaly detection to scheduling preparation, and improve the real-time response and decision reliability of the system.

[0121] In step S4, after completing the state assessment and path stability analysis of the close-fitting workstation and its corresponding robot in the multi-robot collaborative assembly system, an executable scheduling decision needs to be finally formed to achieve dynamic adaptation of system resources and assembly tasks.

[0122] The number of path rearrangements and the path offset magnitude are standardized to obtain the rearrangement factor and the offset factor.

[0123] The path stability index is calculated by combining the rearrangement factor and the offset factor. The formula is as follows: ,in, As the rearrangement factor, As the offset factor, It is a path stability index;

[0124] The path stability index reflects the overall stability of an industrial robot during the motion path replanning and execution process. The larger the path stability index, the lower the frequency of path replanning and the smaller the trajectory deviation, indicating high stability in motion control and path following. The smaller the path stability index, the more frequent the path adjustment or the significant the trajectory deviation, indicating that its motion stability needs to be optimized.

[0125] The path stability index and the operation trajectory correction benchmark are standardized to obtain the stability factor and correction factor.

[0126] The scheduling level adjustment coefficient is calculated by combining the stability factor and the correction factor. The calculation formula is as follows: ,in, As a stabilizing factor, As a correction factor, and To preset the weighting coefficients, This is the adjustment coefficient for the scheduling level;

[0127] It should be noted that the preset weighting coefficients α and β are set according to the relative importance of path stability and historical correction benchmarks in scheduling decisions.

[0128] The scheduling level adjustment coefficient reflects the dynamic adjustment range of the scheduling priority of workstations after considering the overall path stability and historical correction experience. The larger the scheduling level adjustment coefficient, the higher the path stability and the lower the historical correction requirement. The scheduling level can be appropriately increased to optimize resource allocation. The smaller the scheduling level adjustment coefficient, the lower the path stability or the more frequent the historical correction. The scheduling level needs to be reduced accordingly to match the actual operation capacity.

[0129] Multiply the scheduling level adjustment coefficient by the job scheduling level ordinal value to obtain the adjusted job scheduling level ordinal value;

[0130] The adjusted job scheduling hierarchy position value is matched with the job scheduling hierarchy database to obtain the adjusted job scheduling hierarchy.

[0131] The scheduling and coordination agent compares the adjusted job scheduling level with the current job scheduling level to make a judgment:

[0132] If the adjusted job scheduling level is lower than the current job scheduling level, the scheduling coordination agent determines that the marked component assembly robot has the risk of insufficient path stability and uneven energy efficiency utilization in the original job station, and marks the job station to be tested.

[0133] Conversely, the scheduling and coordination agent will determine not to mark the workstation to be tested;

[0134] Match the marked workstations to be tested with the job scheduling management database to obtain the component assembly robot that meets the job scheduling level of the marked workstations to be tested;

[0135] The scheduling of the component assembly robot is changed based on the job scheduling level that meets the requirements of the marked workstations to be tested.

[0136] It should be explained that scheduling change refers to the system operation that dynamically adjusts the work station, work sequence, or movement path of the marked component assembly robot based on the results of multi-agent collaborative decision-making.

[0137] By combining the path stability index with historical correction experience, dynamic optimization and adaptive adjustment of the scheduling level are achieved, forming a complete monitoring-evaluation-decision-execution scheduling closed loop. Ultimately, through intelligent matching and scheduling changes between workstations and robots, the system's resource utilization efficiency, assembly stability, and overall collaborative performance are comprehensively improved.

[0138] Example 2

[0139] Please see Figure 2 A multi-agent collaborative industrial robot scheduling system is used to implement a multi-agent collaborative industrial robot scheduling method, including an energy efficiency sensing module, an imbalance judgment module, a scheduling decision module, and a hierarchical correction module.

[0140] The energy efficiency sensing module is used to set the energy efficiency assessment time, detect the servo drive voltage data of the component assembly robot on the work station under test during the energy efficiency assessment time, generate energy efficiency braking characteristics, and retrieve the work scheduling level of the work station under test.

[0141] The imbalance judgment module is used to screen densely adjusted work stations and mark component assembly robots by comprehensively considering the operation scheduling level and energy efficiency braking characteristics. The component assembly robot is the component assembly robot corresponding to the densely adjusted work station. The module detects the joint stroke data of the marked component assembly robot and analyzes the energy consumption degradation trend. It counts the number of trajectory correction pulses of the marked component assembly robot and generates an energy efficiency imbalance state based on the energy consumption degradation trend.

[0142] The scheduling decision module is used to determine whether to make scheduling changes to the marked component assembly robot based on the energy efficiency imbalance state, access the historical database to obtain the work trajectory correction benchmark of the dense adjustment work station, and collect the motion path rearrangement record information of the marked component assembly robot.

[0143] The hierarchical correction module generates a path stability index based on motion path rearrangement record information, adjusts the job scheduling level in conjunction with the job trajectory correction benchmark, filters and marks the job stations to be tested, and makes scheduling changes to the component assembly robot based on the marked job stations to be tested.

[0144] Finally, it should be noted that in this paper, relational terms such as first and second are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations.

[0145] Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0146] In this document, the singular forms “a,” “an,” and “the” may also include the plural forms unless the context clearly indicates otherwise. It should also be understood that terms such as “comprising / including” or “having” specify the presence of the stated features, integrals, steps, operations, components, parts, or combinations thereof, but do not preclude the possibility of the presence or addition of one or more other features, integrals, steps, operations, components, parts, or combinations thereof. Meanwhile, the term “and / or” as used in this specification includes any and all combinations of the associated listed items.

[0147] The various embodiments in this specification are described in a progressive manner. Each embodiment focuses on the differences from other embodiments. The various embodiments can be combined as needed, and the same or similar parts can be referred to each other.

[0148] The above description of the disclosed embodiments will enable those skilled in the art to make or use various modifications to these embodiments. It will be readily apparent to those skilled in the art that the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of this application. Therefore, this application is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. An industrial robot scheduling method based on multi-agent collaboration, characterized in that: Includes the following steps: Step S1: Set the energy efficiency assessment time, detect the servo drive voltage data of the component assembly robot on the work station to be tested within the energy efficiency assessment time and generate energy efficiency braking characteristics, and retrieve the work scheduling level of the work station to be tested. Step S2: Select densely tuned workstations and mark component assembly robots by comprehensively considering the job scheduling level and energy efficiency braking characteristics. Detect the joint stroke data of the marked component assembly robots and analyze the energy consumption degradation trend. Count the number of trajectory correction pulses of the marked component assembly robots. Combine the energy consumption degradation trend to generate an energy efficiency imbalance state. Step S3: Use the energy efficiency imbalance state to determine whether to make scheduling changes to the marking component assembly robot. When making scheduling changes, access the historical database to obtain the work trajectory correction benchmark of the dense adjustment work station, and collect the motion path rearrangement record information of the marking robot. Step S4: Generate a path stability index based on motion path rearrangement record information, adjust the job scheduling level in conjunction with the job trajectory correction benchmark, select and mark the job stations to be tested, and make scheduling changes to the component assembly robot based on the marked job stations to be tested.

2. The industrial robot scheduling method based on multi-agent collaboration according to claim 1, characterized in that: In step S1, the servo drive voltage data of the component assembly robot at the work station to be tested includes the servo drive voltage. The energy efficiency assessment time is set by the workstation monitoring agent and divided into multiple collection times. The servo drive voltage is obtained by the robot servo drive detection unit and integrated into a servo drive voltage set according to the collection order. The average value of the servo drive voltage set is calculated to obtain the servo drive voltage reference value; Traverse the set of servo drive voltages, calculate the difference between each servo drive voltage and the servo drive voltage reference value, and obtain the voltage deviation set. The average value of the voltage deviation set is calculated and the absolute value is taken as the energy efficiency braking characteristic. Match the job station number of the job station to be tested with the job scheduling management database to obtain the job scheduling level of the job station to be tested.

3. The industrial robot scheduling method based on multi-agent collaboration according to claim 2, characterized in that: In step S2, the job scheduling level of the workstation to be tested is matched with the job scheduling level database to obtain the job scheduling level sequence value; The preset job scheduling level sequence value and energy efficiency braking characteristics corresponding to the job scheduling level of the work station under test are standardized to obtain the scheduling level factor and braking characteristic factor. The density adjustment characteristic value of the workstation under test is calculated by combining the scheduling level factor and the braking characteristic factor. If the density feature value of the work station to be tested is greater than or equal to the preset density feature threshold, the work station to be tested is determined to be a density work station, and the component assembly robot corresponding to the density work station is marked. Conversely, if the result is not found, the work station to be tested is determined not to be a close-fitting work station.

4. The industrial robot scheduling method based on multi-agent collaboration according to claim 1, characterized in that: In step S2, the joint stroke data of the marker component assembly robot includes the cumulative joint displacement and the frequency of joint stroke changes; Set a preset acquisition cycle and obtain the displacement of each joint of the marked component assembly robot through the joint position encoding detection unit; The total displacement of the joints of the robot is obtained by adding up the displacements of the marked parts. The number of stroke changes of each joint of the marking component assembly robot is obtained through the servo drive joint feedback device in the servo drive unit; The stroke change frequency of each joint of the robot assembling the marked parts is obtained by summing the stroke changes of each joint and dividing by the preset acquisition period. The cumulative joint displacement and the frequency of joint stroke changes are standardized to obtain the displacement factor and frequency factor. The energy consumption degradation trend is calculated by combining displacement factor and frequency factor.

5. The industrial robot scheduling method based on multi-agent collaboration according to claim 4, characterized in that: In step S2, the trajectory correction control command received by the marker component assembly robot is obtained through the motion control command monitoring unit; The trajectory correction control commands received by the marking component assembly robot are analyzed one by one, and the valid trajectory correction commands among the trajectory correction control commands received by the marking component assembly robot are counted as the trajectory correction pulse count of the marking component assembly robot. The number of trajectory correction pulses and the energy consumption degradation trend of the marking component assembly robot are standardized to obtain the correction pulse factor and the energy consumption degradation factor. The energy efficiency imbalance state is calculated by combining the corrected pulse factor and the energy consumption degradation factor.

6. The industrial robot scheduling method based on multi-agent collaboration according to claim 1, characterized in that: In step S3, the energy efficiency imbalance state is compared with the preset energy efficiency imbalance threshold for judgment: If the energy efficiency imbalance is greater than or equal to the preset energy efficiency imbalance threshold, it is determined that the scheduling of the robot assembling the marked parts should be changed. Conversely, if the condition is not met, it is determined that no scheduling changes will be made to the robot assembling the marked parts; Match the work station number of the intensive survey work station with the historical database to obtain the work trajectory correction benchmark for the intensive survey work station.

7. The industrial robot scheduling method based on multi-agent collaboration according to claim 1, characterized in that: In step S3, the motion path rearrangement record information of the marked robot includes the number of path rearrangements and the path offset magnitude; The number of path rearrangements is obtained through the path control instruction recording unit; The actual joint position coordinates of each joint of the marked component assembly robot are obtained through the joint position encoding and detection unit. The target position coordinates of each joint of the robot assembling the marked parts are obtained through the trajectory caching unit; The Euclidean distance between the actual joint position coordinates and the corresponding target position coordinates of each joint of the marking component assembly robot is calculated to obtain the position deviation of each joint of the marking component assembly robot. The path offset magnitude is obtained by summing the positional deviations of each joint of the robot assembling the marked parts.

8. The industrial robot scheduling method based on multi-agent collaboration according to claim 7, characterized in that: In step S4, the number of path rearrangements and the path offset magnitude are standardized to obtain the rearrangement factor and the offset factor. The path stability index is calculated by combining the rearrangement factor and the offset factor. The path stability index and the operation trajectory correction benchmark are standardized to obtain the stability factor and correction factor. The scheduling level adjustment coefficient is calculated by combining the stability factor and the correction factor. Multiply the scheduling level adjustment coefficient by the job scheduling level ordinal value to obtain the adjusted job scheduling level ordinal value; The adjusted job scheduling hierarchy position value is matched with the job scheduling hierarchy database to obtain the adjusted job scheduling hierarchy.

9. The industrial robot scheduling method based on multi-agent collaboration according to claim 8, characterized in that: In step S4, if the adjusted job scheduling level is lower than the current job scheduling level, it is determined that the marked component assembly robot has the risk of insufficient path stability and unbalanced energy efficiency utilization in the original job station, and the job station to be tested is marked. Conversely, if the condition is not met, the work station to be tested will not be marked. Match the marked workstations to be tested with the job scheduling management database to obtain the component assembly robot that meets the job scheduling level of the marked workstations to be tested; The scheduling of the component assembly robot is changed based on the job scheduling level that meets the requirements of the marked workstation.

10. An industrial robot scheduling system based on multi-agent collaboration, used to implement the industrial robot scheduling method based on multi-agent collaboration as described in any one of claims 1-9, characterized in that: It includes an energy efficiency sensing module, an imbalance detection module, a scheduling decision module, and a hierarchical correction module; The energy efficiency sensing module is used to set the energy efficiency assessment time, detect the servo drive voltage data of the component assembly robot on the work station under test during the energy efficiency assessment time, generate energy efficiency braking characteristics, and retrieve the work scheduling level of the work station under test. The imbalance judgment module is used to screen densely adjusted work stations and mark component assembly robots by comprehensively considering the operation scheduling level and energy efficiency braking characteristics. The component assembly robot is the component assembly robot corresponding to the densely adjusted work station. The module detects the joint stroke data of the marked component assembly robot and analyzes the energy consumption degradation trend. It counts the number of trajectory correction pulses of the marked component assembly robot and generates an energy efficiency imbalance state based on the energy consumption degradation trend. The scheduling decision module is used to determine whether to make scheduling changes to the marked component assembly robot based on the energy efficiency imbalance state, access the historical database to obtain the work trajectory correction benchmark of the dense adjustment work station, and collect the motion path rearrangement record information of the marked component assembly robot. The hierarchical correction module generates a path stability index based on motion path rearrangement record information, adjusts the job scheduling level in conjunction with the job trajectory correction benchmark, filters and marks the job stations to be tested, and makes scheduling changes to the component assembly robot based on the marked job stations to be tested.

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