Multi-mechanical-arm cooperative task allocation method and system based on high-order network

Abstract

The invention discloses a multi-mechanical-arm cooperative task allocation method and system based on a high-order network, and relates to the technical field of automatic control. The method comprises the following steps: firstly, constructing a high-order network by taking each mechanical arm as a node, integrating a state of each mechanical arm and a target position vector, constructing an energy function reflecting a distance relationship between an end effector and a target, and deducing a gradient descent function by analyzing the energy function, the state of each mechanical arm and the target position vector; based on the gradient descent function and the adjustment parameters of the angular velocity of the mechanical arms, angular velocity expressions of all the mechanical arms are obtained through processing, meanwhile, communication, scalar states, activation states and neighbor sets of the mechanical arms are comprehensively monitored, and task allocation is dynamically adjusted according to the monitoring result so as to optimize the overall task execution effect. The cooperative operation efficiency of the mechanical arm system is improved, reasonable allocation of tasks and flexible scheduling of the mechanical arms are achieved, therefore, optimal allocation of resources is ensured, and excessive or insufficient resource allocation is avoided.

Description

Technical Field

[0001] The present invention relates to the field of automation control technology, and in particular to a multi-robot arm collaborative task allocation method and system based on a high-order network. Background Art

[0002] With the rapid development of industrial automation and intelligence, multi-robot systems have shown important value in manufacturing, logistics, medical care, and service fields. Through the collaborative work of multiple robotic arms, the distributed multi-robot collaborative system can efficiently and accurately complete complex tasks, such as multi-component assembly, dynamic sorting, and flexible operations in heterogeneous environments. However, task allocation and collaborative control between robotic arms have always been the core challenges in system design, which directly affect the efficiency, reliability, and execution effect of the system.

[0003] For example, the invention patent with announcement number CN114932555B announces a robotic arm collaborative operation system, including multiple controllers and multiple robot individuals, different controllers corresponding to different robot individuals, and each robot individual includes one or more robotic arms; wherein the controller is used to: receive control instructions; based on the control instructions, determine one or more target robotic arms that respond to the control instructions from the robotic arms of the robot individuals corresponding to the controller; parse the control instructions into motion instructions for the target robotic arms; and send the motion instructions to the target robotic arms through a preset underlying control communication interface to instruct the target robotic arms to implement the target behavior represented by the control instructions.

[0004] For example, the invention patent with announcement number CN116968037B announces a method for multi-robotic arm collaborative task scheduling, including: step S1, constructing a digital robotic arm model; step S2, obtaining multiple newborn biological individuals; step S3, extracting task motion attributes and obtaining timestamps of each task point; step S4, performing interpolation calculations on each axis on each robotic arm to obtain corresponding motion change data and obtain priority execution planning tasks; step S5, obtaining joint angle information of each task point and obtaining the spatiotemporal point cloud trajectory of the robotic arm; step S6, determining whether there is a spatiotemporal point cloud trajectory of a robotic arm with an undetermined work task start time: if so, finding the target task start time of the spatiotemporal point cloud trajectory of the robotic arm and adjusting the spatiotemporal point cloud trajectory of the robotic arm to avoid the remaining robotic arms; otherwise, exiting.

[0005] Based on the above findings, existing technical solutions may have problems such as slow response speed, high computational overhead, and insufficient ability to adapt to environmental changes, which may lead to resource competition among multiple robotic arms, task conflicts or uneven distribution, significantly reducing the overall efficiency of the system. Summary of the invention

[0006] In view of the deficiencies in the prior art, the present invention provides a multi-robot arm collaborative task allocation method and system based on a high-order network, which solves the problems designed in the above-mentioned background technology.

[0007] To achieve the above objectives, the present invention is implemented through the following technical solutions: a multi-robot collaborative task allocation method based on a high-order network, comprising: building a high-order network with each robot arm as a node, obtaining the environmental information of each robot arm in the high-order network and the resource requirement data of each task, and obtaining the adaptation value of each task of each robot arm based on the resource requirement data of each task and the environmental information of each robot arm.

[0008] Tasks are assigned to each robotic arm according to the adaptation value of each task of each robotic arm, and each robotic arm is adjusted.

[0009] The interactive information of each task execution of each robot arm's task assignment is obtained and analyzed to obtain the compliance index of each task execution of each robot arm, and the task assignment of each robot arm is adjusted according to the compliance index of each task execution of each robot arm.

[0010] Furthermore, the environmental information of each robotic arm includes: the average speed and average acceleration of each adjacent robotic arm, the working radius of each robotic arm, the maximum weight that each robotic arm can bear, and the number of tasks currently executed by each robotic arm.

[0011] The resource requirement data of each task includes: the working radius of each task and the required load of each task.

[0012] The acquired environmental information of each robot arm and the resource requirement data of each task are preprocessed to obtain preprocessed data, wherein the preprocessing includes processing of missing values, abnormal values ​​and repeated data.

[0013] Furthermore, the average speed, average acceleration, working radius and maximum bearable weight of each robotic arm after preprocessing are extracted, and the working radius and required load of each task are extracted to obtain the adaptation value of each robotic arm for each task. The adaptation value of each robotic arm for each task is used to evaluate the performance matching degree of each robotic arm for each task.

[0014] Furthermore, the task adaptation values ​​of each robotic arm are extracted, and the task adaptation values ​​of each robotic arm are sorted from large to small to obtain the task adaptation ranking of each robotic arm. Tasks are assigned to each robotic arm starting from the first place in the task adaptation ranking of each robotic arm, and the robotic arm is assigned to each task in turn according to the sorting result.

[0015] Furthermore, the task adaptation values ​​of each robot arm are extracted and matched with the performance matching degrees of each task of each robot arm corresponding to each interval of the task adaptation value of each robot arm stored in the database to obtain the performance matching degrees of each task of each robot arm. The performance matching degrees of each task of the robot arm include: excellent matching, good matching, medium matching, and poor matching. According to the performance matching degrees of each task of each robot arm, the torque and power output of the robot arm are adjusted.

[0016] Furthermore, a monitoring time period is preset, and the power consumption, power consumption and task progress of each robotic arm performing a task are monitored in each monitoring time period to obtain the power consumption and power consumption of each robotic arm performing each task in each monitoring time period; the workload of each robotic arm performing a task is monitored in each monitoring time period, and the maximum value of the workload of each robotic arm performing a task in each monitoring time period is taken to obtain the maximum workload of each robotic arm performing each task in each monitoring time period; the task progress of each robotic arm performing a task in each monitoring time period is divided by the total number of tasks to obtain the completion percentage of each task performed by each robotic arm in each monitoring time period.

[0017] The electricity consumption, power consumption, maximum workload and completion percentage of each robotic arm performing each task in each monitoring time period are extracted, and the state evaluation value of each robotic arm performing each task in each monitoring time period is obtained by processing. The state evaluation value of each robotic arm performing each task in each monitoring time period is used to evaluate the work efficiency of the robotic arm.

[0018] Furthermore, the task adaptation value of each robotic arm and the state evaluation value of each robotic arm performing each task in each monitoring time period are extracted, and the task execution compliance index of each robotic arm is obtained by processing. The task execution compliance index of each robotic arm is used to evaluate the stability of the robotic arm system.

[0019] The task execution compliance index of each robotic arm is extracted and matched with the system stability corresponding to each interval of the task execution compliance index of the robotic arm stored in the database to obtain the system stability of each task of each robotic arm. The system stability of each task of each robotic arm includes: stable, generally stable, and unstable.

[0020] When the stability of the system of each task of each robot arm is stable, the working data of the robot arm is continuously monitored; when the stability of the system of each task of each robot arm is generally stable or unstable, the load of the robot arm is adjusted.

[0021] Furthermore, the task execution compliance index of each robotic arm is extracted and compared with the task execution compliance index threshold of each robotic arm stored in the database. If the task execution compliance index of a robotic arm is higher than the task execution compliance index threshold of the robotic arm, the task volume of the robotic arm is increased.

[0022] If the task execution compliance index of a certain robot arm is equal to the task execution compliance index threshold of the robot arm, there is no need to adjust the task volume of the robot arm.

[0023] If the task execution compliance index of a certain robot arm is lower than the task execution compliance index threshold of the robot arm, the task amount of the robot arm is reduced.

[0024] Furthermore, the execution compliance index of each task of each robot arm is analyzed as follows:

[0025]

[0026] In the formula, ρ i→j It indicates the execution compliance index of the jth task of the i-th robot arm, I i→j represents the jth task adaptation value of the i-th robotic arm, represents the state evaluation value of the i-th robot performing the j-th task in the q-th monitoring time period, a1 represents the weight factor corresponding to the set task adaptation value, a2 represents the weight factor corresponding to the state evaluation value of the set task, i=1,2,...,m, i represents the number of each robot arm, m represents the total number of robot arms, j=1,2,...,n, j represents the number of each task, n represents the total number of tasks, q=1,2,...,r, q represents the number of each task, r represents the total number of tasks.

[0027] Furthermore, the second aspect of this paper provides a multi-robot collaborative task allocation system based on a high-order network, including:

[0028] The data acquisition and processing module is used to construct a high-order network with each robotic arm as a node, obtain the environmental information of each robotic arm in the high-order network and the resource requirement data of each task, and obtain the adaptation value of each task of each robotic arm based on the resource requirement data of each task and the environmental information of each robotic arm.

[0029] The task allocation module is used to allocate tasks to each robotic arm according to the task adaptation value of each robotic arm and adjust each robotic arm.

[0030] The task adjustment module is used to monitor and obtain the interactive information of each task assigned to each robot arm and analyze it to obtain the compliance index of each task execution of each robot arm, and adjust the task assignment of each robot arm according to the compliance index of each task execution of each robot arm.

[0031] The present invention has the following beneficial effects:

[0032] (1) The present invention constructs a high-order network with each robotic arm as a node, and uses the environmental information of each robotic arm and the resource requirement data of each task in the high-order network to process and obtain the task adaptation value of each robotic arm, and assigns tasks to each robotic arm according to the task adaptation value of each robotic arm; by monitoring the interaction information of the robotic arm during the task execution, the task execution compliance index of each robotic arm is analyzed and obtained, and the task assignment is dynamically adjusted according to the task execution compliance index of each robotic arm to optimize the overall task execution effect, help ensure the optimal configuration of resources, and help avoid excessive or insufficient resource allocation.

[0033] (2) The present invention obtains the task adaptation value of each robotic arm, evaluates the performance matching degree of the robotic arm to each task, assigns tasks to the robotic arms in sequence according to the ranking of the adaptation values, and adjusts the torque and power output of the robotic arms according to the matching degree, thereby optimizing the effect of task execution.

[0034] (3) The present invention obtains a state evaluation value of each robotic arm, evaluates the working efficiency of each robotic arm, and conducts a comprehensive analysis of the working state of the robotic arm based on the evaluation value, thereby providing a basis for collaborative task allocation of multiple robotic arms.

[0035] (4) The present invention obtains the execution compliance index of each task of each robotic arm, evaluates the stability of the robotic arm system according to the execution compliance index of each task of each robotic arm, and adjusts the task amount of the robotic arm to ensure efficient operation of the system.

[0036] Of course, any product implementing the present invention does not necessarily need to achieve all of the advantages described above at the same time. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] Figure 1 It is a schematic diagram of the process of the present invention.

[0038] Figure 2 The task adaptation value-task execution of the present invention conforms to an exponential change curve.

[0039] Figure 3 It is a schematic diagram of the system flow of the present invention. DETAILED DESCRIPTION

[0040] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0041] In the description of the present invention, it should be understood that the terms "opening", "upper", "lower", "thickness", "top", "middle", "length", "inside", "all around" and the like indicating orientation or positional relationship are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the components or elements referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be understood as limiting the present invention.

[0042] See also Figure 1 The embodiment of the present invention provides a technical solution: a multi-robotic arm collaborative task allocation method and system based on a high-order network, including constructing a high-order network with each robotic arm as a node, obtaining the environmental information of each robotic arm in the high-order network and the resource requirement data of each task, and obtaining the task adaptation value of each robotic arm based on the resource requirement data of each task and the environmental information of each robotic arm.

[0043] Tasks are assigned to each robotic arm according to the adaptation value of each task of each robotic arm, and each robotic arm is adjusted.

[0044] The interactive information of each task execution of each robot arm's task assignment is obtained and analyzed to obtain the compliance index of each task execution of each robot arm, and the task assignment of each robot arm is adjusted according to the compliance index of each task execution of each robot arm.

[0045] It should be noted that a high-order network is constructed by taking each robotic arm as a node, and the environmental information of each robotic arm and the resource requirement data of each task in the high-order network are used to process the adaptation value of each task of each robotic arm, and then assign tasks to each robotic arm. According to the interaction information during the execution of tasks by the robotic arm, the execution compliance index of each task of each robotic arm is analyzed, and the task allocation is dynamically adjusted according to the execution compliance index of each task of each robotic arm, so as to optimize the overall task execution effect and achieve more reasonable task allocation, which helps to ensure the optimal configuration of resources and improve the performance and reliability of the overall system.

[0046] It should be noted that each robot is used as a node to construct a high-order network: each robot is regarded as an independent node. In graph theory, a node is the basic unit of a graph, representing an object or entity. The collaborative relationship between the robots is not represented by ordinary edges, but by hyperedges. Hyperedges are an extension of graph theory that can connect multiple nodes, not just two nodes. Therefore, hyperedges in high-order networks can be used to represent the collaborative relationship between multiple robots, reflecting how these robots collaborate in certain tasks or operations. High-order network: In traditional graphs, each edge connects two nodes, while in high-order networks, hyperedges connect multiple nodes. This structure can better express complex collaborations or relationships, especially when multiple robots are involved in a task at the same time. The collaborative relationship of multiple robots is modeled through hyperedges in a high-order network. Each robot is a node, and the collaboration between multiple robots is described by hyperedges containing these robots.

[0047] Specifically, the environmental information of each robotic arm includes: the average speed and average acceleration of each adjacent robotic arm, the working radius of each robotic arm, the maximum weight that each robotic arm can bear, and the number of tasks currently executed by each robotic arm.

[0048] The resource requirement data of each task includes: the working radius of each task and the required load of each task.

[0049] The acquired environmental information of each robot arm and the resource requirement data of each task are preprocessed to obtain preprocessed data, wherein the preprocessing includes processing of missing values, abnormal values ​​and repeated data.

[0050] It should be noted that the speed of the robot arm during the movement process is recorded by a speed meter, and the average is calculated to obtain the average speed of the robot arm; the acceleration of the robot arm is measured by an accelerometer, and the average is calculated to obtain the average acceleration; the position of the end effector of the robot arm is tracked in real time by a laser rangefinder, and the position information of the end effector is recorded to obtain the farthest point that the robot arm can actually reach during the work process, which is the working radius of the robot arm; the maximum weight that the robot arm can bear is measured by a load sensor; and the number of tasks being performed by the robot arm can be displayed using monitoring software (such as ROS).

[0051] It should be noted that when issuing each task instruction, the resource requirement data is determined, including the working radius of each task and the required load of each task. The working radius of the task refers to the radius of the operating space range required for task execution. When the robot arm accepts the task instruction, it also accepts the resource requirement data of each task.

[0052] It should be noted that the processing of missing values, outliers and duplicate data includes using the median to fill in the missing values ​​in the environmental information of each robot arm and the resource requirement data of each task. The outlier value processing means using the mean to replace the outliers in the environmental information of each robot arm and the resource requirement data of each task. The duplicate value processing includes deleting the duplicate data in the environmental information of each robot arm and the resource requirement data of each task.

[0053] Specifically, the average speed, average acceleration, working radius and maximum bearable weight of each robotic arm are extracted after preprocessing, and the working radius and required load of each task are extracted to obtain the adaptation value of each robotic arm for each task. The adaptation value of each robotic arm for each task is used to evaluate the performance matching degree of each robotic arm for each task.

[0054] It should be noted that the working radius of the i-th robot arm is extracted and marked as C , i , the maximum weight that the i-th robot can bear is marked as P , i , the working radius of the jth task is marked as C j , the required load of the jth task is marked as P j , by default, the working radius of the i-th robot arm is greater than or equal to the working radius of the j-th task and the maximum weight that the i-th robot arm can bear is greater than or equal to the required load of the j-th task, that is, C , i ≥C j And P , i ≥P j When , the adaptation value of each task of each robot arm is obtained.

[0055] It should be noted that the specific analysis process of the adaptation value of each task of each robot arm is as follows:

[0056]

[0057] In the formula, I i→j represents the jth task adaptation value of the i-th robot arm, V i represents the average speed of the i-th robot arm, A i represents the average acceleration of the i-th robot arm, C , i represents the working radius of the i-th robot arm, P , i represents the maximum weight that the i-th robotic arm can bear, C j represents the working radius of the jth task, P jrepresents the required load of the jth task, ΔV represents the reference value of the set average speed of the robot arm, ΔA represents the reference value of the set average acceleration of the robot arm, ΔC represents the set allowable value of the working radius error, ΔP represents the set allowable value of the load error, b1 represents the correction factor corresponding to the set average speed of the robot arm, b2 represents the correction factor corresponding to the set average acceleration of the robot arm, b3 represents the correction factor corresponding to the set working radius error, b4 represents the correction factor corresponding to the set load error, i=1,2,...,m, i represents the number of each robot arm, m represents the total number of robot arms, j=1,2,...,n, j represents the number of each task, and n represents the total number of tasks.

[0058] It should be noted that there is a correlation between the average speed, average acceleration, working radius, maximum weight that each robot can bear, working radius of each task and the required load of each task. The longer the working radius of the robot, the greater the torque generated by the load, and the smaller the maximum weight that the robot can bear. A high task demand load usually requires a smaller working radius to ensure a higher task demand load. The greater the average acceleration of the robot, the higher the average speed will be.

[0059] The correction factor corresponding to the average speed of the robot arm set in the database is denoted as b1. The role of this correction factor is to quantify the influence of the average speed of the robot arm on the task adaptation value. In practical applications, the correction factor can be directly obtained from the database, and its corresponding relationship is a pre-set mapping relationship. For example, a one-to-one or many-to-one mapping set is formed between the average speed of the robot arm and the correction factor preset in the database. When the real-time average speed of the robot arm is input into this mapping set, the corresponding correction factor can be obtained. In this example, the value range of the correction factor is limited to [0,1].

[0060] The correction factor corresponding to the average acceleration of the manipulator set in the database is denoted as b2. The function of this correction factor is to quantify the influence of the average acceleration of the manipulator on the task adaptation value. In practical applications, the correction factor can be directly obtained from the database, and its corresponding relationship is a pre-set mapping relationship. For example, a one-to-one or many-to-one mapping set is formed between the average acceleration of the manipulator and the correction factor preset in the database. When the real-time average acceleration of the manipulator is input into this mapping set, the corresponding correction factor can be obtained. In this example, the value range of the correction factor is limited to [0,1].

[0061] The correction factor corresponding to the working radius error set in the database is denoted as b3. The function of this correction factor is to quantify the influence of the difference between the working radius of the robot and the working radius of the task on the task adaptation value. In practical applications, the correction factor can be directly obtained from the database, and its corresponding relationship is a pre-set mapping relationship. For example, a one-to-one or many-to-one mapping set is formed between the working radius error and the correction factor preset in the database. When the real-time working radius error is input into this mapping set, the corresponding correction factor can be obtained. In this example, the value range of the correction factor is limited to [0,1].

[0062] The correction factor corresponding to the load error set in the database is denoted as b4. The function of this correction factor is to quantify the influence of the difference between the maximum weight that the robot can bear and the required load of the task on the task adaptation value. In practical applications, the correction factor can be directly obtained from the database, and its corresponding relationship is a pre-set mapping relationship. For example, a one-to-one or many-to-one mapping set is formed between the load error and the correction factor preset in the database. When the real-time load error is input into this mapping set, the corresponding correction factor can be obtained. In this example, the value range of the correction factor is limited to [0,1].

[0063] It should be noted that the evaluation of the performance matching degree of each robot for each task includes: extracting the performance matching degree corresponding to each task adaptation value of each robot and each interval of the task adaptation value stored in the database for matching. The database is preset with each performance matching degree. When in use, each performance matching degree can be directly obtained from the database. The corresponding relationship can be a pre-set mapping relationship. For example, the task adaptation value of each robot and the performance matching degree in the database form a mapping set, and the real-time task adaptation value of each robot is input into the mapping set to obtain each performance matching degree. The mapping relationship can be a one-to-one relationship. The performance matching degree includes: excellent matching, good matching, medium matching, and poor matching. The larger the task adaptation value of each robot, the better the resource requirement data of each task. Evaluating the matching degree between the robot and the task can effectively avoid the performance mismatch between the task and the robot and reduce the waste of resources caused by the mismatch.

[0064] Specifically, the task adaptation values ​​of each robotic arm are extracted, and the task adaptation values ​​of each robotic arm are sorted from large to small to obtain the task adaptation ranking of each robotic arm. Tasks are assigned to each robotic arm starting from the first place in the task adaptation ranking of each robotic arm, and robotic arms are assigned to each task in turn according to the sorting results.

[0065] It should be noted that the task allocation to each robotic arm includes: processing to obtain the task adaptation value of each robotic arm, and sorting the tasks of each robotic arm from large to small according to the task adaptation value of each robotic arm; then, assigning a robotic arm to each task according to the sorting. If a robotic arm has been assigned a task, skip the robotic arm and continue to assign tasks downward until all tasks have been assigned or all robotic arms have been used. Reasonable allocation of robotic arms helps to more reasonably utilize the working capacity of each robotic arm, avoid waste of resources, and improve overall efficiency.

[0066] Specifically, each task adaptation value of each robotic arm is extracted and matched with the performance matching degree of each task of the robotic arm corresponding to each interval of the task adaptation value of each robotic arm stored in the database to obtain the performance matching degree of each task of each robotic arm. The performance matching degree of each task of the robotic arm includes: excellent match, good match, medium match, and poor match. According to the performance matching degree of each task of each robotic arm, the torque and power output of the robotic arm are adjusted.

[0067] It should be noted that the database has preset the resource requirement data of each task corresponding to each interval of the task adaptation value of each robotic arm. When in use, the performance matching degree corresponding to each interval of the task adaptation value of each robotic arm can be directly obtained from the database, and the corresponding relationship can be a pre-set mapping relationship. For example, the task adaptation value of each robotic arm and the performance matching degree corresponding to each interval of the task adaptation value in the database form a mapping set, and the real-time task adaptation value of each robotic arm is input into the mapping set to obtain the performance matching degree corresponding to each interval of the task adaptation value. The mapping relationship can be a one-to-one correspondence.

[0068] It should be noted that the torque and power output of the robot arm are adjusted, including: by comparing the adaptation value of each robot arm for each task with the adaptation value interval preset in the database, determining the performance matching degree of the robot arm corresponding to the task, when the performance matching degree of the robot arm corresponding to the task is an excellent match, there is no need to adjust the torque and power output of the robot arm; when the performance matching degree of the robot arm corresponding to the task is a good match, a medium match and a poor match, extracting the torque and power historical improvement amplitudes of the robot arm corresponding to each performance matching degree stored in the database, and obtaining the torque and power improvement amplitudes of the robot arm corresponding to the performance matching degree of each robot arm corresponding to the task, and increasing the torque and power output of the robot arm according to different amplitudes, for example: when the performance matching degree of the robot arm to the task is a good match, the torque and power output of the robot arm are respectively increased by 15%; when the performance matching degree of the robot arm to the task is a medium match, the torque and power output of the robot arm are respectively increased by 40%; when the performance matching degree of the robot arm to the task is a poor match, the torque and power output of the robot arm are respectively increased by 60%. By flexibly adjusting the torque and power output of the robot arm according to different performance matching degrees, it helps to optimize the execution efficiency and effectiveness of the robot arm in collaborative tasks.

[0069] Specifically, a monitoring time period is preset, and the power consumption, power consumption and task progress of each robotic arm performing a task are monitored in each monitoring time period to obtain the power consumption and power consumption of each robotic arm performing each task in each monitoring time period; the workload of each robotic arm performing a task is monitored in each monitoring time period, and the maximum value of the workload of each robotic arm performing a task in each monitoring time period is taken to obtain the maximum workload of each robotic arm performing each task in each monitoring time period; the task progress of each robotic arm performing a task in each monitoring time period is divided by the total number of tasks to obtain the completion percentage of each task performed by each robotic arm in each monitoring time period.

[0070] The electricity consumption, power consumption, maximum workload and completion percentage of each robotic arm performing each task in each monitoring time period are extracted, and the state evaluation value of each robotic arm performing each task in each monitoring time period is obtained by processing. The state evaluation value of each robotic arm performing each task in each monitoring time period is used to evaluate the work efficiency of the robotic arm.

[0071] It should be noted that the robot arm is monitored in real time by a power meter to obtain its power consumption data; the robot arm is monitored in real time by a power meter to obtain its power consumption data; the workload of the robot arm is monitored in real time by a load sensor; and the task control system can provide the robot arm's task progress data in real time.

[0072] It should be noted that the state evaluation value of each robot arm performing each task in each monitoring time period is analyzed in the following specific process:

[0073]

[0074] In the formula, represents the state evaluation value of the i-th robot arm performing the j-th task in the q-th monitoring time period, represents the power consumption of the i-th robot arm performing the j-th task during the q-th monitoring period, represents the power consumption of the i-th robot arm performing the j-th task during the q-th monitoring period, represents the maximum workload of the i-th robot performing the j-th task during the q-th monitoring period, represents the completion percentage of the jth task performed by the ith robot in the qth monitoring time period, ΔE represents the reference value of the set power consumption, ΔU represents the reference value of the set power consumption, ΔLM represents the reference value of the set maximum workload, ΔRC represents the reference value of the set task completion percentage, e is a natural constant, d1 represents the correction factor corresponding to the set power consumption, d2 represents the correction factor corresponding to the set power consumption, d3 represents the correction factor corresponding to the set maximum workload, d4 ​​represents the correction factor corresponding to the set task completion percentage, i=1,2,...,m, i represents the number of each robot arm, m represents the total number of robot arms, j=1,2,...,n, j represents the number of each task, n represents the total number of tasks, q=1,2,...,r, q represents the number of each task, r represents the total number of tasks.

[0075] It should be noted that there is a correlation between the electricity consumption, power consumption, maximum workload and completion percentage of each robot arm performing each task during each monitoring time period. The higher the power consumption, the higher the electricity consumption; there is a positive correlation between the task completion percentage and the electricity consumption. As the task completion percentage of the robot arm increases, the electricity consumption will increase accordingly; the higher the maximum workload of the robot arm when working, the power consumption of the robot arm will increase significantly.

[0076] The correction factor corresponding to the power consumption set in the database is denoted as d1. The function of this correction factor is to quantify the influence of power consumption on the state evaluation value of the task. In practical applications, the correction factor can be directly obtained from the database, and its corresponding relationship is a pre-set mapping relationship. For example, a one-to-one or many-to-one mapping set is formed between power consumption and the correction factors preset in the database. When the real-time power consumption is input into this mapping set, the corresponding correction factor can be obtained. In this example, the value range of the correction factor is limited to [0,1].

[0077] The correction factor corresponding to the power consumption set in the database is denoted as d2. The function of this correction factor is to quantify the influence of power consumption on the state evaluation value of the task. In practical applications, the correction factor can be directly obtained from the database, and its corresponding relationship is a pre-set mapping relationship. For example, a one-to-one or many-to-one mapping set is formed between the power consumption and the correction factors preset in the database. When the real-time power consumption is input into this mapping set, the corresponding correction factor can be obtained. In this example, the value range of the correction factor is limited to [0,1].

[0078] The correction factor corresponding to the maximum workload set in the database is denoted as d3. The function of this correction factor is to quantify the influence of the maximum workload on the state evaluation value of the task. In practical applications, the correction factor can be directly obtained from the database, and its corresponding relationship is a pre-set mapping relationship. For example, a one-to-one or many-to-one mapping set is formed between the maximum workload and the correction factor preset in the database. When the real-time maximum workload is input into this mapping set, the corresponding correction factor can be obtained. In this example, the value range of the correction factor is limited to [0,1].

[0079] The correction factor corresponding to the task completion percentage set in the database is denoted as d4. The function of this correction factor is to quantify the degree of influence of the task completion percentage on the task status evaluation value. In practical applications, the correction factor can be directly obtained from the database, and its corresponding relationship is a pre-set mapping relationship. For example, a one-to-one or many-to-one mapping set is formed between the task completion percentage and the correction factor preset in the database. When the real-time task completion percentage is input into this mapping set, the corresponding correction factor can be obtained. In this example, the value range of the correction factor is limited to [0,1].

[0080] It should be noted that the state evaluation value of each robot arm performing each task in each monitoring time period is extracted and matched with the work efficiency of the robot arm corresponding to each interval of the state evaluation value of the task stored in the database. The work efficiency of each robot arm is preset in the database. When in use, the work efficiency of each robot arm can be directly obtained from the database. The corresponding relationship can be a pre-set mapping relationship. For example, the state evaluation value of each robot arm performing each task in each monitoring time period and the work efficiency of the robot arm in the database form a mapping set, and the real-time state evaluation value of each robot arm performing each task in each monitoring time period is input into the mapping set to obtain the work efficiency of each robot arm. The mapping relationship can be a one-to-one relationship. The work efficiency of the robot arm includes: high efficiency, general, and low efficiency. By evaluating the work efficiency of the robot arm, it helps to reduce task waiting time and execution conflicts, thereby improving the overall task execution efficiency.

[0081] Specifically, the task adaptation value of each robotic arm and the state evaluation value of each robotic arm performing each task in each monitoring time period are extracted, and the task execution compliance index of each robotic arm is obtained by processing. The task execution compliance index of each robotic arm is used to evaluate the stability of the robotic arm system.

[0082] The task execution compliance index of each robotic arm is extracted and matched with the system stability corresponding to each interval of the task execution compliance index of the robotic arm stored in the database to obtain the system stability of each task of each robotic arm. The system stability of each task of each robotic arm includes: stable, generally stable, and unstable.

[0083] When the stability of the system of each task of each robot arm is stable, the working data of the robot arm is continuously monitored; when the stability of the system of each task of each robot arm is generally stable or unstable, the load of the robot arm is adjusted.

[0084] It should be noted that the execution compliance index of each task of each manipulator is extracted and matched with the stability of the manipulator system corresponding to each interval of the state evaluation value of the task in the database. The stability of each manipulator system is preset in the database. When in use, the stability of each manipulator system can be directly obtained from the database. The corresponding relationship can be a pre-set mapping relationship. For example, the execution compliance index of each task of each manipulator and the stability of the manipulator system in the database form a mapping set, and the real-time execution compliance index of each task of each manipulator is input into the mapping set to obtain the stability of each manipulator system. The mapping relationship can be a one-to-one relationship. The stability of the manipulator system includes: stable, generally stable, and unstable. By evaluating the stability of the manipulator system and dynamically adjusting the load of the manipulator, it is helpful to optimize resource allocation.

[0085] It should be noted that adjusting the load of the robotic arm includes: when the stability of the robotic arm system is generally stable, extracting the workload of the robotic arm and comparing it with the load threshold of the robotic arm stored in the database; if the workload of the robotic arm is higher than or equal to the load threshold of the robotic arm, then reducing the workload of the robotic arm according to the preset workload reduction range stored in the database; if the workload of the robotic arm is lower than the load threshold of the robotic arm, continuously monitoring the working data of the robotic arm, for example: the preset workload reduction range is 10%, when the workload of the robotic arm is higher than or equal to the load threshold of the robotic arm, reducing the workload of the robotic arm by 10%; when the stability of the robotic arm system is unstable, reducing the workload of the robotic arm by half to adjust the load of the robotic arm.

[0086] Specifically, the task execution compliance index of each robotic arm is extracted and compared with the task execution compliance index threshold of each robotic arm stored in the database. If the task execution compliance index of a robotic arm is higher than the task execution compliance index threshold of the robotic arm, the task volume of the robotic arm is increased.

[0087] If the task execution compliance index of a certain robot arm is equal to the task execution compliance index threshold of the robot arm, there is no need to adjust the task volume of the robot arm.

[0088] If the task execution compliance index of a certain robot arm is lower than the task execution compliance index threshold of the robot arm, the task amount of the robot arm is reduced.

[0089] It should be noted that increasing the task volume of the robotic arm includes: extracting the task execution compliance index of each robotic arm and comparing it with the task execution compliance index threshold of the robotic arm in the database; if the task execution compliance index of a certain robotic arm is higher than the task execution compliance index threshold of the robotic arm, subtracting the task execution compliance index threshold of the robotic arm from the task execution compliance index of the robotic arm to obtain the difference, multiplying the difference by the current task volume of the robotic arm, rounding the result to an integer, and obtaining the task volume that can be increased for the robotic arm; if the task execution compliance index of a certain robotic arm is equal to the task execution compliance index threshold of the robotic arm, there is no need to modify the task volume of the robotic arm.

[0090] It should be noted that reducing the task volume of the robotic arm includes: extracting the task execution compliance index of each robotic arm and comparing it with the task execution compliance index threshold of the robotic arm in the database; if the task execution compliance index of a certain robotic arm is lower than the task execution compliance index threshold of the robotic arm, subtract the task execution compliance index threshold of the robotic arm from the task execution compliance index of the robotic arm to obtain the difference, multiply the difference by the current task volume of the robotic arm, round the result to an integer, and obtain the task volume that needs to be reduced for the robotic arm; allocating tasks according to the task volume that needs to be adjusted for each robotic arm, which helps to avoid excessive load or insufficient tasks, thereby improving resource utilization.

[0091] Specifically, the execution compliance index of each task of each robot arm is analyzed as follows:

[0092]

[0093] In the formula, ρ i→j It indicates the execution compliance index of the jth task of the i-th robot arm, I i→j represents the jth task adaptation value of the i-th robotic arm, represents the state evaluation value of the i-th robot performing the j-th task in the q-th monitoring time period, a1 represents the weight factor corresponding to the set task adaptation value, a2 represents the weight factor corresponding to the state evaluation value of the set task, i=1,2,...,m, i represents the number of each robot arm, m represents the total number of robot arms, j=1,2,...,n, j represents the number of each task, n represents the total number of tasks, q=1,2,...,r, q represents the number of each task, r represents the total number of tasks.

[0094] The correction factor corresponding to the task adaptation value set in the database is denoted as a1. The function of this correction factor is to quantify the influence of the task adaptation value on the task execution compliance index. In practical applications, the correction factor can be directly obtained from the database, and its corresponding relationship is a pre-set mapping relationship. For example, a one-to-one or many-to-one mapping set is formed between the task adaptation value and the correction factor preset in the database. When the real-time task adaptation value is input into this mapping set, the corresponding correction factor can be obtained. In this example, the value range of the correction factor is limited to [0,1].

[0095] The correction factor corresponding to the state evaluation value of the task set in the database is denoted as a2. The function of this correction factor is to quantify the influence of the state evaluation value of the task on the task execution compliance index. In practical applications, the correction factor can be directly obtained from the database, and its corresponding relationship is a pre-set mapping relationship. For example, a one-to-one or many-to-one mapping set is formed between the state evaluation value of the task and the correction factor preset in the database. When the state evaluation value of the real-time task is input into this mapping set, the corresponding correction factor can be obtained. In this example, the value range of the correction factor is limited to [0,1].

[0096] like Figure 2 As shown, Figure 2Represents the task adaptation value-task execution compliance index change curve, where the x-axis represents the j-th task execution compliance index of the i-th robot arm, and the y-axis represents the j-th task adaptation value of the i-th robot arm. Three different sets of example parameters are defined in the figure, corresponding to different situations of the three curves, represented by solid lines, dashed lines and dotted lines, respectively, and the corresponding curve labels are a, b, and c, respectively. Assume that the weight factor corresponding to the set task adaptation value is a1=0.55, and the weight factor corresponding to the set task state evaluation value is a2=0.8. When the q-th monitoring time period is the same as the q-th monitoring time period, the task execution compliance index of the j-th task is the same as the y-axis, and the y-axis represents the j-th task adaptation value of the i-th robot arm. When the sum of the state evaluation values ​​q of the i-th robot arm performing the j-th task is 0.8, the schematic diagram of the j-th task adaptation value of the i-th robot arm is shown as curve a. When the sum of the state evaluation values ​​q of the i-th robot arm performing the j-th task in the q-th monitoring time period is 1.0, the schematic diagram of the j-th task adaptation value of the i-th robot arm is shown as curve b. When the sum of the state evaluation values ​​q of the i-th robot arm performing the j-th task in the q-th monitoring time period is 1.2, the schematic diagram of the j-th task adaptation value of the i-th robot arm is shown as curve c.

[0097] As shown in Table 1, Table 1 is example data of the task execution compliance index, which lists the task adaptation value and the task execution compliance index when the total number of tasks is 1.

[0098] Table 1 Example data of task execution compliance index

[0099] Task adaptation value The task status evaluation value Task Execution Compliance Index 1.2 1.0 1.836 1.4 1.0 1.901 1.6 1.0 1.961

[0100] As shown in Table 1, in a specific embodiment, it is assumed that the weight factor a1 corresponding to the set task adaptation value is 0.55, and the weight factor a2 corresponding to the set task status evaluation value is 0.8. The task execution compliance index is jointly determined by the task adaptation value and the task status evaluation value. When the task adaptation value increases, the task execution compliance index increases accordingly.

[0101] Specifically, the second aspect of this article provides a multi-robot collaborative task allocation system based on a high-order network, including: a data acquisition and processing module, which is used to build a high-order network with each robot arm as a node, obtain the environmental information of each robot arm in the high-order network and the resource requirement data of each task, and obtain the task adaptation value of each robot arm based on the resource requirement data of each task and the environmental information of each robot arm.

[0102] The task allocation module is used to allocate tasks to each robotic arm according to the task adaptation value of each robotic arm and adjust each robotic arm.

[0103] The task adjustment module is used to monitor and obtain the interactive information of each task assigned to each robot arm and analyze it to obtain the compliance index of each task execution of each robot arm, and adjust the task assignment of each robot arm according to the compliance index of each task execution of each robot arm.

[0104] It should be noted that a high-order network is constructed through the data acquisition and processing module to collect the environmental information of the robotic arm and the resource requirement data of the task, and process them to obtain the adaptation value of each task of each robotic arm; the task allocation module reasonably allocates tasks according to the adaptation value and adjusts the robotic arm; the task adjustment module monitors the task execution of each robotic arm, analyzes and obtains the execution compliance index of each task of each robotic arm, and optimizes the task allocation according to the execution compliance index of each task of each robotic arm to ensure the best task execution effect, help optimize resource utilization, reduce conflicts and delays in task execution, and improve overall production efficiency and task execution quality.

[0105] It should be noted that the multi-robotic arm collaborative task allocation method and system based on a high-order network also includes a database to store and manage the resource requirement data of the task and the environmental information of the robotic arm. In this embodiment, the database is used to store the performance matching degree of each task of the robotic arm, the stability of the robotic arm system, the weight factor corresponding to the set task adaptation value, the weight factor corresponding to the set task state evaluation value, the reference value of the set average speed of the robotic arm, the reference value of the set average acceleration of the robotic arm, the set working radius error allowable value, the set load error allowable value, the correction factor corresponding to the set average speed of the robotic arm, the correction factor corresponding to the set average acceleration of the robotic arm, the correction factor corresponding to the set working radius error, the correction factor corresponding to the set load error, the reference value of the set power consumption, the reference value of the set power consumption, the reference value of the set maximum workload, the reference value of the set task completion percentage, the correction factor corresponding to the set power consumption, the correction factor corresponding to the set power consumption, the correction factor corresponding to the set maximum workload, the correction factor corresponding to the set task completion percentage, the working efficiency of the robotic arm, the task execution compliance index threshold of the robotic arm, the torque and power history improvement range of the robotic arm, and the preset workload reduction range.

[0106] It should be noted that, in this article, relational terms such as first and second, etc. are only used 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. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device.

[0107] The preferred embodiments of the present invention disclosed above are only used to help illustrate the present invention. The preferred embodiments do not describe all the details in detail, nor do they limit the invention to the specific implementation methods described. Obviously, many modifications and changes can be made according to the content of this specification. This specification selects and specifically describes these embodiments in order to better explain the principles and practical applications of the present invention, so that those skilled in the art can understand and use the present invention well. The present invention is limited only by the claims and their full scope and equivalents.

Claims

1. A multi-robot collaborative task allocation method based on a high-order network, characterized in that: include: A high-order network is constructed by taking each robotic arm as a node, obtaining the environmental information of each robotic arm and the resource requirement data of each task in the high-order network, and obtaining the adaptation value of each task of each robotic arm based on the resource requirement data of each task and the environmental information of each robotic arm; Allocate tasks to each robotic arm according to the adaptation value of each task of each robotic arm, and adjust each robotic arm; The interactive information of each task execution of each robot arm's task assignment is obtained and analyzed to obtain the compliance index of each task execution of each robot arm, and the task assignment of each robot arm is adjusted according to the compliance index of each task execution of each robot arm.

2. The multi-robot collaborative task allocation method based on a high-order network according to claim 1, characterized in that: The environmental information of each robot arm and the resource requirement data of each task in the high-order network are obtained, and the specific analysis is as follows: The environmental information of each robotic arm includes: the average speed and average acceleration of each adjacent robotic arm, the working radius of each robotic arm, the maximum weight that each robotic arm can bear, and the number of tasks currently executed by each robotic arm; The resource requirement data for each task include: the working radius of each task, the required load of each task; The acquired environmental information of each robot arm and the resource requirement data of each task are preprocessed to obtain preprocessed data, wherein the preprocessing includes processing of missing values, abnormal values ​​and repeated data.

3. The multi-robot collaborative task allocation method based on a high-order network according to claim 2 is characterized in that: Based on the resource requirement data of each task and the environmental information of each robotic arm, the adaptation value of each task of each robotic arm is obtained, and the specific analysis is as follows: The average speed, average acceleration, working radius and maximum load-bearing weight of each robotic arm after preprocessing are extracted, and the working radius and required load of each task are extracted to obtain the adaptation value of each robotic arm for each task. The adaptation value of each robotic arm for each task is used to evaluate the performance matching degree of each robotic arm for each task.

4. The multi-robot collaborative task allocation method based on a high-order network according to claim 1, characterized in that: The task allocation for each robot arm is specifically analyzed as follows: Extract the task adaptation value of each robot arm, sort the task adaptation values ​​of each robot arm from large to small, obtain the task adaptation ranking of each robot arm, assign tasks to each robot arm starting from the first place in the task adaptation ranking of each robot arm, and assign a robot arm to each task in turn according to the sorting result.

5. The multi-robot collaborative task allocation method based on a high-order network according to claim 1, characterized in that: The specific process of adjusting each robotic arm is as follows: The task adaptation values ​​of each robot arm are extracted and matched with the performance matching degrees of each task of the robot arm corresponding to each interval of the task adaptation values ​​of each robot arm stored in the database to obtain the performance matching degrees of each task of each robot arm. The performance matching degrees of each task of the robot arm include: excellent matching, good matching, medium matching, and poor matching. According to the performance matching degrees of each task of each robot arm, the torque and power output of the robot arm are adjusted.

6. The multi-robot collaborative task allocation method based on a high-order network according to claim 1, characterized in that: The monitoring obtains and analyzes the execution interaction information of each task assigned to each robot arm, and the specific analysis is as follows: Preset monitoring time periods, monitor the power consumption, power consumption and task progress of each robotic arm performing a task in each monitoring time period, obtain the power consumption and power consumption of each robotic arm performing each task in each monitoring time period, monitor the workload of each robotic arm performing a task in each monitoring time period, take the maximum workload of each robotic arm performing a task in each monitoring time period, obtain the maximum workload of each robotic arm performing each task in each monitoring time period, divide the task progress of each robotic arm performing a task in each monitoring time period by the total amount of tasks, and obtain the completion percentage of each task performed by each robotic arm in each monitoring time period; The electricity consumption, power consumption, maximum workload and completion percentage of each robotic arm performing each task in each monitoring time period are extracted, and the state evaluation value of each robotic arm performing each task in each monitoring time period is obtained by processing. The state evaluation value of each robotic arm performing each task in each monitoring time period is used to evaluate the work efficiency of the robotic arm.

7. The multi-robot collaborative task allocation method based on a high-order network according to claim 1, characterized in that: The execution compliance index of each task of each robotic arm is obtained, and the specific analysis is as follows: Extracting the adaptation value of each task of each robotic arm and the state evaluation value of each robotic arm performing each task in each monitoring time period, and processing them to obtain the execution compliance index of each task of each robotic arm, wherein the execution compliance index of each task of each robotic arm is used to evaluate the stability of the robotic arm system; Extracting each task execution compliance index of each robotic arm and matching it with the stability of the system corresponding to each interval of the task execution compliance index of the robotic arm stored in the database, to obtain the stability of the system of each task of each robotic arm, wherein the stability of the system of each task of each robotic arm includes: stable, generally stable, and unstable; When the stability of the system of each task of each robot arm is stable, the working data of the robot arm is continuously monitored; when the stability of the system of each task of each robot arm is generally stable or unstable, the load of the robot arm is adjusted.

8. The multi-robot collaborative task allocation method based on a high-order network according to claim 1, characterized in that: The task allocation of each robotic arm is adjusted according to the execution compliance index of each task of each robotic arm. The specific analysis is as follows: Extract each task execution compliance index of each robotic arm and compare it with the task execution compliance index threshold of each robotic arm stored in the database. If the task execution compliance index of a robotic arm is higher than the task execution compliance index threshold of the robotic arm, increase the task volume of the robotic arm. If the execution compliance index of each task of a certain robot arm is equal to the task execution compliance index threshold of the robot arm, there is no need to adjust the task volume of the robot arm; If the task execution compliance index of a certain robot arm is lower than the task execution compliance index threshold of the robot arm, the task amount of the robot arm is reduced.

9. The multi-robot collaborative task allocation method based on high-order network according to claim 7, characterized in that: The execution compliance index of each task of each robot arm is specifically analyzed as follows: In the formula, ρ i→j It indicates the execution compliance index of the jth task of the i-th robot arm, I i→j represents the jth task adaptation value of the i-th robotic arm, represents the state evaluation value of the i-th robot performing the j-th task in the q-th monitoring time period, a1 represents the weight factor corresponding to the set task adaptation value, a2 represents the weight factor corresponding to the state evaluation value of the set task, i=1,2,...,m, i represents the number of each robot arm, m represents the total number of robot arms, j=1,2,...,n, j represents the number of each task, n represents the total number of tasks, q=1,2,...,r, q represents the number of each task, r represents the total number of tasks.

10. A multi-robot collaborative task allocation system based on a high-order network, characterized in that: include: The data acquisition and processing module is used to construct a high-order network with each robotic arm as a node, obtain the environmental information of each robotic arm and the resource requirement data of each task in the high-order network, and obtain the adaptation value of each task of each robotic arm based on the resource requirement data of each task and the environmental information of each robotic arm; A task allocation module, used to allocate tasks to each robotic arm according to the task adaptation value of each robotic arm, and adjust each robotic arm; The task adjustment module is used to monitor and obtain the interactive information of each task assigned to each robot arm and analyze it to obtain the compliance index of each task execution of each robot arm, and adjust the task assignment of each robot arm according to the compliance index of each task execution of each robot arm.

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