Multi-manipulator collaborative task allocation method and system based on high-order network

Abstract

The present invention discloses a multi-manipulator collaborative task allocation method and system based on a high-order network, which relates to the field of automated control technology. First, a high-order network is constructed with each manipulator as a node, and the state of each manipulator and the target position vector are integrated to construct an energy function reflecting the distance relationship between the end effector and the target. By analyzing the energy function, the state of each manipulator and the target position vector, a gradient descent function is derived. Based on the gradient descent function and the adjustment parameters of the angular velocity of the manipulator, the angular velocity expression of each manipulator is obtained. At the same time, the communication, scalar state, activation state and neighbor set of the manipulator are comprehensively monitored, and the task allocation is dynamically adjusted according to the monitoring results to optimize the overall task execution effect. The present invention helps to improve the collaborative operation efficiency of the manipulator system, realize the reasonable allocation of tasks and the flexible scheduling of manipulators, thereby ensuring the optimal configuration of resources and helping to avoid excessive or insufficient resource allocation.

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 intelligent systems, multi-robot systems are demonstrating significant value in manufacturing, logistics, healthcare, and service sectors. By enabling the coordinated operation of multiple robotic arms, distributed multi-robot collaborative systems can efficiently and accurately complete complex tasks such as multi-part assembly, dynamic sorting, and flexible manipulation in heterogeneous environments. However, task allocation and collaborative control between robotic arms remain core challenges in system design, directly impacting system efficiency, reliability, and performance.

[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 including 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 collaborative task scheduling of multiple robotic arms, 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, judging 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; if not, 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, and significantly reduce the overall efficiency of the system. Summary of the Invention

[0006] In response to the deficiencies of the prior art, the present invention provides a multi-robot 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-manipulator collaborative task allocation method based on a high-order network, including: constructing a high-order network with each manipulator as a node, obtaining the state data and target position vector of each manipulator in the high-order network, and constructing an energy function of the relationship between the position of the end effector of each manipulator and the target distance based on the state data and the target position vector.

[0008] The energy function of the relationship between the position of each robot end effector and the target distance, the state of each robot arm and the target position vector are analyzed to derive the gradient descent function.

[0009] The adjustment parameters of the angular velocity of the robotic arm are obtained by integrating the gradient descent function to obtain the angular velocity expression of each robotic arm.

[0010] The communication status of each robotic arm, the scalar status maintained by each robotic arm, the activation status of each robotic arm and the neighbor set of each robotic arm are monitored and processed. Tasks are assigned to each robotic arm based on the processing results, and the angular velocity expression of each robotic arm is optimized. At the same time, the angular velocity of each robotic arm is limited, and the movement speed of each robotic arm is adjusted.

[0011] Furthermore, the status data of each robotic arm includes: the x-axis and y-axis coordinates of the base of each robotic arm, the angle information of each joint of each robotic arm, and the length of each connecting rod of each robotic arm.

[0012] Furthermore, the x- and y-axis coordinates of each robotic arm base, the angle information of each joint of each robotic arm, and the length of each connecting rod of each robotic arm are extracted, and the position vector of each robotic arm end effector is obtained through processing. The target position vector is monitored, and the energy function of the relationship between the position of each robotic arm end effector and the target distance is obtained in combination with the position vector of each robotic arm end effector.

[0013] Furthermore, the energy function of the relationship between the length of each link of each robotic arm, the angle of each joint of each robotic arm, and the position of the end effector of each robotic arm and the target distance is extracted to obtain a gradient descent function described by a differential equation related to the position of the end effector of each robotic arm, the target distance and the angle of each robotic arm. The real-time angle state of each robotic arm is controlled by evolving along the gradient descent direction.

[0014] Furthermore, the gradient descent function is extracted and combined with the adjustment parameters of the angular velocity of the robotic arm to obtain the angular velocity expression of each robotic arm.

[0015] Furthermore, the communication status of each robotic arm, the scalar state maintained by each robotic arm, the activation state of each robotic arm and the neighbor set of each robotic arm are extracted and processed to obtain the derivative of the scalar state maintained by each robotic arm with respect to time and the derivative of the activation state of each robotic arm with respect to time.

[0016] Furthermore, the energy function of the relationship between the position of the end effector of each robotic arm and the target distance and the adjustment parameters of the external input of the winner-takes-all model preset in the database are extracted and processed as the external input of the winner-takes-all model. By introducing the winner-takes-all model, the activation state of each robotic arm is obtained, and tasks are assigned to each robotic arm according to the activation state of each robotic arm.

[0017] Furthermore, the angular velocity expression of each robotic arm is extracted, and combined with the activation state of each robotic arm, the optimized angular velocity expression of each robotic arm is obtained, and the angular velocity of each robotic arm is restricted.

[0018] Furthermore, the optimized expression of the angular velocity of each robotic arm is extracted to obtain the optimized estimated value of the angular velocity of each robotic arm. The optimized estimated value of the angular velocity of each robotic arm is compared with the maximum reference value and the minimum reference value of the angular velocity of the robotic arm stored in the database, and the optimized estimated limit value of the angular velocity of each robotic arm is obtained according to the restriction expression of the optimized estimated value of the angular velocity of each robotic arm. The angular velocity of each robotic arm is determined according to the comparison result. If the optimized estimated value of the angular velocity of a certain robotic arm is greater than the maximum reference value of the angular velocity of the robotic arm stored in the database, the optimized estimated limit value of the angular velocity of the robotic arm is taken as the maximum reference value of the angular velocity of the robotic arm. If the optimized estimated value of the angular velocity of a certain robotic arm is less than the minimum reference value of the angular velocity of the robotic arm stored in the database, the optimized estimated limit value of the angular velocity of the robotic arm is taken as the minimum reference value of the angular velocity of the robotic arm. If the optimized estimated value of the angular velocity of a certain robotic arm is between the minimum reference value and the maximum reference value of the angular velocity of the robotic arm stored in the database, the optimized estimated value of the angular velocity of the certain robotic arm is used as the optimized estimated limit value of the angular velocity of the robotic arm.

[0019] When the optimized estimated value of the angular velocity of a certain robotic arm is greater than the maximum reference value of the angular velocity of the robotic arm, the movement speed of the robotic arm is reduced by limiting the angular velocity. When the optimized estimated value of the angular velocity of a certain robotic arm is less than the minimum reference value of the angular velocity of the robotic arm, the movement speed of the robotic arm is reduced by limiting the angular velocity. When the optimized estimated value of the angular velocity of a certain robotic arm is between the minimum reference value and the maximum reference value of the angular velocity of the robotic arm, the movement speed of the robotic arm is not adjusted.

[0020] Furthermore, the second aspect of this paper provides a multi-manipulator collaborative task allocation system based on a high-order network, including:

[0021] The energy function construction module is used to construct a high-order network with each robotic arm as a node, obtain the state data and target position vector of each robotic arm in the high-order network, and construct an energy function of the relationship between the position of the end effector of each robotic arm and the target distance based on the state data and the target position vector.

[0022] The gradient descent function construction module is used to analyze the energy function of the relationship between the position of each robot end effector and the target distance, the state of each robot arm, and the target position vector to derive the gradient descent function.

[0023] The module for deriving the angular velocity expression of each robotic arm is used to obtain the adjustment parameters of the angular velocity of the robotic arm and integrate the gradient descent function to obtain the angular velocity expression of each robotic arm.

[0024] The robot arm status monitoring and angular velocity optimization module is used to monitor and process the communication status of each robot arm, the scalar status maintained by each robot arm, the activation status of each robot arm and the neighbor set of each robot arm. It assigns tasks to each robot arm based on the processing results, optimizes the angular velocity expression of each robot arm, limits the angular velocity of each robot arm, and adjusts the movement speed of each robot arm.

[0025] The present invention has the following beneficial effects:

[0026] (1) The present invention constructs a high-order network with each manipulator as a node, integrates the state of each manipulator and the target position vector, and constructs an energy function that reflects the distance relationship between the end effector and the target. By analyzing the energy function, the state of each manipulator and the target position vector, a gradient descent function is derived. Based on the gradient descent function and the adjustment parameters of the angular velocity of the manipulator, the angular velocity expression of each manipulator is obtained. At the same time, the communication, scalar state, activation state and neighbor set of the manipulator are comprehensively monitored, and task allocation is dynamically adjusted according to the monitoring results to optimize the overall task execution effect. The present invention helps to improve the collaborative operation efficiency of the manipulator system, realize the reasonable allocation of tasks and the flexible scheduling of manipulators, thereby ensuring the optimal configuration of resources and helping to avoid excessive or insufficient resource allocation.

[0027] (2) The present invention extracts the state data and target position vector of each robotic arm, constructs an energy function that describes the relationship between the position of the end effector of each robotic arm and the target distance, and designs an evolution rule based on the gradient descent method to calculate the estimated value of the angular velocity of each robotic arm, which helps to optimize the motion trajectory of the robotic arm and improve the efficiency and accuracy of multiple robotic arms when completing tasks in a collaborative manner.

[0028] (3) The present invention extracts the communication status, scalar status, activation status and neighbor set information of each robotic arm, combines the energy function with the winner-takes-all model, and processes the activation status of each robotic arm, thereby effectively improving the flexibility of task allocation and the efficiency of collaborative work of the robotic arms, ensuring the optimal configuration of system resources and the precise execution of tasks.

[0029] (4) The present invention achieves accurate tracking of the target by calculating the difference between the target position and the position of the end effector of each robotic arm and optimizing the angular velocity expression of each robotic arm, combined with the activation state and angle limit, while ensuring that the movement speed of each robotic arm is within a safe range, which helps to ensure the reliability and safety of the robotic arm operation and improves the stability of the overall system and the efficiency of task execution.

[0030] 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

[0031] Figure 1 Schematic diagram of the process of the present invention.

[0032] Figure 2 Schematic diagram of task allocation for the robotic arm.

[0033] Figure 3 Schematic diagram of the system flow of the present invention. DETAILED DESCRIPTION

[0034] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. 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 making creative efforts are within the scope of protection of the present invention.

[0035] In the description of the present invention, it should be understood that the terms "opening", "upper", "lower", "thickness", "top", "middle", "length", "inside", "around" and the like indicating orientation or positional relationship are only for the convenience of describing the present invention and simplifying the description, and do not indicate or imply 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.

[0036] See also Figure 1 An embodiment of the present invention provides a technical solution: a multi-manipulator collaborative task allocation method and system based on a high-order network, including constructing a high-order network with each manipulator as a node, obtaining the state data and target position vector of each manipulator in the high-order network, and constructing an energy function of the relationship between the position of the end effector of each manipulator and the target distance based on the state data and the target position vector.

[0037] The energy function of the relationship between the position of each robot end effector and the target distance, the state of each robot arm and the target position vector are analyzed to derive the gradient descent function.

[0038] The adjustment parameters of the angular velocity of the robotic arm are obtained by integrating the gradient descent function to obtain the angular velocity expression of each robotic arm.

[0039] The communication status of each robotic arm, the scalar status maintained by each robotic arm, the activation status of each robotic arm and the neighbor set of each robotic arm are monitored and processed. Tasks are assigned to each robotic arm based on the processing results, and the angular velocity expression of each robotic arm is optimized. At the same time, the angular velocity of each robotic arm is limited, and the movement speed of each robotic arm is adjusted.

[0040] It should be noted that a high-order network is constructed with each robotic arm as a node, and the state of each robotic arm and the target position vector are integrated to construct an energy function to reflect the distance relationship between the end effector and the target. By analyzing this energy function, the state of each robotic arm and the target position vector, the gradient descent function is derived. By integrating the gradient descent function and the adjustment parameters of the angular velocity of the robotic arm, the angular velocity expression of each robotic arm is determined, and the communication, scalar state, activation state and neighbor set of each robotic arm are comprehensively monitored to dynamically adjust the task allocation. By accurately calculating the energy function and the gradient descent function and comprehensive state monitoring, resource utilization is optimized, ensuring flexible scheduling of the robotic arm and optimal resource allocation, thereby significantly improving the overall operation efficiency and task execution effect.

[0041] It should be noted that each robotic arm is constructed as a node in a high-order network: each robotic arm is represented 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 robotic arms is not represented by ordinary edges, but rather by hyperedges. Hyperedges are an extension of graph theory that can connect multiple nodes, not just two. Therefore, hyperedges in a high-order network can be used to represent the collaborative relationship between multiple robotic arms, reflecting how these robotic arms collaborate in certain tasks or operations. High-order network: In traditional graphs, each edge connects two nodes, while in a high-order network, hyperedges connect multiple nodes. This structure can better express complex collaborations or relationships, especially when multiple robotic arms are simultaneously involved in a task. The collaborative relationship between multiple robotic arms is modeled using hyperedges in a high-order network. Each robotic arm is treated as a node, and the collaboration between multiple robotic arms is described using hyperedges that include these robotic arms.

[0042] It should be noted that the present invention also includes the introduction of a competitive strategy through a "winner takes all" mechanism, so that each robotic arm can dynamically participate in the task allocation process based on its own state. The "winner takes all" mechanism refers to a strategy that prioritizes the allocation of resources or tasks to the node with the best performance or the strongest capability, thereby achieving optimization of resource utilization and efficient task collaboration. High-order networks can flexibly express the resource competition relationship and task allocation priority between multiple robotic arms. For example, through high-order network modeling, the complex interactions between the robotic arms due to shared resources can be more intuitively captured, and these relationships can be effectively quantified and optimized. By combining the "winner takes all" mechanism, the distributed high-order network model can achieve efficient task allocation in a dynamic environment, significantly improve the flexibility and computational efficiency of task allocation while reducing communication overhead, use high-order networks to model the complex resource competition relationship between multiple robotic arms, and dynamically optimize task allocation through local information interaction strategies to achieve global performance optimization, thereby significantly reducing communication complexity and improving the real-time and scalability of the system.

[0043] Specifically, the status data of each robotic arm includes: the x-axis and y-axis coordinates of the base of each robotic arm, the angle information of each joint of each robotic arm, and the length of each connecting rod of each robotic arm.

[0044] It should be noted that the x-axis and y-axis coordinates of the base of each robotic arm include the x-axis coordinates of the base of each robotic arm and the y-axis coordinates of the base of each robotic arm. The x-axis coordinate of the base of each robotic arm is horizontal to the base of the robotic arm and usually moves along the front and back direction of the robotic arm. The y-axis coordinate of the base of each robotic arm is perpendicular to the X-axis and usually moves along the up and down direction of the robotic arm. The angle information of each joint of each robotic arm means that the robotic arm is usually composed of multiple joints, and these joints realize the movement of the robotic arm by rotation. The angle information of the joint refers to the rotation angle of each joint relative to its adjacent joint; the length of the connecting rod refers to the straight-line distance between two adjacent joints.

[0045] It should be noted that the position of each robotic arm base is recorded, and the angle information of each joint of each robotic arm is collected in real time using a joint angle sensor; and a laser rangefinder is used to measure the length of each connecting rod of each robotic arm.

[0046] Specifically, the x- and y-axis coordinates of each robotic arm base, the angle information of each joint of each robotic arm, and the length of each connecting rod of each robotic arm are extracted, and the position vector of each robotic arm end effector is obtained through processing. The target position vector is monitored, and the energy function of the relationship between the position of each robotic arm end effector and the target distance is obtained in combination with the position vector of each robotic arm end effector.

[0047] It should be noted that the target position vector is a vector defined in a specific coordinate system, which is used to describe the exact position of a target point in space. The target position vector is determined by arranging infrared sensors in the work area to monitor the spatial coordinates or distance information of the target object.

[0048] It should be noted that the x, y axis coordinates and joint angle information of each robotic arm base are extracted, the end effector position vector is calculated in combination with the connecting rod length, the target position vector is monitored, and an energy function of the relationship between the end effector position of each robotic arm and the target distance is constructed based on this. By constructing an energy function to accurately quantify the distance relationship between the end effector and the target, it helps to improve the accuracy and rationality of task allocation, and enhance the overall work efficiency and task execution quality.

[0049] It should be noted that the energy function of the relationship between the position of each robot end effector and the target distance is analyzed as follows:

[0050]

[0051] Where, represents the position vector of the end effector of the i-th robotic arm, x i (θ i )∈R represents the x-axis coordinate of the position of the end effector of the i-th robot arm, R represents a real number set, y i (θ i )∈R represents the y-axis coordinate of the end effector position of the i-th robotic arm, x i0 Indicates the position of the i-th robot base on the x-axis, y i0 Indicates the position of the i-th robot base on the y-axis, L ik represents the length of the kth link of the i-th robotic arm, θ ic ∈R represents the angle of the cth joint of the i-th robotic arm, f i ∈R represents the energy function of the relationship between the position of the end effector of the i-th manipulator and the target distance, which includes rational and irrational numbers, p d ∈R m is the target position vector, m represents the dimension, m=2, c=1,2,...,a, c represents the number of each joint of the robot arm, a represents the total number of joints of the robot arm, k takes a value between 1 and a, i=1,2,...,n, i represents the number of each robot arm, and n represents the total number of robot arms.

[0052] Specifically, the energy function of the relationship between the length of each link of each robotic arm, the angle of each joint of each robotic arm, and the position of the end effector of each robotic arm and the target distance is extracted to obtain the gradient descent function described by the differential equation related to the position of each robotic arm end effector, the target distance and the angle of each robotic arm. The real-time angle state of each robotic arm is controlled by evolving along the gradient descent direction.

[0053] It should be noted that the gradient descent method is an optimization algorithm used to minimize the objective function in an iterative manner. The basic idea is to adjust the parameters along the gradient direction of the objective function so that the value of the objective function gradually decreases and eventually reaches a local or global minimum. It evolves along the direction of the gradient descent of the objective function until the minimum point is reached.

[0054] It should be noted that the gradient descent function described by the differential equation related to the position of each robot end effector, the target distance and the angle of each robot arm is specifically expressed as follows:

[0055]

[0056] In the formula The partial derivative of the energy function representing the relationship between the position of the end effector of the i-th manipulator and the target distance with respect to the angle of the i-th manipulator, J i ∈R m×a is the Jacobian matrix, represents the partial derivative of the position vector of the end effector of the i-th robot arm with respect to the angle of the i-th robot arm, L ik represents the length of the kth link of the i-th robotic arm, θ ic ∈R represents the angle of the cth joint of the i-th robotic arm, represents the transpose of the Jacobian matrix, p d ∈R m is the target position vector, p i ∈R m represents the position vector of the end effector of the i-th robot arm, R represents a set of real numbers, m represents the dimension, and m = 2, c = 1, 2, ..., a, c represents the number of each joint of the robot arm, a represents the total number of joints of a single robot arm, k takes a value between 1 and a, i = 1, 2, ..., n, i represents the number of each robot arm, and n represents the total number of robot arms.

[0057] It should be noted that the Jacobian matrix is ​​an m×a matrix, where m is the dimension of the function output and a is the dimension of the function input. For a function f that maps from an a-dimensional space to an m-dimensional space, its Jacobian matrix is ​​an m×a matrix, where each element is the partial derivative of function f with respect to a component of the input vector.

[0058] Specifically, the gradient descent function is extracted and combined with the adjustment parameters of the angular velocity of the robotic arm to obtain the angular velocity expression of each robotic arm.

[0059] It should be noted that the high-order network model can effectively coordinate the task allocation between the robotic arms, which helps to optimize the motion path of each robotic arm, avoid conflicts and ensure the smooth completion of collaborative tasks.

[0060] It should be noted that the angular velocity expressions of each robotic arm are used to obtain the estimated angular velocity of each robotic arm. The specific analysis process is as follows:

[0061]

[0062] Where, represents the estimated value of the angular velocity of the i-th robot arm, c0 (c0>0) is the preset adjustment parameter of the angular velocity of the robot arm, The partial derivative of the energy function representing the relationship between the position of the end effector of the i-th manipulator and the target distance with respect to the angle of the i-th manipulator, represents the transpose of the Jacobian matrix, p i ∈R m represents the position vector of the end effector of the i-th robotic arm, p d ∈R m is the target position vector, m represents the dimension, m=2, i=1,2,...,n, i represents the number of each robot arm, and n represents the total number of robots.

[0063] Specifically, the communication status of each robotic arm, the scalar state maintained by each robotic arm, the activation state of each robotic arm and the neighbor set of each robotic arm are extracted and processed to obtain the derivative of the scalar state maintained by each robotic arm with respect to time and the derivative of the activation state of each robotic arm with respect to time.

[0064] It should be noted that the communication status, scalar status, activation status and neighbor set information of each robotic arm are extracted and processed, and further processed to obtain the time derivatives of the scalar state and activation state of each robotic arm to deeply understand the dynamic behavior of the robotic arm.

[0065] It should be noted that the communication status of each robotic arm refers to the communication connection status between each robotic arm and the control system, other robotic arms or external devices; the scalar state maintained by each robotic arm refers to some key parameters maintained by each robotic arm during operation. These parameters or status information are usually expressed in the form of scalars, such as numerical values ​​or Boolean values; the activation status of each robotic arm refers to whether each robotic arm is in an activated state; the neighbor set of each robotic arm refers to the set of other robotic arms that directly communicate with the current robotic arm.

[0066] It should be noted that network monitoring tools (such as Wireshark, etc.) are used to capture and analyze communication data packets between the robotic arm and the control system or other robotic arms, so as to obtain the communication status of each robotic arm; the scalar status information of the robotic arm is obtained through the monitoring interface (such as API) provided by the robotic arm; a status feedback mechanism can be designed through the robotic arm, and the specific status code or signal returned can be analyzed to indicate whether it is in an activated state; by recording and analyzing the communication records between robotic arms, other robotic arms with which the current robotic arm frequently communicates can be identified, thereby determining the neighbor set.

[0067] It should be noted that the derivative of the scalar state maintained by each robotic arm with respect to time is analyzed as follows:

[0068]

[0069] Where, express The derivative of the estimated value with respect to time, γ is the adjustment parameter of the preset robot arm communication state, A ij ∈{0,1} indicates whether the i-th robot arm communicates with the j-th robot arm, 0 indicates no communication between the i-th robot arm and the j-th robot arm, 1 indicates communication between the i-th robot arm and the j-th robot arm, ρ i for The valuation of j for Valuation of A ji ∈{0,1} indicates whether the j-th robot arm communicates with the i-th robot arm, z i ∈R represents the activation state of the i-th robot arm, z j ∈R represents the activation state of the j-th robot arm, R represents a set of real numbers, z i >0 means activated, z i =0, indicating that it is not activated. is the scalar state maintained by the i-th robot arm, is the scalar state maintained by the j-th robot, represents the time derivative of the scalar state maintained by the i-th robot, t represents time, i = 1, 2, ..., n, i represents the number of each robot, n represents the total number of robots, j = 1, 2, ..., n, j represents the number of each robot, n represents the total number of robots.

[0070] Specifically, the energy function of the relationship between the position of the end effector of each robotic arm and the target distance and the adjustment parameters of the external input of the winner-takes-all model preset in the database are extracted and processed as the external input of the winner-takes-all model. By introducing the winner-takes-all model, the activation status of each robotic arm is obtained, and tasks are assigned to each robotic arm according to the activation status of each robotic arm.

[0071] It should be noted that the winner-takes-all model is a model that describes the phenomenon that a few leading players in a competitive market obtain the main resources or returns, or even almost monopolize the market. This model is usually used to analyze market competition, resource allocation, reward mechanisms and other fields, especially in environments with significant network effects and scale effects. In the collaborative task allocation of multiple robotic arms, there is usually resource competition between different robotic arms. By introducing the winner-takes-all model, the robotic arms that complete the task most effectively can be selected, and tasks can be assigned to these robotic arms first, which helps to improve the collaborative efficiency of the system.

[0072] It should be noted that the derivative of the activation state of each robot arm with respect to time is analyzed as follows:

[0073]

[0074] Where, represents the derivative of the activation state of the i-th robot arm with respect to time, α represents the adjustment parameter of the preset winner-takes-all model, and f i ∈R represents the energy function of the relationship between the position of the end effector of the i-th manipulator and the target distance, λ is the external input adjustment parameter of the preset winner-takes-all model, is the external input of the winner-takes-all model, A ij ∈{0,1} indicates whether the i-th robot arm communicates with the j-th robot arm, 0 indicates that the i-th robot arm does not communicate with the j-th robot arm, 1 indicates that the i-th robot arm communicates with the j-th robot arm, z i ∈R represents the activation state of the i-th robot arm, z j ∈R represents the activation state of the jth robot arm, R represents a set of real numbers, i = 1, 2, ..., n, i represents the number of each robot arm, n represents the total number of robot arms, j = 1, 2, ..., n, j represents the number of each robot arm, n represents the total number of robot arms.

[0075] It should be noted that when the task target starts to move, the derivative of the activation state of each manipulator with respect to time is processed to obtain the rate of change of the activation state of each manipulator with time; Figure 2 The figure shows a schematic diagram of task allocation for each robotic arm. The horizontal line represents the moving trajectory of the target task point. When the target task point starts to move, the activation status of each robotic arm is updated in real time. The robotic arm closest to the target point (robotic arm 1) takes over the task first. As the position of the target point changes, the system calculates the relative distance between the robotic arm and the target point in real time and dynamically adjusts the activation status of each robotic arm. The new "winning" robotic arm (such as robotic arm 2) takes over the task, and so on.

[0076] Specifically, the angular velocity expression of each robotic arm is extracted, and combined with the activation state of each robotic arm, the optimized angular velocity expression of each robotic arm is obtained, and the angular velocity of each robotic arm is restricted.

[0077] It should be noted that the angular velocity optimization expression of each robotic arm combines the activation state of the robotic arm, the gradient descent function, and the adjustment parameters of the angular velocity of the robotic arm preset in the database, thereby realizing the precise tracking of the target by the robotic arm, while limiting the angular velocity change to ensure that the maximum rotation speed of the robotic arm is not exceeded, thereby enhancing the stability of the system and the efficiency of task execution.

[0078] It should be noted that the optimized expression of the angular velocity of each robotic arm is used to obtain the optimized estimated value of the angular velocity of each robotic arm. The specific analysis process is as follows:

[0079]

[0080] Where, represents the optimized estimated value of the angular velocity of the i-th manipulator, c0 (c0>0) is the preset adjustment parameter of the angular velocity of the manipulator, z i represents the activation state of the i-th robotic arm, represents the transpose of the Jacobian matrix, p d ∈R m is the target position vector, p i ∈R m Represents the position vector of the end effector of the i-th robotic arm, m represents the dimension, and takes m=2, i=1,2,...,n, i represents the number of each robotic arm, and n represents the total number of robotic arms.

[0081] Specifically, the angular velocity optimization expression of each robotic arm is extracted to obtain the optimized estimated value of the angular velocity of each robotic arm. The optimized estimated value of the angular velocity of each robotic arm is compared with the maximum reference value and the minimum reference value of the angular velocity of the robotic arm stored in the database, and the optimized estimated limit value of the angular velocity of each robotic arm is obtained according to the optimized estimated value limit expression of the angular velocity of each robotic arm. The angular velocity of each robotic arm is determined according to the comparison result. If the optimized estimated value of the angular velocity of a certain robotic arm is greater than the maximum reference value of the angular velocity of the robotic arm stored in the database, the optimized estimated limit value of the angular velocity of the robotic arm is taken as the maximum reference value of the angular velocity of the robotic arm. If the optimized estimated value of the angular velocity of a certain robotic arm is less than the minimum reference value of the angular velocity of the robotic arm stored in the database, the optimized estimated limit value of the angular velocity of the robotic arm is taken as the minimum reference value of the angular velocity of the robotic arm. If the optimized estimated value of the angular velocity of a certain robotic arm is between the minimum reference value and the maximum reference value of the angular velocity of the robotic arm stored in the database, the optimized estimated value of the angular velocity of the certain robotic arm is used as the optimized estimated limit value of the angular velocity of the robotic arm.

[0082] It should be noted that the optimized estimated value of the angular velocity of a certain robotic arm is between the minimum reference value and the maximum reference value of the angular velocity of the robotic arm stored in the database, including the optimized estimated value of the angular velocity of a certain robotic arm being equal to the minimum reference value of the angular velocity of the robotic arm or equal to the maximum reference value.

[0083] It should be noted that when the optimized estimated value of the angular velocity of a certain robotic arm is greater than the maximum reference value of the angular velocity of the robotic arm, the movement speed of the robotic arm is reduced by limiting the angular velocity; when the optimized estimated value of the angular velocity of a certain robotic arm is less than the minimum reference value of the angular velocity of the robotic arm, the movement speed of the robotic arm is reduced by limiting the angular velocity; when the optimized estimated value of the angular velocity of a certain robotic arm is between the minimum reference value and the maximum reference value of the angular velocity of the robotic arm, the movement speed of the robotic arm is not adjusted.

[0084] It should be noted that by comparing the current angular velocity of each robotic arm with the maximum and minimum reference values ​​stored in the database, it is ensured that the angular velocity of each robotic arm is limited to a safe range, thereby preventing out-of-range movement, ensuring stable operation of the system, and improving the safety and reliability of task execution.

[0085] It should be noted that the minimum reference value for angular velocity is a negative value. For example, the angular velocity has +5 and -5, where positive and negative indicate direction, a positive value indicates counterclockwise rotation, and a negative value indicates clockwise rotation. The number indicates the angular size of the unit of rotation per second. Therefore, +5 means that the object robot arm is rotating counterclockwise at an angle of 5 units per second, and -5 means that the object is rotating clockwise at an angle of 5 units per second. Therefore, when the angular velocity of a robot arm is less than the minimum reference value of the angular velocity of the robot arm, the movement speed of the robot arm is reduced by limiting the angular velocity. The movement speed here is understood as a positive value. By limiting the angular velocity, the overall movement speed of the robot arm is affected. The angular velocity of the robot arm will be adjusted according to the preset minimum and maximum reference values ​​to ensure that its movement speed is within a safe range. When it exceeds or falls below these reference values, the movement speed will be reduced by limiting the angular velocity, while within the reference value range, the original speed will be maintained, which will help improve the working efficiency of the robot arm and reduce manual intervention.

[0086] It should be noted that the optimized estimated value limit expression of the angular velocity of each robotic arm is used to obtain the optimized estimated limit value of the angular velocity of each robotic arm. The specific analysis is:

[0087]

[0088] Where, represents the optimized estimated limit value of the angular velocity of the i-th robot arm, Indicates the maximum reference value of the angular velocity of the robotic arm stored in the database. Indicates the minimum reference value of the angular velocity of the robotic arm stored in the database, i=1,2,...,n, i represents the number of each robotic arm, and n represents the total number of robotic arms.

[0089] Specifically, the second aspect of this article provides a multi-robotic arm collaborative task allocation system based on a high-order network, including: an energy function construction module, which is used to construct a high-order network with each robotic arm as a node, obtain the state data and target position vector of each robotic arm in the high-order network, and construct an energy function of the relationship between the position of the end effector of each robotic arm and the target distance based on the state data and the target position vector.

[0090] The gradient descent function construction module is used to analyze the energy function of the relationship between the position of each robot end effector and the target distance, the state of each robot arm, and the target position vector to derive the gradient descent function.

[0091] The module for deriving the angular velocity expression of each robotic arm is used to obtain the adjustment parameters of the angular velocity of the robotic arm and integrate the gradient descent function to obtain the angular velocity expression of each robotic arm.

[0092] The robot arm status monitoring and angular velocity optimization module is used to monitor and process the communication status of each robot arm, the scalar status maintained by each robot arm, the activation status of each robot arm and the neighbor set of each robot arm. It assigns tasks to each robot arm based on the processing results, optimizes the angular velocity expression of each robot arm, limits the angular velocity of each robot arm, and adjusts the movement speed of each robot arm.

[0093] It should be noted that in the multi-manipulator collaborative task allocation method based on high-order networks, the energy function construction module is used to accurately quantify the relationship between the position of each manipulator's end effector and the target distance, the gradient descent function construction module accurately calculates the manipulator's adjustment direction and amplitude, and the angular velocity expression derivation module of each manipulator derives the operating angle of each manipulator based on this, while the state monitoring and angular velocity optimization module of each manipulator adjusts the angular velocity in real time and limits its range, ensuring that the system completes the collaborative task efficiently and safely, significantly improving the overall performance and reliability.

[0094] 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 status data of each robotic arm. In this embodiment, the database is used to store the preset adjustment parameters of the robotic arm's angular velocity, the preset adjustment parameters of the external input of the winner-takes-all model, the preset adjustment parameters of the winner-takes-all model, the preset adjustment parameters of the robotic arm's communication status, the maximum reference value of the robotic arm's angular velocity, and the minimum reference value of the robotic arm's angular velocity.

[0095] It should be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that includes a list of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, method, article, or apparatus.

[0096] The preferred embodiments of the present invention disclosed above are intended only to help illustrate the present invention. These preferred embodiments do not exhaustively describe all details, nor do they limit the present invention to the specific embodiments described. Obviously, many modifications and variations are possible based on the content of this specification. These embodiments are selected and described in detail in this specification to better explain the principles and practical applications of the present invention, thereby enabling those skilled in the art to better understand and utilize the present invention. 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 using each robotic arm as a node. The state data and target position vector of each robotic arm in the high-order network are obtained. Based on the state data and target position vector, an energy function is constructed to determine the relationship between the position of each robotic arm's end effector and the target distance. The energy function of the relationship between the position of each robot end effector and the target distance, the state of each robot arm and the target position vector are analyzed to derive the gradient descent function; Obtain the adjustment parameters of the angular velocity of the robotic arm and integrate the gradient descent function to obtain the angular velocity expression of each robotic arm; The communication status of each manipulator, the scalar status maintained by each manipulator, the activation status of each manipulator, and the neighbor set of each manipulator are monitored and processed. Tasks are assigned to each manipulator based on the processing results. The angular velocity expression of each manipulator is optimized, the angular velocity of each manipulator is limited, and the movement speed of each manipulator is adjusted. The task allocation for each robotic arm is specifically analyzed as follows: an energy function of the relationship between the position of the end effector of each robotic arm and the target distance and an adjustment parameter of the external input of the winner-takes-all model preset in the database are extracted and processed as the external input of the winner-takes-all model, the activation state of each robotic arm is obtained by introducing the winner-takes-all model, and the task is allocated to each robotic arm according to the activation state of each robotic arm; The angular velocity of each robotic arm is limited, and the movement speed of each robotic arm is adjusted. The specific analysis is as follows: extract the angular velocity optimization expression of each robotic arm to obtain the optimized estimated value of the angular velocity of each robotic arm, compare the optimized estimated value of the angular velocity of each robotic arm with the maximum reference value and the minimum reference value of the angular velocity of the robotic arm stored in the database, and limit the expression of the optimized estimated value of the angular velocity of each robotic arm to obtain the optimized estimated limit value of the angular velocity of each robotic arm, determine the angular velocity of each robotic arm according to the comparison result, if the optimized estimated value of the angular velocity of a robotic arm is greater than the value stored in the database If the maximum reference value of the angular velocity of the robotic arm is stored in the database, the optimized estimated limit value of the angular velocity of the robotic arm is taken as the maximum reference value of the angular velocity of the robotic arm; if the optimized estimated value of the angular velocity of a robotic arm is less than the minimum reference value of the angular velocity of the robotic arm stored in the database, the optimized estimated limit value of the angular velocity of the robotic arm is taken as the minimum reference value of the angular velocity of the robotic arm; if the optimized estimated value of the angular velocity of a robotic arm is between the minimum reference value and the maximum reference value of the angular velocity of the robotic arm stored in the database, the optimized estimated value of the angular velocity of the robotic arm is taken as the optimized estimated limit value of the angular velocity of the robotic arm; When the optimized estimated value of the angular velocity of a certain robotic arm is greater than the maximum reference value of the angular velocity of the robotic arm, the movement speed of the robotic arm is reduced by limiting the angular velocity. When the optimized estimated value of the angular velocity of a certain robotic arm is less than the minimum reference value of the angular velocity of the robotic arm, the movement speed of the robotic arm is reduced by limiting the angular velocity. When the optimized estimated value of the angular velocity of a certain robotic arm is between the minimum reference value and the maximum reference value of the angular velocity of the robotic arm, the movement speed of the robotic arm is not adjusted.

2. The multi-robot collaborative task allocation method based on a high-order network according to claim 1, characterized in that: The state data of each robot arm in the high-order network is obtained, and the specific analysis is as follows: The status data of each robotic arm includes: the x and y axis coordinates of each robotic arm base, the angle information of each joint of each robotic arm, and the length of each connecting rod of each robotic arm.

3. The multi-robot collaborative task allocation method based on a high-order network according to claim 1, characterized in that: The energy function of the relationship between the position of each robot end effector and the target distance is constructed based on the state data and the target position vector. The specific analysis is as follows: The x- and y-axis coordinates of each robotic arm base, the angle information of each robotic arm joint, and the length of each robotic arm link are extracted and processed to obtain the position vector of each robotic arm end effector. The target position vector is monitored and combined with the position vector of each robotic arm end effector to obtain the energy function of the relationship between the position of each robotic arm end effector and the target distance.

4. The multi-robot collaborative task allocation method based on a high-order network according to claim 1, characterized in that: The energy function of the relationship between the position of each manipulator end effector and the target distance, the state of each manipulator and the target position vector are analyzed to derive the gradient descent function. The specific analysis is as follows: The energy function of the relationship between the length of each link of each robotic arm, the angle of each joint of each robotic arm, and the position of the end effector of each robotic arm and the target distance is extracted to obtain the gradient descent function described by differential equations related to the position of each robotic arm end effector, the target distance and the angle of each robotic arm. The real-time angle state of each robotic arm is controlled by evolving along the gradient descent direction.

5. The multi-robot collaborative task allocation method based on a high-order network according to claim 4 is characterized in that: The real-time angle state of each robotic arm is controlled by evolving along the gradient descent direction. The specific process is as follows: The gradient descent function is extracted and combined with the adjustment parameters of the angular velocity of the robotic arm to obtain the angular velocity expression of each robotic arm.

6. The multi-robot collaborative task allocation method based on a high-order network according to claim 1, characterized in that: The communication status of each robotic arm, the scalar status maintained by each robotic arm, the activation status of each robotic arm, and the neighbor set of each robotic arm are monitored and processed. The specific analysis is as follows: The communication status of each manipulator, the scalar status maintained by each manipulator, the activation status of each manipulator and the neighbor set of each manipulator are extracted and processed to obtain the time derivative of the scalar status maintained by each manipulator and the time derivative of the activation status of each manipulator.

7. The multi-robot collaborative task allocation method based on a high-order network according to claim 1, characterized in that: The above optimization of the angular velocity expressions of each manipulator is specifically analyzed as follows: The angular velocity expression of each robotic arm is extracted and combined with the activation state of each robotic arm to obtain the optimized angular velocity expression of each robotic arm, and the angular velocity of each robotic arm is restricted.

8. A multi-manipulator collaborative task allocation system based on a high-order network, applying the multi-manipulator collaborative task allocation method based on a high-order network as claimed in any one of claims 1 to 7, characterized in that: include: An energy function construction module is used to construct a high-order network using each robotic arm as a node, obtain the state data and target position vector of each robotic arm in the high-order network, and construct an energy function of the relationship between the position of each robotic arm's end effector and the target distance based on the state data and the target position vector; A gradient descent function construction module is used to analyze the energy function of the relationship between the position of each robot end effector and the target distance, the state of each robot arm, and the target position vector to derive the gradient descent function; The module for deriving the angular velocity expression of each manipulator arm is used to obtain the adjustment parameters of the angular velocity of the manipulator arm and integrate the gradient descent function to obtain the angular velocity expression of each manipulator arm; The robot arm status monitoring and angular velocity optimization module is used to monitor and process the communication status of each robot arm, the scalar status maintained by each robot arm, the activation status of each robot arm and the neighbor set of each robot arm. It assigns tasks to each robot arm based on the processing results, optimizes the angular velocity expression of each robot arm, limits the angular velocity of each robot arm, and adjusts the movement speed of each robot arm.

Images