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 arm collaborative task allocation method based on a high-order network, including: building a high-order network with each robot arm as a node, obtaining the state data and target position vector of each robot arm in the high-order network, and based on the state data and the target position vector, constructing an energy function of the relationship between the position of the end effector of each robot arm and the target distance.

[0008] The energy function of the relationship between the position of each robot end effector and the target distance, the state of each robot, 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, and tasks are assigned to each robotic arm according to the processing results. The angular velocity expression of each robotic arm is optimized, 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: x-axis and y-axis coordinates of the base of each robotic arm, angle information of each joint of each robotic arm, and 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 connecting rod 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, and 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 expressions of each robotic arm.

[0015] Furthermore, 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 are extracted and processed to obtain the time derivative of the scalar status maintained by each robot arm and the time derivative of the activation status of each robot arm.

[0016] Furthermore, the energy function of the relationship between the end effector position 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. The activation state of each robotic arm is obtained by introducing the winner-takes-all model, and tasks are assigned to each robotic arm according to its activation state.

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

[0018] Further, 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, and 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 limit expression of the optimized estimated value of the angular velocity of each robotic arm, and 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 robot arm is greater than the maximum reference value of the angular velocity of the robot arm, the movement speed of the robot arm is reduced by limiting the angular velocity. When the optimized estimated value of the angular velocity of a certain 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. When the optimized estimated value of the angular velocity of a certain robot arm is between the minimum reference value and the maximum reference value of the angular velocity of the robot arm, the movement speed of the robot arm is not adjusted.

[0020] Furthermore, the second aspect of this paper provides a multi-robot 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 building 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, 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 state monitoring and angular velocity optimization module is used to monitor and process the communication state of each robot arm, the scalar state maintained by each robot arm, the activation state of each robot arm and the neighbor set of each robot arm. It assigns tasks to each robot arm according to 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 robot arm as a node, integrates the state of each robot arm and the target position vector, constructs an energy function that reflects the distance relationship between the end effector and the target, and derives the gradient descent function by analyzing the energy function, the state of each robot arm and the target position vector. Based on the gradient descent function and the adjustment parameters of the angular velocity of the robot arm, the angular velocity expression of each robot arm is processed. At the same time, the communication, scalar state, activation state and neighbor set of the robot arm are fully 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 robot arm system, realize the reasonable allocation of tasks and the flexible scheduling of the robot arm, 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 collaboration.

[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 robot arm and optimizing the angular velocity expression of each robot arm, combined with the activation state and angle limit, while ensuring that the movement speed of each robot arm is within a safe range, which helps to ensure the reliability and safety of the robot 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 It is a schematic diagram of the process of the present invention.

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

[0033] Figure 3 It is a schematic diagram of the system flow of the present invention. DETAILED DESCRIPTION

[0034] 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.

[0035] 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.

[0036] 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 state data and target position vector of each robotic arm in the high-order network, and constructing 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.

[0037] The energy function of the relationship between the position of each robot end effector and the target distance, the state of each robot, 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, and tasks are assigned to each robotic arm according to the processing results. The angular velocity expression of each robotic arm is optimized, 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. The gradient descent function is derived by analyzing the energy function, the state of each robotic arm and the target position vector. The angular velocity expression of each robotic arm is determined by integrating the gradient descent function and the adjustment parameters of the angular velocity of the robotic arm. The communication, scalar state, activation state and neighbor set of each robotic arm are fully monitored to dynamically adjust the task allocation. Through the precise calculation of the energy function and the gradient descent function and comprehensive state monitoring, resource utilization is optimized to ensure 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 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.

[0042] It should be noted that the present invention also includes introducing a competition strategy through a "winner takes all" mechanism, so that each robot can dynamically participate in the task allocation process based on its own state. The "winner takes all" mechanism refers to a strategy that preferentially allocates resources or tasks to the nodes with the best performance or the strongest capabilities, thereby optimizing resource utilization and improving the efficiency of task collaboration. High-order networks can flexibly express the resource competition relationship and task allocation priority between multiple robot arms. For example, through high-order network modeling, the complex interactions between the robot 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 robot arms, and dynamically optimize task allocation through local information interaction strategies to achieve global performance optimization, thereby greatly 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 each robotic arm base include the x-axis coordinates of each robotic arm base and the y-axis coordinates of each robotic arm base. The x-axis coordinate of each robotic arm base 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 each robotic arm base 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 by 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 working 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 accordingly. 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 improve 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] In the formula, represents the position vector of the end effector of the i-th robot 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 position of the end effector of the i-th robot 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 ith robot arm, θ ic ∈R represents the angle of the cth joint of the ith robot arm, f i ∈R represents the energy function of the relationship between the position of the end effector of the i-th robot 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, a represents the total number of joints of the robot, k takes a value between 1 and a, i=1,2,...,n, i represents the number of each robot, and n represents the total number of robots.

[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 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.

[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 in 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 end effector position of each robot arm, the target distance and the angle of each robot arm is specifically expressed as:

[0055]

[0056] In the formula The partial derivative of the energy function representing the relationship between the position of the end effector of the ith robot and the target distance with respect to the angle of the ith robot, 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 ith robot arm, θ ic ∈R represents the angle of the cth joint of the ith robot 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, 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, and each element in the matrix is ​​the partial derivative of the 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, the angular velocity expressions of each robotic arm are obtained.

[0059] It should be noted that the high-order network model can effectively coordinate the task allocation among 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] In the formula, represents the estimated angular velocity of the i-th robot arm, c 0 (c 0 >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 ith robot and the target distance with respect to the angle of the ith robot, represents the transpose of the Jacobian matrix, p i ∈R m represents the position vector of the end effector of the i-th robot 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 robot arms.

[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 time derivative of the scalar state maintained by each robotic arm and the time derivative of the activation state of each robotic arm.

[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 in order 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 communicate directly 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 state 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 robot arm with respect to time is analyzed as follows:

[0068]

[0069] In the formula, express The estimated derivative of 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 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, ρ i for The valuation of j for The 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 jth robot, R represents a real number set, z i >0 means activated, z i =0, indicating that it is not activated. is the scalar state maintained by the i-th robot, 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 end effector position 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. The activation state of each robotic arm is obtained by introducing the winner-takes-all model, and tasks are assigned to each robotic arm according to its activation state.

[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 task allocation of multi-robot collaboration, there is usually resource competition among different robots. By introducing the winner-takes-all model, the robot arms that can complete the task most effectively can be selected, and tasks can be assigned to these robots first, which helps to improve the system collaboration efficiency.

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

[0073]

[0074] In the formula, 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 robot 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. 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 real number set, 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 robot arm with respect to time is processed to obtain the activation state change rate of each robot arm with time; Figure 2As shown in the figure, the task allocation diagram for each robotic arm is shown. 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 over time. The robotic arm closest to the target point (robotic arm 1) has priority to perform the task. 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 angular velocity optimization 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 angular velocity optimization expression 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] In the formula, represents the optimized estimated value of the angular velocity of the i-th robot arm, c 0 (c 0 >0) is the preset adjustment parameter of the angular velocity of the robot arm, z i represents the activation state of the i-th robot 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, 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 robot arms.

[0081] Specifically, an optimized expression for the angular velocity of each robotic arm is extracted to obtain an 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 a maximum reference value and a minimum reference value of the angular velocity of the robotic arm stored in a database; and an optimized estimated limit value of the angular velocity of each robotic arm is obtained according to a limit 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 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 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.

[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 angle of the unit of rotation per second. Therefore, +5 means that the object robot 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] In the formula, 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 robot arm stored in the database. It represents 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-robot 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 robot as a node, obtain the state data and target position vector of each robot in the high-order network, and construct an energy function of the relationship between the end effector position of each robot and the target distance based on the state data and the target position vector.

[0090] The gradient descent function building 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, 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 state monitoring and angular velocity optimization module is used to monitor and process the communication state of each robot arm, the scalar state maintained by each robot arm, the activation state of each robot arm and the neighbor set of each robot arm. It assigns tasks to each robot arm according to 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-robot 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 robot arm's end effector and the target distance, the gradient descent function construction module accurately calculates the robot arm's adjustment direction and amplitude, and each robot arm's angular velocity expression derivation module derives the operating angle of each robot arm based on this, while each robot arm's state monitoring and angular velocity optimization module 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 angular velocity of the robotic arm, 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 communication status, the maximum reference value of the angular velocity of the robotic arm, and the minimum reference value of the angular velocity of the robotic arm.

[0095] 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.

[0096] 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 robot arm as a node, obtaining the state data and target position vector of each robot arm in the high-order network, and constructing an energy function of the relationship between the position of the end effector of each robot arm and the target distance based on the state data and the target position vector; The energy function of the relationship between the position of each robot end effector and the target distance, the state of each robot, 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 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, and tasks are assigned to each robotic arm according to the processing results. The angular velocity expression of each robotic arm is optimized, the angular velocity of each robotic arm is limited, and the movement speed of each robotic arm is 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 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.

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 robot end effector and the target distance, the state of each robot 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 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.

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 robot arm is controlled by evolving along the gradient descent direction. The specific process is: 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, and the specific analysis is as follows: 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 are extracted and processed to obtain the time derivative of the scalar status maintained by each robot arm and the time derivative of the activation status of each robot arm.

7. 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: The energy function of the relationship between the end-effector position 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. The activation state of each robotic arm is obtained by introducing the winner-takes-all model, and tasks are assigned to each robotic arm according to its activation state.

8. 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 robot arm 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, the optimized angular velocity expression of each robotic arm is obtained, and the angular velocity of each robotic arm is limited.

9. The multi-robot collaborative task allocation method based on a high-order network according to claim 1, characterized in that: The angular velocity of each robot arm is limited, and the movement speed of each robot arm is adjusted. The specific analysis is as follows: Extract the optimized expression of the angular velocity of each mechanical arm to obtain the optimized estimated value of the angular velocity of each mechanical arm, compare the optimized estimated value of the angular velocity of each mechanical arm with the maximum reference value and the minimum reference value of the angular velocity of the mechanical arm stored in the database, and obtain the optimized estimated limit value of the angular velocity of each mechanical arm according to the limit expression of the optimized estimated value of the angular velocity of each mechanical arm, and determine the angular velocity of each mechanical arm according to the comparison result. If the optimized estimated value of the angular velocity of a certain mechanical arm is greater than the maximum reference value of the angular velocity of the mechanical arm stored in the database, then the optimized estimated limit value of the angular velocity of the mechanical arm is taken as the maximum reference value of the angular velocity of the mechanical arm. If the optimized estimated value of the angular velocity of a certain mechanical arm is less than the minimum reference value of the angular velocity of the mechanical arm stored in the database, then the optimized estimated limit value of the angular velocity of the mechanical arm is taken as the minimum reference value of the angular velocity of the mechanical arm. If the optimized estimated value of the angular velocity of a certain mechanical arm is between the minimum reference value and the maximum reference value of the angular velocity of the mechanical arm stored in the database, then the optimized estimated value of the angular velocity of the certain mechanical arm is taken as the optimized estimated limit value of the angular velocity of the mechanical arm. When the optimized estimated value of the angular velocity of a certain robot arm is greater than the maximum reference value of the angular velocity of the robot arm, the movement speed of the robot arm is reduced by limiting the angular velocity. When the optimized estimated value of the angular velocity of a certain 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. When the optimized estimated value of the angular velocity of a certain robot arm is between the minimum reference value and the maximum reference value of the angular velocity of the robot arm, the movement speed of the robot arm is not adjusted.

10. A multi-robot collaborative task allocation system based on a high-order network, 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 the end effector of each robotic arm and the target distance based on the state data and the target position vector; A gradient descent function building 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, and the target position vector to derive the gradient descent function; 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; The robot arm state monitoring and angular velocity optimization module is used to monitor and process the communication state of each robot arm, the scalar state maintained by each robot arm, the activation state of each robot arm and the neighbor set of each robot arm. It assigns tasks to each robot arm according to 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