Distributed dynamic task allocation method and device, equipment and storage medium

Abstract

The invention discloses a distributed dynamic task allocation method and device, equipment and a storage medium, and relates to the technical field of multi-robot control, the method is applied to a multi-robot system, and the method comprises the following steps: obtaining communication state information of each mobile robot, and determining minimum communication topological graph information by adopting a preset distributed minimum spanning tree algorithm; inputting the system parameters and the minimum communication topological graph information into a preset k-WTA network calculation formula to obtain activation signals corresponding to the mobile robots; and obtaining position information of the to-be-tracked target equipment and the mobile robots, and performing task allocation on the mobile robots according to the position information and the activation signals. According to the method, the complex communication topology in the multi-robot system is simplified through the minimum spanning tree algorithm, and calculation is performed based on the minimum communication topological graph information, so that the consumption of calculation resources is reduced, and the task allocation efficiency of each mobile robot is improved.

Description

Technical Field

[0001] This application relates to the field of multi-robot control, and particularly to a distributed dynamic task allocation method, device, equipment, and storage medium. Background Technique

[0002] Multi-robot systems are becoming increasingly important in scientific research and engineering fields due to their distributed characteristics, higher efficiency, robustness, adaptability, and excellent scalability and flexibility. In a multi-robot system, dynamic task allocation can allocate tasks to the most suitable robots according to changes in factors such as the environment and location, thereby improving overall efficiency.

[0003] However, the existing dynamic task allocation algorithms in multi-robot systems do not fully consider the complexity of the communication topology structure composed of mobile robots in real application scenarios. In the case of redundant connections in the communication topology, these algorithms will perform unnecessary node information calculations. That is, when the existing dynamic task allocation algorithms perform task allocation, the communication topology information they rely on may contain hundreds of node information, resulting in a large amount of redundant calculations during the calculation process, thus causing waste of computing resources and memory resources and reducing the overall efficiency of the algorithm. Summary of the Invention

[0004] The main purpose of this application is to provide a distributed dynamic task allocation method, device, equipment, and storage medium, aiming to solve the technical problem that the existing dynamic task allocation algorithms in multi-robot systems rely on complex communication topology structures and have redundant calculations, resulting in low overall efficiency of the algorithms.

[0005] To achieve the above objective, this application proposes a distributed dynamic task allocation method, which is applied to a multi-robot system. The multi-robot system includes: at least one target device to be tracked and several mobile robots. The method includes: Obtain the communication status information of each mobile robot, and based on the communication status information, use a preset distributed minimum spanning tree algorithm to determine the minimum communication topology graph information; Initialize the system parameters of each mobile robot, and input the system parameters and the minimum communication topology graph information into a preset k-WTA network calculation formula to obtain the activation signals corresponding to each mobile robot; Obtain the position information of the target device to be tracked and each mobile robot, and perform task allocation for each mobile robot according to the position information and the activation signals.

[0006] In an embodiment, the step of using a preset distributed minimum spanning tree algorithm to determine the minimum communication topology graph information based on the communication status information includes: Determine a number of communication edges of each of the mobile robots according to the communication status information, and determine the minimum communication edge among the communication edges; Obtain the minimum communication edge corresponding to each of the mobile robots, construct a minimum communication topology graph according to each of the minimum communication edges, and determine minimum communication topology graph information according to the weight information of each of the minimum communication edges.

[0007] In one embodiment, the step of determining a number of communication edges of each of the mobile robots according to the communication status information and determining the minimum communication edge among the communication edges includes: Determine a number of communication edges of each of the mobile robots according to the communication status information, and determine the communication edge with the minimum communication cost as the minimum communication edge to be verified; Perform message verification on the minimum communication edge to be verified, and determine the minimum communication edge to be verified as the minimum communication edge when the verification passes.

[0008] In one embodiment, the minimum communication topology graph information is the minimum communication edge weight; The step of initializing the system parameters of each of the mobile robots and inputting the system parameters and the minimum communication topology graph information into a preset k-WTA network calculation formula to obtain the activation signal corresponding to each of the mobile robots includes: Initialize the system parameters of each of the mobile robots, where the system parameters include: a first system parameter, a second system parameter, a first state variable parameter, a second state variable parameter, and a preset network input parameter; Input the first system parameter, the second system parameter, the first state variable parameter, the second state variable parameter, the preset network input parameter, and the minimum communication edge weight into a preset k-WTA network calculation formula to obtain the activation signal corresponding to each of the mobile robots; Wherein, the preset k-WTA network calculation formula is:

[0009] In the formula, is the first system parameter, is the second system parameter, is the first state variable parameter, is the second state variable parameter, is the preset network input parameter, .) is a preset projection function, is the total number of each of the mobile robots, k is the number of mobile robots performing tasks, is the activation signal corresponding to each of the mobile robots, is the minimum communication edge weight; Among them, the calculation formula of the preset network input parameter is as follows:

[0010] In the formula, is the position vector of each of the mobile robots, is the position vector of the target device to be tracked.

[0011] In one embodiment, the step of task - allocating each of the mobile robots according to the position information and the activation signal includes: Determine the task - allocation speed of each of the mobile robots according to the position information and the activation signal; Task - allocate each of the mobile robots according to each of the task - allocation speeds; Among them, the calculation formula of the task - allocation speed is: +

[0012] In the formula, is the task - allocation speed of each of the mobile robots, is the preset error feedback gain parameter, is the robot control parameter, is the position vector of each of the mobile robots, is the position vector of the target device to be tracked, is the moving speed of the target device to be tracked.

[0013] In one embodiment, the calculation formula of the robot control parameter is:

[0014] In the formula, x is the position difference between the position vector of each of the mobile robots and the position vector of the target device to be tracked, and r is the preset feedback parameter.

[0015] In one embodiment, the step of task - allocating each of the mobile robots according to each of the task - allocation speeds includes: Control each of the mobile robots to move at the corresponding task - allocation speed until at least one mobile robot tracks the target device to be tracked, and then control each of the mobile robots to stop moving.

[0016] In addition, to achieve the above object, the present application also proposes a distributed dynamic task - allocation device, and the device includes: A topology optimization module, configured to obtain the communication status information of each mobile robot, and based on the communication status information, determine the minimum communication topology graph information by using a preset distributed minimum spanning tree algorithm; A task determination module, configured to initialize the system parameters of each mobile robot, and input the system parameters and the minimum communication topology graph information into a preset k-WTA network calculation formula to obtain the activation signals corresponding to the mobile robots; A task allocation module, configured to obtain the position information of the target device to be tracked and each mobile robot, and perform task allocation for each mobile robot according to the position information and the activation signals.

[0017] In addition, to achieve the above object, the present application further provides a distributed dynamic task allocation device, including: a memory, a processor, and a distributed dynamic task allocation program stored on the memory and executable on the processor, where when the distributed dynamic task allocation program is executed by the processor, the steps of the distributed dynamic task allocation method as described above are implemented.

[0018] In addition, to achieve the above object, the present application further provides a storage medium, on which a distributed dynamic task allocation program is stored, where when the distributed dynamic task allocation program is executed by a processor, the steps of the distributed dynamic task allocation method as described above are implemented.

[0019] The present application discloses a distributed dynamic task allocation method, which is applied to a multi-robot system including at least one target device to be tracked and several mobile robots. The method includes: obtaining the communication status information of each mobile robot, and based on the communication status information, determining the minimum communication topology graph information by using a preset distributed minimum spanning tree algorithm; initializing the system parameters of each mobile robot, and inputting the system parameters and the minimum communication topology graph information into a preset k-WTA network calculation formula to obtain the activation signals corresponding to the mobile robots; obtaining the position information of the target device to be tracked and each mobile robot, and performing task allocation for each mobile robot according to the position information and the activation signals. By using the minimum spanning tree algorithm, the present application simplifies the complex communication topology in the multi-robot system, generates a communication topology graph with the minimum communication cost, and then inputs the minimum communication topology graph information into a preset k-WTA network calculation formula, greatly reducing the consumption of computing resources and being beneficial to improving the task allocation efficiency of each mobile robot. Description of the Drawings

[0020] The drawings here are incorporated into the specification and form a part of the specification, showing the embodiments consistent with the present application, and are used together with the specification to explain the principles of the present application.

[0021] To more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the accompanying drawings required for the description of the embodiments or the prior art. Obviously, for those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0022] Figure 1 It is a schematic flowchart of the first embodiment of the distributed dynamic task allocation method of the present application; Figure 2 It is a schematic flowchart of the second embodiment of the distributed dynamic task allocation method of the present application; Figure 3 It is a schematic flowchart of the full process of the distributed dynamic task allocation method of the present application; Figure 4 It is an operation data flow diagram of the target tracking task stage; Figure 5 It is a structural block diagram of the first embodiment of the distributed dynamic task allocation device of the present application; Figure 6 It is a structural schematic diagram of the distributed dynamic task allocation device of the present application.

[0023] The implementation, functional features and advantages of the purpose of the present application will be further described with reference to the embodiments and the accompanying drawings. Specific Embodiments

[0024] It should be understood that the specific embodiments described herein are only used to explain the technical solutions of the present application and are not used to limit the present application.

[0025] To better understand the technical solutions of the present application, the following will be described in detail in combination with the accompanying drawings of the specification and specific embodiments.

[0026] The embodiments of the present application provide a distributed dynamic task allocation method, refer to Figure 1 , Figure 1 It is a schematic flowchart of the first embodiment of the distributed dynamic task allocation method of the present application. In this embodiment, the method is applied to a multi-robot system, and the multi-robot system includes: at least one target device to be tracked and several mobile robots. The method includes: steps S10 to S30: Step S10: Obtain the communication status information of each of the mobile robots, and based on the communication status information, determine the minimum communication topology graph information by using a preset distributed minimum spanning tree algorithm.

[0027] It should be noted that the execution subject of the method in this embodiment can be a computing service device with functions of task allocation, data processing, network communication, and program operation, such as a tablet computer, a personal computer, a multi-robot master control server, etc., or other electronic devices that can implement the same or similar functions. Here, a distributed dynamic task allocation device (referred to as the "allocation device" for short) is used as an example to explain each embodiment of the present application.

[0028] It should be understood that in a multi-robot system, dynamic task allocation is a process of allocating tracking tasks to the first k mobile robots that are closest to the target device to be tracked as the positions of the mobile robots and the target device to be tracked change. The number of the target devices to be tracked can be one or greater than one, and this embodiment does not limit this.

[0029] It can be understood that the target device to be tracked and each mobile robot in the multi-robot system can be connected through a network to form a k-WTA network. Each mobile robot or target device to be tracked in the k-WTA network can be a node in the communication topology corresponding to the multi-robot system. Then the above communication status information can be the adjacent relationship of each node and the message passing relationship between each node and its neighbor nodes.

[0030] It should also be noted that the preset distributed minimum spanning tree algorithm can be the GHS algorithm (a distributed minimum spanning tree algorithm proposed by Gallager, Humblet, and Spira). The GHS algorithm is an algorithm for constructing a minimum spanning tree (MST) in an asynchronous distributed system (such as a multi-robot system). By using the GHS algorithm, without a global clock or a synchronization mechanism, nodes can independently construct a tree structure (Minimum-Weight Spanning Tree, MST) that connects all nodes at any time, and the total edge weight of this structure is the smallest.

[0031] In specific implementation, by applying the GHS algorithm to obtain the MST to simplify the communication topology of the multi-robot system, the minimum communication topology graph information of the multi-robot system can be obtained. Thus, redundant communication in the subsequent task allocation algorithm is eliminated, which is beneficial to saving computing resources.

[0032] Step S20: Initialize the system parameters of each of the mobile robots, and input the system parameters and the minimum communication topology graph information into a preset k-WTA network calculation formula to obtain activation signals corresponding to each of the mobile robots.

[0033] It should be noted that the input parameter of the preset k-WTA network is a parameter negatively correlated with the distance, so that the function of the preset k-WTA network is converted from the conventional one of finding k maximum values in a data set to k minimum values. Through the calculation result of the preset k-WTA network calculation formula, it can be determined whether each mobile robot should move to perform the tracking task.

[0034] It should also be noted that the minimum communication topology graph information is used to describe the minimum communication topology in the multi-robot system, and it can be the minimum communication edge weight in the minimum communication topology.

[0035] Therefore, step S20 specifically includes: steps S201 to S202: Step S201: Initialize the system parameters of each mobile robot, where the system parameters include: the first system parameter, the second system parameter, the first state variable parameter, the second state variable parameter, and the preset network input parameter.

[0036] Step S202: Input the first system parameter, the second system parameter, the first state variable parameter, the second state variable parameter, the preset network input parameter, and the minimum communication edge weight into the preset k-WTA network calculation formula to obtain the activation signal corresponding to each mobile robot.

[0037] Among them, the preset k-WTA network calculation formula is:

[0038] In the formula, is the first system parameter, is the second system parameter, is the first state variable parameter, is the second state variable parameter, is the preset network input parameter (k-WTA network input parameter), .) is the preset projection function, is the total number of each mobile robot, k is the number of mobile robots performing the task, is the set of neighbor nodes of the i-th node, is the activation signal corresponding to each mobile robot, is the minimum communication edge weight. respectively represent the derivative with respect to time t .

[0039] Among them, the calculation formula of the preset network input parameter is:

[0040] In the formula, is the position vector of each of the mobile robots, is the position vector of the target device to be tracked.

[0041] It can be understood that in this embodiment, i is used as the corresponding number of each mobile robot, and j is used as the corresponding number of the adjacent mobile robots of each mobile robot in the minimum communication topology. The mobile robot i sends the state variable , , and receives the state variables ， 。

[0042] It should be noted that the above-mentioned preset k-WTA network calculation formula describes the information interaction relationship of three variables ( , ) corresponding to each mobile robot. And , and k can all be parameters given by the allocation device, where k represents selecting the top k mobile robots closest to the target device to be tracked to perform tasks. For the activation signal , = 1 indicates that the i-th mobile robot is one of the top k mobile robots closest to the target and the i-th mobile robot is activated, = 0 indicates that the i-th mobile robot remains stationary.

[0043] It should also be noted that in the above formula are the values of the diagonal elements in the Laplacian matrix L corresponding to the minimum communication topology of the multi-robot system, and the values of the non-diagonal elements in the Laplacian matrix L are 0. can reflect the communication cost between each mobile robot and its adjacent mobile robots, and is usually related to network channel bandwidth, etc. in practical applications.

[0044] Compared with the existing method that depends on the original communication topology of all adjacent mobile robots of each mobile robot when calculating the activation signal of the mobile robot, this embodiment uses the GHS algorithm to obtain a new communication topology, simplifies the communication topology result of the system, eliminates redundant communication under the condition of maintaining connectivity, minimizes the communication cost, simplifies the calculation complexity of the above-mentioned preset k-WTA network calculation formula, and improves the overall efficiency.

[0045] Step S30: Obtain the position information of the target device to be tracked and each of the mobile robots, and perform task allocation for each of the mobile robots according to the position information and the activation signal.

[0046] It should be noted that when after determining the k mobile robots that need to perform the tracking task among the mobile robots based on the activation signal, the mobile robots can be further controlled to move to the target device to be tracked according to the position information of the target device to be tracked and the mobile robots. Therefore, step S30 specifically includes steps S301 to S303: Step S301: Obtain the position information of the target device to be tracked and each of the mobile robots.

[0047] It should be understood that the position information of the target device to be tracked and each mobile robot can be obtained through a radar or a distance detector, and the position information of the target device to be tracked can be represented as a position vector , and the position information of each mobile robot can be represented as a position vector .

[0048] It should also be noted that the moving speed of the target device to be tracked can also be obtained through a radar and can be represented as .

[0049] Step S302: Determine the task assignment speed of each of the mobile robots according to the position information and the activation signal.

[0050] Specifically, the following calculation formula for the task assignment speed can be used for calculation, and the calculation formula for the task assignment speed is: +

[0051] In the formula, is the task assignment speed of each mobile robot, is the preset error feedback gain parameter, is the robot control parameter, is the position vector of each mobile robot, is the position vector of the target device to be tracked, is the moving speed of the target device to be tracked.

[0052] Furthermore, in practical applications, the upper and lower limits of the speed of each mobile robot during actual movement can also be restricted by a preset saturation function to simulate the situation where the moving speed is restricted in the actual scenario. At this time, the above calculation formula for the task assignment speed can be updated as: + In the formula, is the preset saturation function. ​

[0053] Among them, it can be defined as the calculation formula of the robot control parameters and is expressed as follows:

[0054] In the formula, x is the position difference between the position vector of each of the mobile robots and the position vector of the target device to be tracked, and r is a preset feedback parameter.

[0055] Step S303: Perform task allocation for each of the mobile robots according to each of the task allocation speeds.

[0056] In a specific implementation, the allocation device can control each mobile robot to move at the corresponding task allocation speed until at least one mobile robot tracks the target device to be tracked, and then control each of the mobile robots to stop moving, completing the tracking task of the mobile robot target tracking.

[0057] In this embodiment, the minimum spanning tree algorithm is used to simplify the complex communication topology in the multi-robot system, generating a communication topology graph with the minimum communication cost. Then, the minimum communication topology graph information is input into the preset k-WTA network calculation formula, greatly reducing the computational resource consumption of the preset k-WTA network calculation formula, and thus helping to improve the overall efficiency of task allocation for each mobile robot.

[0058] Based on the first embodiment of the present application, in the second embodiment of the present application, the same or similar content as in the above-mentioned embodiment one can be referred to the above introduction and will not be repeated hereinafter. On this basis, please refer to Figure 2 , Figure 2 which is the flow schematic diagram of the second embodiment of the distributed dynamic task allocation method of the present application.

[0059] In this embodiment, in order to specifically illustrate how to obtain the minimum communication topology corresponding to the multi-robot system by using the GHS algorithm, step S10 specifically includes: steps S101~S102: Step S101: Determine a number of communication edges of each of the mobile robots according to the communication status information, and determine the minimum communication edge among the communication edges.

[0060] It should be understood that a number of communication edges of each mobile robot can be determined according to the communication status information, and the communication edge with the minimum communication cost is determined as the to-be-verified minimum communication edge; message verification is performed on the to-be-verified minimum communication edge, and when the verification passes, the to-be-verified minimum communication edge is determined as the minimum communication edge.

[0061] It can be understood that in a multi-robot system, the communication edges (outgoing edges) of each mobile robot can be the communication connection relationships between the mobile robot and adjacent mobile robots. By representing each mobile robot as a node in the communication topology, each mobile robot can maintain the following variables: : The state of mobile robot i, which may take values of Sleeping, Find, and Found, with the initial value being Sleeping.

[0062] : The state of the edge between mobile robot i and adjacent mobile robot j, which may take values of Basic, Branch, and Rejected, , is the set of neighbor nodes of i, with the initial value being Basic.

[0063] It should be understood that in a multi-robot system, first, each mobile robot can be initialized, and the state of the mobile robot is set to Sleeping, and the state of each edge is initialized to Basic.

[0064] Next, select one (or more) mobile robots i, and assign its state from Sleeping to Found; find the outgoing edge with the smallest weight among the edges connected to this mobile robot i, that is , and record this minimum weight as , to obtain the minimum communication edge. Since this weight is the communication cost, this minimum communication edge is the connection edge with the minimum communication cost.

[0065] Finally, mobile robot i can send a test message to check whether it can become part of the minimum spanning tree. If the other end of the edge is in the Sleeping state, wake it up and add it to the Find state. If the test message returns Accept, then becomes Branch; if the test message returns Reject, then becomes Rejected.

[0066] Step S102: Obtain the minimum communication edges corresponding to each of the mobile robots, construct a minimum communication topology graph based on each of the minimum communication edges, and determine the minimum communication topology graph information based on the weight information of each of the minimum communication edges.

[0067] It is understandable that for each mobile robot in the multi-robot system, the above method in step S101 can be respectively adopted to obtain the outgoing edge with the smallest weight and record the corresponding minimum weight. Then, it is judged whether the mobile robots in each fragment are connected by Branch edges. If so, the fragment merging and updating are performed, and the global minimum communication topology information is updated.

[0068] Each mobile robot repeatedly tests its corresponding minimum communication edge until the minimum communication edges of all mobile robots in the multi-robot system are tested, and all mobile robots are in the Found state and there are no more communication edges to be tested, then the GHS algorithm is ended, the minimum communication topology graph composed of each Branch is obtained, and the minimum communication topology information is determined according to the weights corresponding to each Branch.

[0069] In this embodiment, a number of communication edges of each mobile robot are determined according to the communication state information, and the minimum communication edge is determined among the communication edges; the minimum communication edges corresponding to each mobile robot are obtained, the minimum communication topology graph is constructed according to the minimum communication edges, and the minimum communication topology information is determined according to the weight information of the minimum communication edges; since this embodiment simplifies the complex communication topology corresponding to the multi-robot system through the distributed minimum spanning tree algorithm, unnecessary communications are eliminated, which is beneficial to saving communication resources and simplifies the calculation amount in subsequent task allocation.

[0070] In addition, reference can be made here Figure 3 to illustrate the entire process of the distributed dynamic task allocation method of the present application, Figure 3 which is a schematic diagram of the entire process of the distributed dynamic task allocation method of the present application.

[0071] In Figure 3 , the process of distributed dynamic task allocation can be divided into two stages: the communication topology simplification stage and the target tracking task stage.

[0072] In the communication topology simplification stage, the allocation device can control each mobile robot in the multi-robot system to execute the GHS algorithm to obtain the minimum spanning tree topology, that is, the minimum communication topology graph.

[0073] In the target tracking task stage, the allocation device first inputs the above minimum communication topology information and system parameters into the k-WTA network calculation formula to solve the activation signal corresponding to each mobile robot; then calculates the task allocation speed of each mobile robot through the task allocation speed calculation formula (mobile robot control rate); finally, controls each mobile robot to track the target device to be tracked based on the task allocation speed until at least one mobile robot tracks the target device to be tracked, then the tracking task is completed.

[0074] The distributed dynamic task allocation method of this application has the characteristics of distribution: Since both the GHS algorithm and the target tracking algorithm in the above communication topology simplification stage are implemented in a distributed manner, for any mobile robot in the multi-robot system, it only needs to exchange information with adjacent mobile robots to complete the algorithm tasks, and has good scalability and single-point robustness.

[0075] Furthermore, in order to further illustrate the above target tracking task stage in combination with specific formulas, reference can be made here Figure 4 , Figure 4 which is the operation data flow diagram of the target tracking task stage.

[0076] In Figure 4 , the system parameters of mobile robot i can be initialized first , , , and the corresponding preset network input parameters of each mobile robot can be calculated through the calculation formula of the preset network input parameters. . The calculation formula of the preset network input parameters is:

[0077] In the formula, is the preset network input parameter corresponding to each mobile robot, is the position vector of each mobile robot, is the position vector of the target device to be tracked.

[0078] Next, mobile robot i sends the state variables , to adjacent mobile robot j, and receives the state variables ， of adjacent robot j, so as to calculate the activation signal corresponding to each mobile robot through the preset k-WTA network calculation formula.

[0079] Among them, the preset k-WTA network calculation formula is:

[0080] Then, substitute the activation signal , the position vector of each mobile robot, and the position vector of the target device to be tracked into the calculation formula of the task allocation speed (composed of position feedback and speed feedforward) to calculate the task allocation speed of each mobile robot. The calculation formula of the allocation speed is: +

[0081] Among them, represents position feedback, which is the moving speed of the target device to be tracked, that is, it represents speed feedforward.

[0082] Finally, through a preset saturation function , the upper and lower limits of the speed of each mobile robot based on the task assignment speed are restricted during the actual movement process to simulate the situation where the moving speed is restricted in the actual scenario. Then, according to the finally obtained each mobile robot is controlled to move until at least one mobile robot tracks the target device to be tracked.

[0083] It should be noted that the above examples are only used to understand the present application and do not constitute a limitation on the distributed dynamic task assignment method of the present application. Based on this technical concept, more simple transformations in various forms are within the protection scope of the present application.

[0084] In addition, the present application also provides a distributed dynamic task assignment device. Referring to Figure 5 , Figure 5 which is the structural block diagram of the first embodiment of the distributed dynamic task assignment device of the present application; as Figure 5 shown, the device includes: A topology optimization module 501, configured to obtain the communication status information of each mobile robot, and based on the communication status information, determine the minimum communication topology graph information by using a preset distributed minimum spanning tree algorithm; A task determination module 502, configured to initialize the system parameters of each of the mobile robots, and input the system parameters and the minimum communication topology graph information into a preset k-WTA network calculation formula to obtain the activation signals corresponding to each of the mobile robots; A task assignment module 503, configured to obtain the position information of the target device to be tracked and each of the mobile robots, and perform task assignment on each of the mobile robots according to the position information and the activation signals.

[0085] Furthermore, the topology optimization module 501 is further configured to determine several communication edges of each of the mobile robots according to the communication status information, and determine the minimum communication edge among the communication edges; obtain the minimum communication edges corresponding to each of the mobile robots, construct a minimum communication topology graph according to the minimum communication edges, and determine the minimum communication topology graph information according to the weight information of each of the minimum communication edges.

[0086] Further, the topology optimization module 501 is further configured to determine a number of communication edges of each mobile robot according to the communication status information, and determine the communication edge with the minimum communication cost as the to-be-verified minimum communication edge; perform message verification on the to-be-verified minimum communication edge, and determine the to-be-verified minimum communication edge as the minimum communication edge when the verification passes.

[0087] Wherein, the minimum communication topology graph information is the minimum communication edge weight.

[0088] Further, the task determination module 502 is further configured to initialize system parameters of each mobile robot, where the system parameters include: a first system parameter, a second system parameter, a first state variable parameter, a second state variable parameter, and a preset network input parameter; input the first system parameter, the second system parameter, the first state variable parameter, the second state variable parameter, the preset network input parameter, and the minimum communication edge weight into a preset k-WTA network calculation formula to obtain activation signals corresponding to each mobile robot; Wherein, the preset k-WTA network calculation formula is:

[0089] In the formula, is the first system parameter, is the second system parameter, is the first state variable parameter, is the second state variable parameter, is the preset network input parameter, .) is a preset projection function, is the total number of each mobile robot, k is the number of mobile robots performing tasks, is the activation signal corresponding to each mobile robot, is the minimum communication edge weight; Wherein, the calculation formula of the preset network input parameter is:

[0090] In the formula, is the position vector of each mobile robot, is the position vector of the to-be-tracked target device.

[0091] Further, the task determination module 502 is further configured to determine the task assignment speed of each mobile robot according to the position information and the activation signal; perform task assignment on each mobile robot according to each task assignment speed; Wherein, the calculation formula of the task assignment speed is: +

[0092] Wherein, is the task allocation speed of each of the mobile robots, is the preset error feedback gain parameter, is the robot control parameter, is the position vector of each of the mobile robots, is the position vector of the target device to be tracked, is the moving speed of the target device to be tracked.

[0093] Among them, the calculation formula of the robot control parameter is:

[0094] Wherein, x is the position difference between the position vector of each of the mobile robots and the position vector of the target device to be tracked, and r is the preset feedback parameter.

[0095] Furthermore, the task allocation module 503 is further configured to control each of the mobile robots to move at the corresponding task allocation speed until at least one mobile robot tracks the target device to be tracked, and then control each of the mobile robots to stop moving.

[0096] In this embodiment, the minimum spanning tree algorithm is used to simplify the complex communication topology in the multi-robot system, generating a communication topology graph with the minimum communication cost. Then, the minimum communication topology graph information is input into the preset k-WTA network calculation formula, greatly reducing the consumption of computing resources and facilitating the improvement of the task allocation efficiency for each mobile robot.

[0097] In addition, the present application also provides a distributed dynamic task allocation device, which includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein, the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the distributed dynamic task allocation method in the first embodiment above.

[0098] Next, refer to Figure 6 , Figure 6This is a schematic structural diagram of the distributed dynamic task allocation device of the present application. The distributed dynamic task allocation device in the embodiments of the present application may include, but is not limited to, mobile terminals such as mobile phones, laptop computers, digital broadcast receivers, PDAs (Personal Digital Assistants), PADs (Portable Application Descriptions), PMPs (Portable Media Players), etc., and fixed terminals such as digital TVs, desktop computers, etc. Figure 6 The shown distributed dynamic task allocation device is merely an example and should not impose any limitations on the functions and usage scope of the embodiments of the present application.

[0099] As Figure 6 shown, the distributed dynamic task allocation device may include a processing device 1001 (such as a central processing unit, a graphics processing unit, etc.), which can perform various appropriate actions and processes according to the program stored in the read-only memory (ROM: Read Only Memory) 1002 or the program loaded from the storage device 1003 into the random access memory (RAM: Random Access Memory) 1004. In the RAM 1004, various programs and data required for the operation of the distributed dynamic task allocation device are also stored. The processing device 1001, the ROM 1002, and the RAM 1004 are connected to each other through a bus 1005. The input / output (I / O) interface 1006 is also connected to the bus. Generally, the following systems may be connected to the I / O interface 1006: an input device 1007 including, for example, a touch screen, a touchpad, a keyboard, a mouse, an image sensor, a microphone, an accelerometer, a gyroscope, etc.; an output device 1008 including, for example, a liquid crystal display (LCD: Liquid Crystal Display), a speaker, a vibrator, etc.; a storage device 1003 including, for example, a magnetic tape, a hard disk, etc.; and a communication device 1009. The communication device 1009 can allow the distributed dynamic task allocation device to communicate with other devices wirelessly or wiredly to exchange data. Although the figure shows a distributed dynamic task allocation device with various systems, it should be understood that it is not required to implement or have all the shown systems. Instead, more or fewer systems may be implemented or had.

[0100] In addition, the present application also provides a computer-readable storage medium having computer-readable program instructions (i.e., computer programs) stored thereon, and the computer-readable program instructions are used to execute the distributed dynamic task allocation method in the above embodiments.

[0101] The computer-readable storage medium provided by the present application may be, for example, a USB flash drive, but is not limited to electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices, or components, or any combination of the above. More specific examples of the computer-readable storage medium may include, but are not limited to: electrical connections with one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM) or flash memory, optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the above. In this embodiment, the computer-readable storage medium may be any tangible medium that contains or stores a program that can be used by or in conjunction with an instruction execution system, device, or component. The program code contained on the computer-readable storage medium may be transmitted using any appropriate medium, including but not limited to: wires, optical cables, RF (radio frequency), etc., or any suitable combination of the above.

[0102] It should be noted that in this article, the term "comprising", "including" or any other variant thereof is intended to cover non-exclusive inclusion, such that a process, method, article or system comprising a series of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article or system. Without further limitation, an element defined by the statement "comprising one..." does not exclude the presence of additional elements in the process, method, article or system comprising such element.

[0103] The serial numbers of the above embodiments of the present invention are only for description and do not represent the advantages or disadvantages of the embodiments. Moreover, they are only partial embodiments of the present application and do not limit the patent scope of the present application. Any equivalent structural transformation made using the specification and drawings of the present application under the technical concept of the present application, or any direct / indirect application in other related technical fields, is included in the patent protection scope of the present application.

Claims

1. A distributed dynamic task allocation method, characterized in that: The method is applied to a multi-robot system, the multi-robot system comprising: at least one target device to be tracked and a plurality of mobile robots, the method comprising: Acquire communication status information of each of the mobile robots, and based on the communication status information, use a preset distributed minimum spanning tree algorithm to determine minimum communication topology information; Initializing the system parameters of each of the mobile robots, and inputting the system parameters and the minimum communication topology information into a preset k-WTA network calculation formula to obtain an activation signal corresponding to each of the mobile robots; The position information of the target device to be tracked and each of the mobile robots is obtained, and tasks are assigned to each of the mobile robots according to the position information and the activation signal.

2. The method according to claim 1, characterized in that The step of determining the minimum communication topology graph information based on the communication state information by using a preset distributed minimum spanning tree algorithm comprises: Determine a number of communication edges of each of the mobile robots according to the communication state information, and determine a minimum communication edge among the communication edges; The minimum communication edge corresponding to each of the mobile robots is obtained, a minimum communication topology graph is constructed according to each of the minimum communication edges, and minimum communication topology graph information is determined according to weight information of each of the minimum communication edges.

3. The method according to claim 2, characterized in that The step of determining a number of communication edges of each of the mobile robots according to the communication state information and determining a minimum communication edge among the communication edges comprises: Determine a plurality of communication edges of each of the mobile robots according to the communication state information, and determine the communication edge with the minimum communication cost as the minimum communication edge to be verified; Message verification is performed on the minimum communication edge to be verified, and when the verification passes, the minimum communication edge to be verified is determined as the minimum communication edge.

4. The method according to claim 1, characterized in that The minimum communication topology graph information is the minimum communication edge weight; The step of initializing the system parameters of each of the mobile robots, and inputting the system parameters and the minimum communication topology information into a preset k-WTA network calculation formula to obtain an activation signal corresponding to each of the mobile robots includes: Initializing system parameters of each of the mobile robots, the system parameters including: a first system parameter, a second system parameter, a first state variable parameter, a second state variable parameter, and a preset network input parameter; Input the first system parameter, the second system parameter, the first state variable parameter, the second state variable parameter, the preset network input parameter and the minimum communication edge weight into a preset k-WTA network calculation formula to obtain an activation signal corresponding to each of the mobile robots; Wherein, the preset k-WTA network calculation formula is: In the formula, is the first system parameter, is the second system parameter, is the first state variable parameter, is the second state variable parameter, inputting parameters for the preset network, .) is the preset projection function, is the total number of the mobile robots, k is the number of mobile robots performing tasks, is an activation signal corresponding to each of the mobile robots, is the minimum communication edge weight; Wherein, the calculation formula of the preset network input parameter is: In the formula, is the position vector of each mobile robot, is the position vector of the target device to be tracked.

5. The method according to claim 4, characterized in that The step of assigning tasks to each of the mobile robots according to the position information and the activation signal comprises: Determine the task allocation speed of each of the mobile robots according to the position information and the activation signal; Allocating tasks to each of the mobile robots according to each of the task allocation speeds; The calculation formula of the task allocation speed is: + In the formula, assigning a speed to each of the tasks of the mobile robots, is the preset error feedback gain parameter, are the robot control parameters, is the position vector of each mobile robot, is the position vector of the target device to be tracked, is the moving speed of the target device to be tracked.

6. The method according to claim 5, characterized in that The calculation formula of the robot control parameters is: Wherein, x is the position difference between the position vector of each mobile robot and the position vector of the target device to be tracked, and r is a preset feedback parameter.

7. The method according to claim 5, characterized in that The step of allocating tasks to each of the mobile robots according to each of the task allocation speeds comprises: Each of the mobile robots is controlled to move at a corresponding task allocation speed until at least one mobile robot tracks the target device to be tracked, and then each of the mobile robots is controlled to stop moving.

8. A distributed dynamic task allocation device, characterized in that: The device comprises: A topology optimization module is used to obtain the communication status information of each mobile robot, and based on the communication status information, determine the minimum communication topology graph information by using a preset distributed minimum spanning tree algorithm; A task determination module, used to initialize the system parameters of each of the mobile robots, and input the system parameters and the minimum communication topology information into a preset k-WTA network calculation formula to obtain an activation signal corresponding to each of the mobile robots; The task allocation module is used to obtain the position information of the target device to be tracked and each of the mobile robots, and to allocate tasks to each of the mobile robots according to the position information and the activation signal.

9. A distributed dynamic task allocation device, characterized in that: The device comprises: a memory, a processor, and a distributed dynamic task allocation program stored in the memory and executable on the processor, wherein the distributed dynamic task allocation program is configured to implement the steps of the distributed dynamic task allocation method according to any one of claims 1 to 7.

10. A storage medium, characterized in that: The storage medium stores a distributed dynamic task allocation program, which, when executed by a processor, implements the steps of the distributed dynamic task allocation method according to any one of claims 1 to 7.

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