Unmanned aerial vehicle distributed task allocation method for reducing communication times

Abstract

The invention provides an unmanned aerial vehicle distributed task allocation method for reducing communication times, and belongs to the technical field of unmanned aerial vehicle command decision. The specific process is as follows: step 1, collecting initial environment information; 2, independently constructing a task bundle by each unmanned aerial vehicle by adopting a consensus-based binding algorithm task bundle construction method; step 3, clustering is carried out by the unmanned aerial vehicle; 4, selecting one unmanned aerial vehicle as a key communication point for each cluster, exchanging task beam construction results by the unmanned aerial vehicles in the clusters, and eliminating conflict tasks by using a consistency processing principle; 5, the key communication nodes of all the clusters exchange consistency processing results, conflict tasks are removed according to the consistency processing principle, and task distribution conflicts between the clusters are solved; and 6, if the task allocation of the unmanned aerial vehicles is not converged, the task beam construction and consistency processing process is repeated until convergence, and an executable path of each unmanned aerial vehicle is generated.

Description

Technical Field

[0001] The invention belongs to the technical field of unmanned aerial vehicle command decision-making, and in particular relates to a method for allocating distributed tasks of unmanned aerial vehicles for reducing the number of communications. Background Art

[0002] Relying on its low cost, small size, high speed, strong maneuverability and high reliability, UAVs are increasingly widely used in military and civilian fields, such as regional search, electronic jamming, tracking and identification, control and suppression, forest rescue, etc. Coordinated decision-making and control of multiple UAVs is a key technology to achieve multi-UAV coordination, which is extremely important for achieving effective coordination of multiple UAVs and improving the overall combat effectiveness of multiple UAVs. Especially under the condition of limited communication, how to efficiently and orderly assign combat tasks to multiple UAVs has attracted widespread attention. The Consistency Bundle Algorithm (CBBA) is one of the most effective methods to solve UAV task allocation at present, which can adapt to different application scenarios.

[0003] The Consistency Bundle Algorithm (CBBA) is one of the most effective methods for solving UAV task allocation at present, and can be adapted to different application scenarios. However, the CBBA algorithm has a large number of invalid and repeated communications when processing multi-UAV task allocation. In view of this, the present invention provides a UAV distributed task allocation method that reduces the number of communications, which can effectively reduce the number of communications between UAVs. Summary of the invention

[0004] In view of this, the present invention provides a method for allocating distributed tasks among unmanned aerial vehicles (UAVs) for reducing the number of communications. The method can significantly reduce the number of communications between UAVs.

[0005] The technical solution for implementing the present invention is as follows:

[0006] A method for allocating distributed tasks of unmanned aerial vehicles to reduce the number of communications, the specific process is as follows:

[0007] Step 1: Collect initial environment information;

[0008] Step 2: Using the consensus-based bundle algorithm (CBBA) task bundle construction method, each UAV independently constructs a task bundle;

[0009] Step 3: Randomly generate the number of clusters and use the K-means algorithm to cluster the drones;

[0010] Step 4: Each cluster selects a UAV as the key communication point. The UAVs within the cluster exchange the task bundle construction results in step 2 and eliminate conflicting tasks according to the consistency processing principle of the CBBA algorithm;

[0011] Step 5: The key communication nodes of each cluster exchange the consistency processing results of step 4, eliminate conflicting tasks according to the consistency processing principle of the CBBA algorithm, and resolve the task allocation conflicts between clusters;

[0012] Step 6: Determine whether the UAV task allocation has converged. If not, each UAV will jump to step 2 based on the current consistency processing result, repeat the task bundle construction and consistency processing process, and continue the UAV task allocation. If converged, output the task allocation result and generate the executable path for each UAV.

[0013] Furthermore, the selection of key communication nodes of the present invention is as follows:

[0014] Comprehensively consider each UAV i∈[1,N a ]’s degree centrality DC(i), closeness centrality CNC(i), betweenness centrality BNC(i), and eigenvector centrality ENC(i). When selecting key nodes, each indicator is normalized and a key communication node is selected for each cluster.

[0015] Furthermore, the key communication nodes of the present invention are selected as follows:

[0016]

[0017] in:

[0018]

[0019] Among them, Y(i) represents the UAV communication metric value, ω 1 ,ω 2 ,ω 3 ,ω 4 Represent the weights of betweenness centrality, closeness centrality, degree centrality, and eigenvector centrality, σ st (i) represents the number of drones i passing through all the shortest paths from drone s to drone t, σ st represents the number of shortest paths from UAV s to UAV t, d ij represents the shortest path length from UAV i to UAV j, a ij is the network adjacency matrix A ij The i-th row and j-th column element of , when UAV i and UAV j can communicate directly, a ij =1, otherwise a ij =0, c e (j) represents the adjacency matrix A ij is the j-th eigenvector of , and λ is the corresponding eigenvalue.

[0020] Beneficial effects:

[0021] The existing CBBA algorithm requires drones to communicate with each other, and there is a large amount of repeated and unnecessary communication between drones. The present invention clusters drones and selects key communication nodes. Each drone only needs to communicate with drones in the cluster. Key communication nodes can further reduce the communication within the cluster. Therefore, the method of the present invention can significantly reduce the number of communications between drones. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0023] Figure 1 A flow chart of a method for allocating distributed tasks of unmanned aerial vehicles to reduce the number of communications;

[0024] Figure 2 This is the initial distribution map of 4 drones and 8 targets;

[0025] Figure 3 This is the task allocation and path planning result based on the present invention. DETAILED DESCRIPTION

[0026] The embodiments of the present invention are described in detail below with reference to the accompanying drawings.

[0027] It should be noted that the following embodiments and features in the embodiments may be combined with each other in the absence of conflict; and, based on the embodiments in the present disclosure, all other embodiments obtained by ordinary technicians in the field without making any creative work are within the scope of protection of the present disclosure.

[0028] It should be noted that various aspects of the embodiments within the scope of the appended claims are described below. It should be apparent that the aspects described herein may be embodied in a wide variety of forms, and any specific structure and / or function described herein is merely illustrative. Based on the present disclosure, it should be understood by those skilled in the art that an aspect described herein may be implemented independently of any other aspect, and two or more of these aspects may be combined in various ways. For example, any number of aspects described herein may be used to implement the device and / or practice the method. In addition, other structures and / or functionalities other than one or more of the aspects described herein may be used to implement this device and / or practice this method.

[0029] The present application embodiment provides a method for allocating distributed tasks of unmanned aerial vehicles to reduce the number of communications. Figure 1 As shown, the steps are as follows:

[0030] Step 1: Given the known initial environment information, including the initial position of the drone, maximum range, speed range, maximum number of missions, and initial position of the target.

[0031] Step 2: Using the consensus-based bundle algorithm (CBBA) task bundle construction method, each drone independently constructs a task bundle. Before the first execution of step 2, the task bundle is empty.

[0032] Step 3: After the task bundle is constructed, randomly generate the number of clusters and use the K-means algorithm to cluster the drones.

[0033] Step 4: According to the clustering results of step 3, each cluster selects a UAV as the key communication point. The UAVs within the cluster exchange the task bundle construction results in step 2, eliminate conflicting tasks according to the consistency processing principle of the CBBA algorithm, and resolve the task allocation conflicts within the cluster.

[0034] The selection of key communication nodes is as follows:

[0035] Comprehensively consider each UAV i∈[1,N a ]’s degree centrality DC(i), closeness centrality CNC(i), betweenness centrality BNC(i), and eigenvector centrality ENC(i). When selecting key nodes, each indicator is normalized and a key communication node is selected for each cluster.

[0036]

[0037] in:

[0038]

[0039] Among them, Y(i) represents the communication metric value of the drone, and the drone with the largest communication metric value in the cluster is regarded as the key communication node of the cluster. 1 ,ω 2 ,ω 3 ,ω 4 They represent the weights of betweenness centrality, closeness centrality, degree centrality and eigenvector centrality respectively, Y(i) represents the UAV communication metric, σ st (i) represents the number of drones i passing through all the shortest paths from drone s to drone t, σ st represents the number of shortest paths from UAV s to UAV t, d ij represents the shortest path length from UAV i to UAV j. ij is the network adjacency matrix A ijThe i-th row and j-th column element of a ij =1 means that drone i and drone j can communicate directly, otherwise a ij =0. c e (j) represents the adjacency matrix A ij is the j-th eigenvector of , and λ is the corresponding eigenvalue.

[0040] Step 5: After the consistency processing within the cluster in step 4, the key communication points of the cluster obtain the task allocation information of other drones within the cluster. The key communication nodes of each cluster exchange the consistency processing results of step 4, eliminate conflicting tasks according to the consistency processing principle of the CBBA algorithm, and resolve the task allocation conflicts between clusters.

[0041] Step 6: According to the consistency processing result of step 5, determine whether the UAV task allocation has converged. If not, jump to step 2 and continue the UAV task allocation. At this time, after jumping to step 2, when using the CBBA algorithm to construct the task bundle, the task bundle substituted into the CBBA algorithm is not empty. If converged, output the task allocation result and generate the executable path for each UAV.

[0042] Below is N a drone and N T The present invention is described in detail with the objective as an example.

[0043] Step 1: Given N a Drone location speed Maximum range Maximum number of executable tasks N T The location of the target speed Latest execution time

[0044] Step 2: Using the CBBA task bundle construction method, each drone uses a sequential greedy method to construct a task bundle

[0045] Step 3: Randomly generate the number of clusters K, K∈[1,N a ], the Kmeans algorithm is used to cluster the drones and generate K clusters C 1 ,C 2 ,…,C K .

[0046] Step 4: Based on the clustering result C of step 3 1 ,C 2 ,…,C K , respectively set the weight values ​​ω of betweenness centrality, closeness centrality, degree centrality and eigenvector centrality1 ,ω 2 ,ω 3 ,ω 4 , calculate the communication metric of the drone. Each cluster selects a drone as the key communication point, and the key communication point of each cluster is Uk 1 ,Uk 2 ,…,Uk K The drones in the cluster use the CBBA algorithm for consistency processing to resolve the task allocation conflicts within the cluster.

[0047] Step 5: After the consistency processing within the cluster in step 4, the key communication points of the cluster obtain the task allocation information of other drones within the cluster. The key communication nodes of each cluster exchange the consistency processing results of step 4, eliminate conflicting tasks according to the consistency processing principle of the CBBA algorithm, resolve the task allocation conflicts between clusters, and update the task execution sequence.

[0048] Step 6: According to the consistency processing result of step 5, determine whether the UAV task allocation has converged. If not, jump to step 2 and continue the UAV task allocation. If converged, output the task allocation result and generate the executable path for each UAV.

[0049] Example:

[0050] This embodiment adopts a method for allocating distributed tasks of unmanned aerial vehicles to reduce the number of communications provided by the present invention, thereby reducing the number of communications between unmanned aerial vehicles. The specific execution steps are as follows:

[0051] Step 1: In a range of 10km×10km, 4 drones are given to perform 6 tasks. The speed of each drone is 100m / s, the maximum range of each drone is 50km, and the maximum number of tasks is 6. The initial distribution map of drones and tasks is as follows: Figure 2 shown.

[0052] Step 2: Using the CBBA task bundle construction method, each UAV uses a sequential greedy method to construct a task bundle. The task bundle construction result is:

[0053] p 1 =[7-2-1-8-6]

[0054] p 2 =[7-2-1-8-6]

[0055] p 3 =[4-5-3]

[0056] p 4 =[4-5-3]

[0057] Step 3: Randomly generate 2 clusters, use Kmeans algorithm to cluster the drones, and generate 2 clusters of drones.

[0058] C 1 =[1-2-3]

[0059] C 2 =[4]

[0060] Step 4: Based on the clustering result C of step 3 1 ,C 2 , respectively set the weight values ​​ω of betweenness centrality, closeness centrality, degree centrality and eigenvector centrality 1 =0.1,ω 2 =0.1,ω 3 =0.6,ω 4 =0.2, calculate the drone communication metric value of each drone Y(2)>Y(3)>Y(1), therefore, drone 2 is clustered C 1 Similarly, drone 4 is the key communication node of cluster C. 2 key communication nodes.

[0061] The drones in the cluster use the CBBA algorithm for consistency to resolve the task allocation conflicts within the cluster. The number of internal cluster communications is 6. The results are as follows:

[0062] p 1 =[]

[0063] p 2 =[7-2-1-8-6]

[0064] p 3 =[4-5-3]

[0065] p 4 =[4-5-3]

[0066] Step 5: Use the CBBA consistency processing method to resolve the task allocation conflicts between clusters and update the task execution sequence p 1 ,p 2 ,p 3 ,p 4 , the number of communications between clusters is 2, and the results are as follows:

[0067] p 1 =[]

[0068] p 2 =[7-2-1-8-6]

[0069] p 3 =[]

[0070] p 4=[4-5-3]

[0071] Step 6: According to the consistency processing result of step 5, the algorithm has not converged, so jump to step 2 and continue the drone task allocation. If converged, output the task allocation result and generate the executable path for each drone. The final task allocation result is as follows Figure 3 shown.

[0072] According to the method of the present invention, the conflict-free task allocation result is obtained, and the UAVs communicate with each other for a total of 12 times. Under the same conditions, according to the CBBA algorithm, the UAVs communicate with each other for a total of 24 times. The present invention can significantly reduce the number of communications between UAVs.

[0073] The above is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any changes or substitutions that can be easily thought of by a person skilled in the art within the technical scope disclosed by the present invention should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.

Claims

1. A method for allocating distributed tasks of unmanned aerial vehicles to reduce the number of communications, characterized in that: The specific process is: Step 1: Collect initial environment information; Step 2: Using the consensus-based bundling algorithm CBBA task bundle construction method, each drone independently constructs a task bundle; Step 3: Randomly generate the number of clusters and cluster the drones; Step 4: Each cluster selects a UAV as the key communication point. The UAVs within the cluster exchange the task bundle construction results in step 2 and eliminate conflicting tasks according to the consistency processing principle of the CBBA algorithm; Step 5: The key communication nodes of each cluster exchange the consistency processing results of step 4, eliminate conflicting tasks according to the consistency processing principle of the CBBA algorithm, and resolve the task allocation conflicts between clusters; Step 6: Determine whether the UAV task allocation has converged. If not, each UAV will jump to step 2 based on the current consistency processing result, repeat the task bundle construction and consistency processing process, and continue the UAV task allocation. If converged, output the task allocation result and generate the executable path for each UAV.

2. The method for allocating distributed tasks of unmanned aerial vehicles to reduce the number of communications according to claim 1, characterized in that: The selection of key communication nodes is as follows: Comprehensively consider each UAV i∈[1,N a ]’s degree centrality DC(i), closeness centrality CNC(i), betweenness centrality BNC(i), and eigenvector centrality ENC(i). When selecting key nodes, each indicator is normalized and a key communication node is selected for each cluster.

3. The method for allocating distributed tasks of unmanned aerial vehicles to reduce the number of communications according to claim 2, characterized in that: The selection of key communication nodes is: in: Among them, Y(i) represents the UAV communication metric value, ω1, ω2, ω3, ω4 represent the weight values ​​of betweenness centrality, closeness centrality, degree centrality and eigenvector centrality respectively, σ st (i) represents the number of drones i passing through all the shortest paths from drone s to drone t, σ st represents the number of shortest paths from UAV s to UAV t, d ij represents the shortest path length from UAV i to UAV j, a ij is the network adjacency matrix A ij The i-th row and j-th column element of , when UAV i and UAV j can communicate directly, a ij =1, otherwise a ij =0, c e (j) represents the adjacency matrix A ij is the j-th eigenvector of , and λ is the corresponding eigenvalue.

4. The method for allocating distributed tasks of unmanned aerial vehicles to reduce the number of communications according to claim 1, characterized in that: The step 3 is: randomly generating a number of clusters and clustering the drones using a K-means algorithm.

Images