A Task-Oriented Unmanned Cluster Topology Control Method

Abstract

The present invention relates to the field of topology control for unmanned aerial vehicle (UAV) swarms. It mainly studies a task-oriented topology control method for unmanned swarms. The main process is that after a node receives a task, it determines the task level and decides at which level the task will be executed. Intra-cluster control mainly includes cluster center selection and execution of low-level tasks. Inter-cluster cooperative control mainly includes selection of the inter-cluster cooperative control center and execution of medium-level tasks. Overall network control mainly includes selection of the network center and execution of high-level tasks. Finally, a task-oriented topology control method for unmanned swarms with intra-cluster determination, inter-cluster cooperation, and overall network control is formed. In the present invention, the node selection adopts a dual-criterion selection method of geographical center + location center, which can ensure the efficiency and quality of executing communication tasks, and the network can adapt to various communication guarantee tasks.

Description

Technical Field

[0001] The present invention relates to the field of topology control of unmanned aerial vehicle (UAV) swarms, and particularly to a task-oriented topology control method for UAV swarms. Background Art

[0002] In recent years, small UAVs have accounted for an increasingly large proportion in various industries, and the future development of UAVs is evolving towards unmanned, intelligent, distributed, and collaborative directions. In emergency support operations, UAVs have great advantages in communication support, mainly reflected in strong flexibility, low cost, full coverage of the working area, strong anti-destruction ability, etc. Under the background of the country's strong support for the development of unmanned intelligence, UAVs have also developed from simple auxiliary training and intelligence collection in the past to UAV swarm intelligent support directions such as communication support and emergency support. Unmanned swarm emergency support and communication support will be an essential part of the future communication system. UAV swarms are the main trend in the development of the future UAV emergency support system. Their significant support effectiveness, low cost, small damage loss, and easy mass equipment make their role in emergency support more and more prominent, and they will surely become an important and even key force in emergency support means.

[0003] Through the flexibility of UAV swarms, the risk of casualties on our side can be greatly reduced. Therefore, the aerial communication network composed of UAV swarms occupies an indispensable position in future emergency support. However, how to control a large number of UAVs and how to efficiently and reliably utilize the UAV swarm network have always been the focus of research at home and abroad. Centralized control is currently a relatively mainstream control application method in small-scale unmanned swarm control. Ultra-large-scale unmanned swarm control is carried out through clustering control algorithms, and both of these have relatively obvious disadvantages. Centralized control is prone to problems such as network congestion and network paralysis, and clustering control has problems such as untimely inter-cluster cooperation and inter-cluster isolation. Therefore, the control of large-scale unmanned swarms has always been one of the key research points. The present invention proposes a corresponding topology control structure from the perspective of task support, defines the topology structure by tasks, and solves the current problems of difficult control and inability to control of unmanned swarms through a combination of task-topology control. Summary of the Invention

[0004] The present invention proposes a task-oriented topology control method for UAV swarms, which on the one hand solves the problems of untimely and unsmooth control of current non-fixed networks, and on the other hand improves the task execution ability of the entire network and can be applied to UAV communication support networks in multiple scenarios.

[0005] The technical solution adopted by the present invention is as follows:

[0006] A task-oriented topology control method for UAV swarms includes the following processes:

[0007] Step 1: Determine the clustering range based on geographical location and network topological location, and screen out the candidate set of central nodes through initial conditions. Elect the current network central node from the candidate set of central nodes. The initial conditions include geographical location, energy, communication ability, and its own status.

[0008] Step 2: Determine the regions where each cluster is located based on geographical location and network topological location, and conduct the election of cluster central nodes within each region.

[0009] Step 3: The cluster central node issues a clustering instruction, which includes the energy, communication ability, and historical information of the UAV. Nodes within the corresponding cluster range and non-cluster central nodes within one-hop around automatically join the cluster central node that issues the clustering instruction.

[0010] Step 4: After the cluster is constructed, the inter-cluster gateway node automatically becomes the inter-cluster cooperation node of adjacent clusters.

[0011] Step 5: After receiving the task, the node transmits it to the cluster central node of its own cluster. The cluster central node conducts task level determination. If it is a low-level task, it is solved within the cluster. If it is not a low-level task, it is transmitted to the inter-cluster cooperation node.

[0012] Step 6: The inter-cluster cooperation node conducts task level determination. If it is a medium-level task, the inter-cluster cooperation node is responsible for the cooperation of relevant neighbor clusters to jointly solve the current task. If it is a high-level task, it is transmitted to the network central node.

[0013] Step 7: The network central node mobilizes all nodes in the network to jointly solve the current task.

[0014] Further, in Step 1, when electing the current network central node from the candidate set of central nodes, specifically:

[0015] Apply the raft algorithm in the candidate set of central nodes to elect the current network central node. When the ability of the current network central node is insufficient, delete the current network central node from the candidate set of central nodes, and at the same time rotate the central node for the nodes in the candidate set of central nodes through the leach algorithm. The specific steps for rotating the central node are as follows:

[0016] Step 101: Assign random numbers to the nodes in the candidate set of central nodes, and set type and selected to 'N'.

[0017] Step 102: Compare the random values of all nodes with selected as 'N' with the threshold. If the random value of the node is less than or equal to the threshold, go to Step 103; otherwise, go to Step 104.

[0018] Step 103: The node is selected as the cluster head node, the type is assigned 'C', and the selected is assigned 'O'. The cluster head node is the network center node in the current round; return to Step 101 to start the next round of process;

[0019] Step 104: The node is selected as an ordinary node, the type is still assigned 'N', and the selected remains unchanged; return to Step 101 to start the next round of process.

[0020] Compared with the prior art, the beneficial effects achieved by the present invention are as follows:

[0021] The node selection of the present invention adopts a dual - standard selection method of geographical center + location center, which can ensure the efficiency and quality of executing communication tasks, and the network can adapt to various communication guarantee tasks.

[0022] The present invention adopts a hierarchical control network structure strategy based on the combination of geographical location and network topology. According to the geographical location of the UAV distribution, the network topology location after networking, etc., different node "roles" are assigned to the UAV cluster. Secondly, the node role rotation is carried out in combination with the dynamic ability change of the UAV, the performance of the network is evaluated by integrating various information, and combined with the degree of task execution efficiency, a task - oriented logical hierarchical network topology control structure is formed. Brief Description of the Drawings

[0023] Figure 1 It is a flowchart of a method for generating a network topology structure of the present invention.

[0024] Figure 2 It is a flowchart of a task execution of an in - cluster control module of the present invention.

[0025] Figure 3 It is a flowchart of a task execution of an inter - cluster cooperation module of the present invention.

[0026] Figure 4 It is a schematic diagram of the selection of the in - cluster central node of the present invention. Detailed Embodiment

[0027] The present invention will be further described below with reference to the drawings and embodiments.

[0028] Below in combination with the attached Figures 1 to 4 The present invention is further explained and described.

[0029] As Figures 1 to 3 shown, a task - oriented UAV cluster topology control method includes the following processes:

[0030] Step 1: Determine the clustering range based on the geographical location and the network topology location, and screen out the candidate set of central nodes through the initial conditions. Elect the current network central node from the candidate set of central nodes, where the initial conditions include geographical location, energy, communication ability, and its own status;

[0031] Elect the current network central node from the candidate set of central nodes, specifically:

[0032] Apply the raft algorithm in the candidate set of central nodes to elect the current network central node. When the current network central node has insufficient capabilities, delete the current network central node from the candidate set of central nodes, and at the same time rotate the central node among the nodes in the candidate set of central nodes through the leach algorithm. As Figure 4 shown, the specific steps for rotating the central node are as follows:

[0033] Step 101: Assign random numbers to the nodes in the candidate set of central nodes, and set type and selected to 'N';

[0034] Step 102: Compare the random numbers of all nodes with selected as 'N' with the threshold. If the random number of a node is less than or equal to the threshold, go to Step 103; otherwise, go to Step 104;

[0035] Step 103: Select the node as the cluster head node, assign 'C' to type and 'O' to selected. The cluster head node is the current round of network central node; return to Step 101 to start the next round of process;

[0036] Step 104: Select the node as an ordinary node, still assign 'N' to type, and do not change the assignment of selected; return to Step 101 to start the next round of process.

[0037] Step 2: Determine the regions where each cluster is located based on the geographical location and the network topology location, and conduct the election of cluster central nodes within each region;

[0038] Step 3: The cluster central node issues a clustering instruction, including the energy, communication ability, and historical information of the UAV. The nodes within the corresponding cluster range and the non-cluster central nodes within one-hop around automatically join the cluster central node that issues the clustering instruction;

[0039] Step 4: After the cluster is constructed, the inter-cluster gateway node automatically becomes the inter-cluster cooperation node of the adjacent clusters;

[0040] Step 5: After the node receives the task, it transmits it to the cluster central node of its own cluster. The cluster central node judges the task level. If it is a low-level task, it is solved within the cluster; if it is not a low-level task, it is transmitted to the inter-cluster cooperation node;

[0041] Step 6: The inter-cluster collaborative node determines the task level. If it is a medium-level task, the inter-cluster collaborative node is responsible for collaborating with relevant neighboring clusters to jointly solve the current task; if it is a high-level task, it is transmitted to the network center node.

[0042] Step 7: The network center node mobilizes all network nodes to jointly solve the current task.

Claims

1. A task-oriented unmanned cluster topology control method, characterized in that, it includes the following processes: Step 1, determine the clustering range based on the geographical location and the network topology location, and screen out the candidate set of central nodes through the initial conditions, and elect the current network central node from the candidate set of central nodes; wherein the initial conditions include geographical location, energy, communication ability, and its own state; Step 2, determine the regions where each cluster is located based on the geographical location and the network topology location, and elect the cluster central nodes within each region; Step 3, the cluster central node issues a clustering instruction, including the energy, communication ability, and historical information of the unmanned aerial vehicle, and the nodes within the corresponding cluster range and the non-cluster central nodes within one-hop range around automatically join the cluster central node that issues the clustering instruction; Step 4, after the cluster is constructed, the inter-cluster gateway node automatically becomes the inter-cluster cooperation node of the adjacent cluster; Step 5, after the node receives the task, it transmits it to the cluster central node of this cluster. The cluster central node makes a task level determination. If it is a low-level task, it is solved within the cluster. If it is not a low-level task, it is transmitted to the inter-cluster cooperation node; Step 6, the inter-cluster cooperation node makes a task level determination. If it is a medium-level task, the inter-cluster cooperation node is responsible for the cooperation of the relevant neighbor clusters to jointly solve the current task; if it is a high-level task, it is transmitted to the network central node; Step 7: The network central node mobilizes all the nodes in the network to jointly solve the current task.

2. The task-oriented unmanned cluster topology control method according to claim 1, characterized in that, in Step 1, the current network central node is elected from the candidate set of central nodes, specifically: Apply the raft algorithm in the candidate set of central nodes to elect the current network central node. When the ability of the current network central node is insufficient, delete the current network central node from the candidate set of central nodes, and at the same time rotate the central node for the nodes in the candidate set of central nodes through the leach algorithm. The specific steps of rotating the central node are as follows: Step 101: Assign random numbers to the nodes in the candidate set of central nodes, and set type and selected to 'N'; Step 102: Compare the random values of all nodes with selected as 'N' with the threshold. If the random value of the node is less than or equal to the threshold, go to Step 103, otherwise go to Step 104; Step 103: The node is selected as the cluster head node, assign type to 'C', and selected to 'O'. The cluster head node is the network central node of the current round; return to Step 101 to start the next round of process; Step 104: The node is selected as an ordinary node, still assign type to 'N', and do not change the assignment of selected; return to Step 101 to start the next round of process.

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