Clustered UAV self-organizing network communication architecture, information transmission method and clustering method

Abstract

The present invention relates to a clustered unmanned aerial vehicle (UAV) self-organizing network communication architecture, information transmission method, and clustering method. Each cluster of UAVs is provided with a master node UAV, and the remaining UAVs in the cluster are sub-node UAVs. The network card mode configured on the master node UAV is wireless access point mode and peer-to-peer mode Ad-Hoc; the network card mode configured on the sub-node UAV is client mode. The master node UAVs and sub-node UAVs in the cluster use the AP-AC communication protocol, and each cluster is allocated an independent subnet to form its own local area network. The master node UAVs in the cluster communicate with each other using the Ad-Hoc protocol, a virtual network is established, and a static routing table is configured for each master node UAV to achieve direct communication between any two nodes. The multi-band multi-protocol clustered UAV self-organizing network communication architecture proposed by the present invention supports large-scale node communication while ensuring the stability and efficiency of the UAV transmission network, thereby improving the anti-interference capability of the UAV cluster.

Description

Technical Field

[0001] The present invention relates to the field of communication positioning, and in particular to a self-organizing hierarchical reliable network communication architecture realized by using an external network card. Background Art

[0002] With the rapid development of unmanned systems, unmanned swarms have become a crucial component of these systems. UAV swarm missions not only improve the efficiency and success rate of mission execution but also transform the limited intelligence of individual drones into the collective wisdom of a swarm. However, in environments with communication interference, information exchange and coordinated control operations within unmanned swarms face significant challenges. Therefore, studying the challenges faced by unmanned swarms in communication interference environments and proposing effective solutions are crucial for ensuring highly reliable information exchange and coordinated control operations within unmanned swarms. Summary of the Invention

[0003] The purpose of the present invention is to overcome the defects of the above-mentioned prior art and provide a self-organizing hierarchical reliable network communication architecture implemented by using an external network card.

[0004] The purpose of the present invention can be achieved by the following technical solutions:

[0005] As a first aspect of the present invention, a clustered drone ad hoc network communication architecture is provided, wherein the communication architecture includes multiple clusters of independent drones, each cluster of drones includes a master node drone, and the other drones in the cluster are sub-node drones;

[0006] The master node UAV is configured with multiple network card modes, including wireless access point mode AP and peer-to-peer mode Ad-Hoc; the network card mode configured in the sub-node UAV is set to client mode AC;

[0007] The AP-AC communication protocol is used between the master node drone and the sub-node drones in the cluster. An independent subnet is allocated to each cluster under the AP-AC communication protocol to form its own local area network.

[0008] The communication between the master node drones in the cluster adopts the Ad-Hoc protocol. The Ad-Hoc mode communication between the master node drones in the cluster builds a virtual network and configures a static routing table for each master node drone to achieve direct communication between any two nodes.

[0009] As a preferred technical solution, the routing table is configured with all possible routing link addresses, including the address of the local area network where the target network segment is located and the next hop address.

[0010] As an optimal technical solution, the intra-cluster UAV communication uses dual frequency bands, including a main frequency band and a backup frequency band. When the main frequency band is interfered with, the backup frequency band is used for communication.

[0011] As an optimal technical solution, the main frequency band for intra-cluster drone communication is 5.8 GHz, and the backup frequency band is 2.4 GHz; inter-cluster drone communication uses the 2.4 GHz frequency band.

[0012] As a second aspect of the present invention, a method for transmitting information of a drone swarm is provided. The method is applied to the clustered drone ad hoc network communication architecture described above, and the steps include:

[0013] The master node drone starts routing forwarding and configures a static routing table;

[0014] Search the routing table for the link with the shortest hop count as the current route for communication.

[0015] As a preferred technical solution, when a certain inter-cluster communication is interrupted, a second shortest link is selected from the remaining links in the routing table to perform multi-hop communication.

[0016] As a preferred technical solution, when a communication link in the cluster is interrupted, the communication link switches to a corresponding backup frequency band for data transmission.

[0017] As a third aspect of the present invention, a method for adaptive clustering of a swarm of drones is provided. The method is applied to the clustered drone ad hoc network communication architecture described above, and the steps include:

[0018] Initialize drone clustering;

[0019] The sub-node drone regularly detects the signal strength of all master node drones and compares the first signal RSSI of the master node drone with the strongest signal with the second signal RSSI of the master node drone currently connected;

[0020] If the difference between the first signal RSSI and the second signal RSSI is greater than the set signal RSSI threshold, the first switching flag is set;

[0021] A switching timer is set, and if the switching timer reaches a set switching time threshold, a second switching flag is set;

[0022] When the first switching flag and the second switching flag are set at the same time, the current child node drone switches to the new strongest master node drone signal network connection, and resets the first switching flag and the second switching flag.

[0023] As a preferred technical solution, the initialization of drone clustering is specifically as follows:

[0024] Each master node drone periodically broadcasts, and the broadcast content includes ID, signal strength, and the number of currently connected nodes;

[0025] For each child node drone, it receives the signals of all master node drones and calculates the weight function based on the RSSI of the signals of each master node drone and the current number of access nodes N;

[0026] The child node drones join the master node drone with the highest score based on the weight function;

[0027] After receiving the joining request from the sub-node drone, the master node drone will add the sub-node drone to its network and update its broadcast signal.

[0028] As an optimal technical solution, the drone group increases the number of clusters by increasing the number of topological Ad-Hoc nodes, and increases the number of drones in the cluster by adding new child node drones.

[0029] Compared with the prior art, the present invention has the following beneficial effects:

[0030] 1) To address the problem of large-scale UAV swarm operations, this paper proposes a multi-band, multi-protocol clustered UAV self-organizing network communication architecture that supports large-scale node communications while reducing costs and facilitating deployment.

[0031] 2) To address the high dynamics and topology changes of drone clusters, an adaptive cluster switching and joining algorithm is proposed to ensure that drones always have a stable and efficient transmission network;

[0032] 3) In order to address the problem that unmanned aerial vehicle clusters are susceptible to communication interference, a multi-link and Ad-Hoc dynamic routing communication mechanism is combined to ensure reliable communication in the event of intra-cluster and inter-cluster communication link interruption, thereby improving the anti-interference capability of the drone cluster. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] Figure 1 A schematic diagram of the basic network architecture of the low-cost multi-band multi-protocol converged communication architecture of the present invention;

[0034] Figure 2 Schematic diagram of adaptive cluster switching of sub-nodes in the dynamic process of the present invention;

[0035] Figure 3 A schematic diagram of virtual network establishment and routing table configuration according to the present invention;

[0036] Figure 4 Schematic diagram of multi-hop reliable communication under the condition of inter-cluster link interruption in the present invention, a) is before the inter-cluster link interruption, b) is after the inter-cluster link interruption;

[0037] Figure 5 Schematic diagram of dual-link reliable communication in the case of intra-cluster link interruption according to the present invention, a) is before the intra-cluster link interruption, and b) is after the intra-cluster link interruption. DETAILED DESCRIPTION

[0038] The present invention is described in detail below with reference to the accompanying drawings and specific embodiments. This embodiment is implemented based on the technical solution of the present invention, and provides a detailed implementation method and specific operation process, but the protection scope of the present invention is not limited to the following embodiments.

[0039] Example 1

[0040] The present invention relates to a low-cost, lightweight, self-organizing network communication solution, comprising a hardware architecture and software algorithms. By constructing an intra-cluster and inter-cluster communication architecture that integrates multiple frequency bands and multiple protocols, stable communication for large-scale drone swarms is achieved. To address the highly dynamic nature of drones within a cluster, an adaptive clustering algorithm is proposed that can autonomously construct highly stable subnets. Furthermore, to address the need for reliable information exchange within and between clusters with highly dynamic changes, a virtual network-based multi-message transmission solution is proposed between clusters, and a dual-link reliable transmission solution is proposed within a cluster. These solutions enable stable information transmission despite topology changes and link interruptions.

[0041] The low-cost multi-band multi-protocol converged communication architecture is as follows:

[0042] Multiple clusters of drones are relatively independent in terms of spatial distribution or other defined dimensions. Each cluster of drones has a master node drone and the others are sub-node drones. Figure 1 As shown, the present invention is equipped with two network card modes on the master node drone, which are set to wireless access point mode (AP) and peer mode (Ad-Hoc / IBSS). In the child nodes within the cluster, one network card mode is configured and set to client mode (AC / Managed).

[0043] The intra-cluster drones adopt the AP-AC communication protocol and use the 5.8GHz / 2.4GHz dual-band. The 5.8GHz is used as the main frequency band to ensure a larger communication capacity when large-scale data is transmitted within the cluster, and the 2.4GHz is used as a backup. When the 5.8GHz frequency band is interfered with, the 2.4GHz is used for communication. The inter-cluster drone communication adopts the Ad-Hoc protocol, so it can ensure mutual communication between any two master node drones. At the same time, the 2.4GHz frequency band is used to meet the communication needs of longer distances between clusters.

[0044] When the intra-cluster network performs dual-band communication, different network cards are required. Different subnets need to be set up and different network cards need to be applied according to the specific situation. The network architecture has extremely high scalability.

[0045] The above architecture needs to allocate an independent subnet to each cluster under the AP-AC protocol, and also needs to allocate an independent subnet to the Ad-Hoc mode of the master node drone between clusters, thereby forming their own local area networks. Figure 1 shown.

[0046] Example 2

[0047] Based on the low-cost, multi-band, multi-protocol converged communication infrastructure proposed in Example 1, the present invention also designs an adaptive clustering algorithm. Once the master node drone is determined, child node drones can autonomously access the optimal cluster to achieve highly dynamic network construction. Furthermore, the number of clusters can be increased by adjusting the number of topological Ad-Hoc nodes, and the number of drones within a cluster can be increased by adding new child node drones, meeting high scalability requirements.

[0048] The 2.4GHz network is used as the backup network, and the master node drone selected by the child nodes when using the 5.8GHz network is the same. The intra-cluster communication is simplified to an AP-AC communication mode using only the 5.8GHz frequency band. The following initial clustering and real-time cluster switching algorithms are designed:

[0049] 2.1) Initialize clustering:

[0050] a. Each master node drone periodically broadcasts its ID, signal strength, and the number of currently connected nodes;

[0051] b. Each child node receives signals from all master node drones and calculates a weight function based on the RSSI value of the master node drone i and the number of nodes N to which the master node drone i currently connects.

[0052] w(i)=α·RSSI(i)-β·N(i)

[0053] Among them, α and β are the weight calculation coefficients of the signal strength value RSSI of the master node drone i and the number of access nodes N, respectively.

[0054] c. The child node finds the master node drone with the highest score based on the weight function and joins it;

[0055] d. After receiving the request, the master node drone adds the child node to its network and updates its broadcast signal.

[0056] 2.2) Dynamic process adaptive cluster switching:

[0057] a. Detect signal strength changes. The child node regularly detects the signal strength of all master node drones and finds the received signal strength indicator (RSSI) RSSI_1 of the master node drone with the strongest signal. This is then compared with the signal value RSSI_2 of the currently connected master node drone.

[0058] b. If RSSI_1-RSSI_2>Threshold, consider switching and set flag1=1;

[0059] c. Set a switching timer and a switching time threshold Thres_t. If the timer reaches the switching time threshold, consider switching. This is to avoid frequent switching causing network instability. Set flag2 = 1;

[0060] e. When flag1 == 1 and flag2 == 1, the current child node switches to the new network with the strongest signal and updates the values ​​of flag1 and flag2 to 0. This ensures that the current child node is connected to the network with the best signal and does not frequently switch networks and become unstable.

[0061] Example 3

[0062] To achieve highly reliable communication between sub-node drones in different clusters, this invention has designed a virtual network-based transmission scheme, enabling direct communication between two targets on different local area networks. By configuring a routing table and designing a dynamic routing scheme, reliable multi-hop communication can be achieved even if inter-cluster communication is interrupted. Furthermore, because intra-cluster communication utilizes dual-band communication, it can automatically switch to another link if one link is interrupted. The reliable, interference-resistant multi-hop information transmission scheme based on a virtual network is described below:

[0063] To achieve multi-hop communication between any nodes on different local area networks, a virtual network must be built within the existing network architecture to enable direct communication between any two nodes. This allows for direct connection by simply providing the target network address, eliminating the need for users to consider routing relationships; the underlying routing automatically handles this. Furthermore, an algorithmic architecture has been designed to ensure that if a link is disconnected, the underlying routing automatically selects another path.

[0064] In order to achieve the above functions, it is necessary to enable routing forwarding on the master node drone and configure a static routing table. Figure 3 As shown, taking cluster A and cluster C as an example, in order to connect any node in cluster A to any node in cluster C, it is necessary to configure static routing in A and write down all possible routing link addresses. Figure 3 The network architecture of this embodiment requires the configuration of four static routes:

[0065] Target network segment: B's LAN (122.x), next hop address: B's Ad-Hoc address;

[0066] Target network segment: C's LAN (123.x), next hop address: C's Ad-Hoc address;

[0067] Target network segment: B's LAN (122.x), next hop address: C's Ad-Hoc address;

[0068] Target network segment: C's LAN (123.x), next hop address: B's Ad-Hoc address;

[0069] 3.1) Inter-cluster anti-interference reliable communication based on multi-hop network:

[0070] After constructing the initial routing table, a dynamic routing scheme was designed. This means that each time a route is found, the shortest available path in the routing table is used as the current route. For example, when drone A2 wants to connect to drone C2, it first searches the routing table for the link with the shortest hop count, such as A2-AC-C2. However, sometimes a link is interrupted. When this happens, the next shortest link is selected. For example, if the link between drones A and C is disconnected, the route will be A2-ABC-C2. This does not affect the pairwise communication between other links. This scheme ensures reliable communication even in the event of a link interruption.

[0071] 3.2) Intra-cluster anti-interference reliable communication based on dual links:

[0072] Intra-cluster communication defaults to the 5.8 GHz network to support the larger data transmission requirements. If this link is interrupted, data transmission will automatically switch to the corresponding 2.4 GHz link, ensuring stable and reliable communication even in the presence of intra-cluster interference.

[0073] The specific implementation of the present invention is as follows:

[0074] First, build the basic hardware structure, including configuring the network card mode, setting the subnet range, and determining unique IP addresses for the same Mac address across different LANs. Next, build the upper-level software structure, including the automatic cluster switching algorithm, configured in each child node, and a dynamic routing algorithm based on the virtual network. The routing table must be configured in the master node drone, and the corresponding dynamic routing algorithm must be implemented. Finally, use ping to test connectivity between any two nodes before performing upper-level custom data transmission.

[0075] The above describes in detail the preferred embodiments of the present invention. It should be understood that those skilled in the art can make numerous modifications and variations based on the concepts of the present invention without inventive effort. Therefore, any technical solutions that can be derived by those skilled in the art through logical analysis, reasoning, or limited experimentation based on the concepts of the present invention and the prior art should be within the scope of protection defined by the claims.

Claims

1. A clustered UAV self-organizing network communication architecture, characterized by: The communication architecture includes multiple independent clusters of drones, each of which includes a master node drone and the other drones in the cluster are sub-node drones. The master node drone is configured with multiple network card modes, including wireless access point mode AP and peer-to-peer mode Ad-Hoc; The network card mode of the sub-node drone is configured to be set to client mode AC; The AP-AC communication protocol is used between the master node drone and the sub-node drones in the cluster. An independent subnet is allocated to each cluster under the AP-AC communication protocol to form its own local area network. The communication between the master node drones in the cluster adopts the Ad-Hoc protocol. The Ad-Hoc mode communication between the master node drones in the cluster builds a virtual network and configures a static routing table for each master node drone to achieve direct communication between any two nodes.

2. The clustered UAV self-organizing network communication architecture according to claim 1, characterized in that: All possible routing link addresses are configured in the routing table, including the address of the local area network where the target network segment is located and the next hop address.

3. The clustered UAV self-organizing network communication architecture according to claim 1, characterized in that: The intra-cluster UAV communication uses dual frequency bands, including a main frequency band and a backup frequency band. When the main frequency band is interfered with, the backup frequency band is used for communication.

4. The clustered UAV self-organizing network communication architecture according to claim 3, characterized in that: The main frequency band of the intra-cluster UAV communication is 5.8 GHz, and the backup frequency band is 2.4 GHz; Inter-cluster UAV communication uses the 2.4 GHz frequency band.

5. A method for transmitting information of a drone group, characterized in that: The method is applied to the clustered UAV self-organizing network communication architecture according to any one of claims 1 to 4, and the steps include: The master node drone starts routing forwarding and configures a static routing table; Search the routing table for the link with the shortest hop count as the current route for communication.

6. The method for transmitting information of a drone group according to claim 5, characterized in that: In the case of a certain inter-cluster communication interruption, a second shortest link is selected from the remaining links in the routing table for multi-hop communication.

7. The method for transmitting information of a drone group according to claim 5, characterized in that: When a communication link in the cluster is interrupted, the communication link switches to the corresponding backup frequency band for data transmission.

8. A self-adaptive clustering method for drone swarms, characterized in that: The method is applied to the clustered UAV self-organizing network communication architecture according to any one of claims 1 to 4, and the steps include: Initialize drone clustering; The sub-node drone regularly detects the signal strength of all master node drones and compares the first signal RSSI of the master node drone with the strongest signal with the second signal RSSI of the master node drone currently connected; If the difference between the first signal RSSI and the second signal RSSI is greater than the set signal RSSI threshold, the first switching flag is set; A switching timer is set, and if the switching timer reaches a set switching time threshold, a second switching flag is set; When the first switching flag and the second switching flag are set at the same time, the current child node drone switches to the new strongest master node drone signal network connection, and resets the first switching flag and the second switching flag.

9. The method for adaptive clustering of drone swarms according to claim 8, characterized in that: The initialization of drone clustering is as follows: Each master node drone periodically broadcasts, and the broadcast content includes ID, signal strength, and the number of currently connected nodes; For each sub-node drone, it receives the signals of all master node drones and calculates the signal RSSI of each master node drone and the number of current access nodes. N Calculate weight function; The child node drones join the master node drone with the highest score based on the weight function; After receiving the joining request from the sub-node drone, the master node drone will add the sub-node drone to its network and update its broadcast signal.

10. The method for adaptive clustering of drone swarms according to claim 8, characterized in that: The drone group increases the number of clusters by increasing the number of topological Ad-Hoc nodes, and increases the number of drones in the cluster by adding new child node drones.

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