Minimum-time-delay routing path generation method applied to multi-channel unmanned aerial vehicle cluster

Abstract

The invention discloses a minimum-time-delay routing path generation method applied to a multi-channel unmanned aerial vehicle cluster, and belongs to the field of unmanned aerial vehicle communication network routing path optimization generation. Wherein in the link attribute calculation process, external channel input information is received to calculate network link rate information; according to the route path generation process, information such as a link connection relation and media time in an unmanned aerial vehicle cluster network is generated based on OLSR basic ad hoc network protocol broadcast, a route path with the minimum media time is selected, information transmission delay is reduced, the network throughput is improved, and the network congestion degree is reduced. According to the method, the routing path with the minimum media time can be actively selected in a high-dynamic and multi-channel unmanned aerial vehicle cluster communication environment, the information transmission reliability of the network is improved, the network delay is reduced, the throughput of the whole network is improved, and rapid control and reliable guarantee of the unmanned aerial vehicle cluster network are guaranteed.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of unmanned aerial vehicle communication network routing path optimization generation, and particularly relates to a minimum time delay routing path generation method applied to a multi-channel unmanned aerial vehicle cluster. BACKGROUND

[0002] With the significant progress of unmanned equipment in flight endurance, intelligent flight control and load-bearing capacity, the research on unmanned aerial vehicle cluster communication network continues to rise in the academic and industrial circles, and the application exploration is also increasingly in-depth. After long-term practice and polishing, the practical application of unmanned aerial vehicles in the low-altitude economic industry has entered a mature stage, and has made outstanding contributions to the improvement of industry production efficiency. However, as the application scenarios of unmanned aerial vehicle communication network continue to expand, many difficult problems have gradually emerged. In the traditional unmanned cluster network, the routing path of the unmanned cluster network only adopts the shortest path routing algorithm, and takes the number of hops as the selection metric of the routing path, without considering the delay attribute of the routing link in the network, which greatly affects the multi-hop delivery of control information of the unmanned cluster network and limits the real-time control and high-speed transmission of the unmanned cluster network.

[0003] In the running process of unmanned aerial vehicle communication nodes, the geographical environment and electromagnetic environment of each node are quite different, and the channel state also exists. In the actual communication process, when the ground user communicates with other nodes in the network, the information packet usually has to go through multiple relay nodes from the source node to the destination node, and the communication condition of each relay node is complex and changeable. If the control information is simply transmitted according to the minimum hop routing path of the routing protocol, the real-time link state is ignored, which may seriously damage the control effect of the entire network system. Therefore, it is not feasible to simply use the minimum hop routing algorithm to determine the final communication link, and it is necessary to combine the link state information to select the optimal routing path in the current multi-channel network, and transmit the calculated optimal routing information to the networking system, so as to ensure the efficient operation of the network and maximize the transmission efficiency of the network.

[0004] The current routing algorithm in the unmanned cluster network has defects, and the existing research has proposed various improvement schemes, but there are still significant limitations, mainly including (1) short-sightedness of the greedy algorithm: the path selection strategy based on a single indicator (such as the number of hops) ignores the link stability and link bandwidth, leading to network congestion and resource waste. (2) Real-time defect of machine learning method: although the routing prediction model based on deep learning can capture the traffic pattern, it relies on a large amount of historical data for training, and the model inference delay affects the timeliness of routing decision, which is difficult to cope with sudden network changes. These problems have led to the fact that the existing unmanned cluster network and vehicle networking routing technology have been difficult to guarantee the fast and reliable delivery of network control messages, and it is necessary to propose a routing path generation algorithm combined with the network channel state to ensure the fast and reliable delivery of control information in the network. SUMMARY

[0005] Therefore, the application proposes a minimum time delay routing path generation method applied to a multi-channel unmanned aerial vehicle cluster.

[0006] To achieve the above purpose, the technical scheme adopted by the application is as follows:

[0007] The minimum time delay routing path generation method applied to a multi-channel unmanned aerial vehicle cluster comprises the following steps:

[0008] Step 1: Obtain multi-channel link attribute information: receive external input information, including multi-channel link reference signal-to-noise ratio, system channel bandwidth, node transmission power, and communication distance between nodes;

[0009] Step 2: Based on the external input information, calculate the link information rate of transmitting a unit information packet between the current neighbor nodes;

[0010] Step 3: Calculate the transmission time of transmitting a unit information packet through a link, i.e. the media time, and assign it as a link weight to the link attribute, which is broadcast to the whole network through the OLSR routing protocol;

[0011] Step 4: Calculate the shortest path based on the weight: use the Dijkstra algorithm to generate a routing path based on the minimum media time, and complete the minimum time delay routing path generation applied to the multi-channel unmanned aerial vehicle cluster.

[0012] Further, the specific way in step 2 is:

[0013] Calculate the link information rate between the current node k and node k+1 :

[0014] ;

[0015] Wherein, S(k,k+1) represents the reference signal-to-noise ratio between node k and node k+1, B(k,k+1) represents the system bandwidth between node k and node k+1, and p represents the node transmission power, D(k,k+1) represents the communication distance between node k and node k+1.

[0016] Further, the specific way of step 3 is:

[0017] Calculate the information transmission time of transmitting a unit information packet Q between node k and node k+1, i.e. the media time :

[0018] ;

[0019] ; denoted as the link weight between node k and node k+1, is broadcasted to the whole network by OLSR routing protocol.

[0020] Further, the specific manner of step 4 is as follows:

[0021] In step 401, a set of unmanned aerial vehicle network nodes V is defined, and the source node S is stored in the set V, and the link weight value, i.e., the media time, between the source node S and all nodes connected to the source node S is calculated, and the above-mentioned nodes are marked in the set W, and then the node corresponding to the current minimum media time is collected into the set V.

[0022] In step 402, the adjacent nodes of the next hop node that has been collected are found, the node corresponding to the minimum media time is selected from the nodes that have not been marked by the path, the node is marked, and the node is collected into the set V.

[0023] In step 403, the next hop node of the node in step 402 is found, the media time on the path is calculated, the node is marked, if there is a node that has been marked by the remaining links, the link weights of different paths to the marked node are compared, the path with the minimum link weight is retained, and the remaining paths are discarded and no longer calculated, if all are nodes that have not been marked, the routing path with the minimum media time is found, and the node is put into the set V.

[0024] In step 404, step 403 is repeated until all nodes are marked, and the routing path in the set V is the routing path with the minimum media time.

[0025] When the target node D appears in the set V, in the subsequent repeated step 403, no new target node is expanded, and only the routing path with the minimum media time and the target node D is updated.

[0026] Compared with the prior art, the beneficial effects of the present application are as follows:

[0027] 1. The present application improves the throughput of the entire multi-channel network and guarantees the improvement of network performance.

[0028] 2. The present application reduces the transmission delay of control messages in the entire network and guarantees the fast transmission of information in the multi-channel network environment.

[0029] 3. The routing path of the present application can accelerate the transmission of data, avoid the large amount of congestion of information in a node in the minimum hop routing path of the unmanned network, and cause the increase of the information packet loss rate, and guarantee the reliability of the unmanned aerial vehicle cluster network. BRIEF DESCRIPTION OF DRAWINGS

[0030] Figure 1The overall flowchart of the minimum time delay routing path generation method applied to the multi-channel unmanned aerial vehicle cluster in the embodiment of the present application is shown.

[0031] Figure 2 The routing path schematic diagram in the embodiment of the present application is shown.

[0032] Figure 3 The detailed flowchart of the embodiment of the present application is shown. Figure 1 DETAILED DESCRIPTION

[0033] The present application is further described below in combination with the accompanying drawings and specific embodiments.

[0034] The minimum time delay routing path generation method applied to the multi-channel unmanned aerial vehicle cluster mainly includes a link attribute calculation process and a routing path generation process. By inputting the predicted external space environment information (including the reference signal-to-noise ratio of the channel in the space environment, the channel bandwidth, and the spatial distance information of the surrounding nodes) in advance, the communication rate of all links in the network is calculated, and the transmission time of the transmission unit information packet through the link, i.e., the medium time, is calculated according to the communication rate. Due to the existence of complex electromagnetic environment interference and different channel states in the air, the communication rate of each link is different, which forms a multi-channel network. The communication link state calculation is based on the initial space environment information, the reference signal-to-noise ratio, and the distance between the nodes in the space to solve the medium time consumed by the transmission of a unit data packet, and finally based on the Dijkstra algorithm, the routing path with the minimum medium time is solved.

[0035] As shown in Figure 1 , the method mainly includes the following steps:

[0036] Step (1), data input: input the external space environment information including the reference signal-to-noise ratio, the channel bandwidth, the information transmission power, and the distance between the nodes in the space.

[0037] Step (2), communication rate calculation: based on the collected reference signal-to-noise ratio and the distance between the nodes in the space, the link communication rate is finally calculated.

[0038] Step (3), link medium time calculation: the medium time consumed by the transmission of a unit data packet Q through the known link is calculated.

[0039] Step (4), minimum medium time routing path calculation: based on the Dijkstra algorithm, the total minimum medium time of a unit service data packet from the source node S to the destination node D is solved, and finally the path is selected as the final routing path for data transmission.

[0040] ​The following is a more specific example: a minimum latency routing path generation method applied to a multi-channel unmanned aerial vehicle cluster, including a link attribute calculation process and a routing path generation process.

[0041] The link attribute calculation process refers to calculating the link rate of the communication link between all aerial unmanned vehicles in the initial state. Subsequently, the time consumed by the transmission of a unit data packet Q through the link, i.e., the media time, can be calculated based on the communication rate of the communication link between any two connected nodes. The purpose of the link minimum media time calculation process is to introduce all external data into the system to calculate link information, providing a basis for subsequent routing path selection decisions. As shown in Figure 3 , the specific steps are as follows:

[0042] (1) Data input: The initial link rate calculation module needs to obtain external spatial environment information and the distance between aerial unmanned vehicle nodes, etc. The data types mainly include: the reference signal-to-noise ratio (reference SNR) between node k and node k+1 in the external environment, the system bandwidth between node k and node k+1 , the node transmission power p, the communication distance between node k and node k+1 , and the information amount of a unit data packet Q;

[0043] (2) Link rate calculation: based on the reference signal-to-noise ratio and the communication distance between aerial unmanned vehicle nodes, the link communication rate between the kth unmanned vehicle node and the k+1th unmanned vehicle node is calculated as:

[0044]

[0045] The routing path generation process mainly includes link media time calculation and routing path selection based on minimum media time, as shown in Figure 2 , assuming that there are H nodes in the network topology table formed by the source node S through the OLSR protocol in the unmanned aerial vehicle cluster network. The specific steps are as follows:

[0046] (1) Link media time calculation: based on the link rate, the media time of unit information transmission between the kth unmanned vehicle and the k+1th unmanned vehicle node is calculated as:

[0047]

[0048] (2) Link media total time calculation: assuming that there are N nodes in the final routing path table, after the information transmission media time from the kth node unmanned vehicle to the k+1th unmanned vehicle is known, the media time from the 1st node unmanned vehicle to the Nth node unmanned vehicle in the routing path table is calculated as:

[0049] ​

[0050] (3) Minimum media time routing path selection: after getting the media time between all nodes, the problem is the TSP Traveling Salesman Problem, i.e. the shortest path problem of a directed weight graph, and finally Dijkstra algorithm is used to solve the problem, and the final problem is:

[0051]

[0052] In solving the Traveling Salesman Problem, Dijkstra algorithm can be used to solve the shortest path with total weight, i.e. the routing path with minimum media time from the source node to the destination node. The steps of using Dijkstra algorithm to solve the routing path with minimum media time are as follows:

[0053] (1) Define the set of unmanned aerial vehicle network nodes V, and the set of network nodes is W, and the source node S is stored in the set V. Starting from the source node, i.e. S in the graph, the weight value, i.e. the media time, of all nodes connected with S is calculated, and the nodes are marked, and the node with the minimum media time is collected into V.

[0054] (2) Find the adjacent nodes of the next hop node in the above collected nodes, and select the node with the shortest distance from the starting point from the nodes which are not marked by the path, mark it, and collect it into the set V.

[0055] (3) Find the next hop node in the nodes in step (2), calculate the media time on the path, and mark it. If there is a node which has been marked by other links among them, calculate the link weight of different paths to the marked node, keep the path with the minimum link weight, and discard the path with larger weight and no longer calculate, if all are unmarked nodes, find the routing path with the minimum media time, and put the node into the set V.

[0056] (4) Repeat step (3) until the target node D is added to the set V, and all nodes in the set W are marked, then stop the loop, and the routing path in the set V is the routing path with the minimum media time.

[0057] Those skilled in the art will appreciate that the embodiments described are to help the reader understand the principles of the present application, and should be understood as the scope of protection of the present application is not limited to the embodiments described. For those skilled in the art, the present application can have various modifications and changes. Any modification, equivalent replacement, improvement, etc. within the spirit and principles of the present application shall be included in the scope of claims of the present application.

Claims

1. A method for minimum latency routing path generation applied to multi-channel UAV swarm, characterized in that, Comprising the following steps: Step 1, obtaining multi-channel link attribute information: receiving external input information, including multi-channel link reference signal-to-noise ratio, system channel bandwidth, node transmission power and inter-node communication distance; Step 2, based on external input information, calculating the link information rate of transmitting a unit information packet between the current neighbor nodes; Step 3, calculating the transmission time of transmitting a unit information packet through the link, i.e. the media time, and taking it as the link weight value in the link attribute, which is broadcast to the whole network through the OLSR routing protocol; Step 4, calculating the shortest path based on the weight: using Dijkstra algorithm, generating a routing path based on the minimum media time, and completing the minimum delay routing path generation applied to the multi-channel unmanned aerial vehicle cluster.

2. The minimum latency routing path generation method applied to a multi-channel UAV swarm according to claim 1, wherein, The specific way in step 2 is: calculating the link information rate between the current node k and node k+1 : ； wherein, denotes the reference signal-to-noise ratio between node k and node k+1, denotes the system bandwidth between node k and node k+1, and p denotes the node transmit power, denotes the communication distance between node k and node k+1.

3. The minimum latency routing path generation method applied to a multi-channel UAV swarm according to claim 2, characterized in that, The specific way of step 3 is: The information transmission time of a transmission unit information packet Q between the computing node k and the node k+1, i.e. the medium time is: ； Let denote the link weight between node k and node k+1, which is broadcast to the whole network by the OLSR routing protocol.

4. The minimum latency routing path generation method applied to a multi-channel UAV swarm according to claim 3, characterized in that, The specific way of step 4 is: Step 401, define the unmanned aerial vehicle network node set V, and store the source node S in the set W, calculate the link weight value between all nodes connected with S, i.e. the media time, and mark the above nodes in the set W, and then collect the node corresponding to the current minimum media time into V; Step 402, find the adjacent nodes of the next hop node of the above collected node, select the minimum media time corresponding node from the nodes which have not been marked by the path, mark it, and collect the node into the set V; Step 403, find the next hop node of the node in step 402, calculate the media time on the path, mark the node, if there is a node which has been marked by the rest of the link, calculate the link weight of different paths to the marked node, keep the path with the minimum link weight and discard the rest of the path, if all are unmarked nodes, find the routing path with the minimum media time, and put the node into the set V; Step 404, repeat step 403 until all nodes are marked, then the routing path in the set V is the routing path with the minimum media time; When the target node D appears in the set V, in the subsequent repeated step 403, new target nodes are not expanded, only the routing path with the minimum media time and the target node D is updated.

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