Relay communication control method for unmanned aerial vehicle cluster inter-networking

Abstract

The application discloses a relay communication control method for unmanned aerial vehicle cluster interconnection networking, and steps include: S01. In the access planning stage, the communication relay demand of the task unmanned aerial vehicle is analyzed according to the flight path planning and flight speed of each task unmanned aerial vehicle, the position relation of the task unmanned aerial vehicle required for relay is acquired, and the number of the required relay unmanned aerial vehicle is preliminarily determined, and finally the number of the required relay unmanned aerial vehicle, the time and position of the first access are determined; S02. In the online planning stage, the current position information and historical position information of all task unmanned aerial vehicles are fused and estimated, the motion state of each task unmanned aerial vehicle is predicted, the path of each relay unmanned aerial vehicle is planned with the global connectivity optimization as the target, and the running state output of each relay unmanned aerial vehicle is obtained. The application can realize intelligent access and autonomous path planning of the relay unmanned aerial vehicle, and has the advantages of simple implementation method, good cluster network communication performance and communication quality, strong flexibility and the like.

Description

Technical Field

[0001] This invention relates to the field of unmanned aerial vehicle (UAV) swarm control technology, and in particular to a relay communication control method for inter-UAV swarm networking. Background Technology

[0002] Unmanned aerial vehicles (UAVs) are used in swarms, forming aerial networks to perform various tasks, such as building regional aerial emergency communication base stations for disaster relief and conducting coordinated regional rescue and search operations. Aerial communication between UAVs is crucial for constructing a UAV swarm information network. However, factors such as terrain, buildings, distance between UAVs, and flight attitude can hinder the quality of direct communication between UAVs, making it difficult to transmit mission information. For example, in urban disaster relief emergency communications, UAVs equipped with communication systems may be unable to communicate directly due to tall buildings or mountainous terrain, creating communication islands. To address this, relay communication UAVs are used to supplement aerial communication networks, building a collaborative communication layer to maintain smooth communication within the UAV swarm and ensure successful mission execution.

[0003] Unmanned aerial vehicles (UAVs) have a wide range of applications, with relay communication being a key area of ​​application. Compared to communication based on ground base stations and satellite systems, UAV relay communication offers significant advantages: First, rapid deployment. Leveraging the rapid maneuverability of low- and medium-altitude UAVs, short-range line-of-sight (LAS) and medium- to long-range non-line-of-sight (NFS) communication links can be established in many situations, improving communication performance between nodes. Second, easy attitude adjustment. The UAV's movement and attitude can be dynamically adjusted to adapt to changes in the communication environment (e.g., terrain obstruction, attenuation due to rain or fog, or communication noise or interference), enhancing the environmental adaptability of the communication link. Finally, low maintenance costs. UAV systems have lower operating and maintenance costs, and their takeoff and recovery are more flexible, allowing for deployment at any time, making them suitable for unexpected events or short-duration missions.

[0004] Drones play a crucial role in communication relay, with three main application areas: First, regional coverage and emergency communication: deploying drones to assist existing communication infrastructure in providing seamless wireless coverage within the service area. Application scenarios include infrastructure damage caused by natural disasters and base station overload in extremely congested areas. Second, long-distance communication: drone relay provides wireless connectivity between two remote users or user groups without a reliable direct link, such as for emergency response between the front lines and command centers. Third, information collection and distribution: drones assist in information distribution and data collection, dispatching drones to distribute or collect latency-tolerant messages from a large number of distributed wireless devices (e.g., wireless sensors in precision agriculture applications).

[0005] Research on relay UAVs for collaborative communication methods and applications between UAVs and ground stations, as well as between UAVs in the air, faces three main challenges: First, when is relay communication needed, i.e., the time and location of the relay UAV joining; second, how to plan the flight path of the relay UAV in real time to ensure normal communication at both ends of the link; and third, how to ensure the communication quality of the inter-UAV network under conditions where the cluster topology changes due to the aggregation or separation of multiple UAVs in the cluster due to mission / target (for example, multiple UAVs aggregate to form a formation to coordinate reconnaissance and strike a target, and after the mission is completed, the multiple UAVs disperse and, through online resource coordination, aggregate with other UAVs to coordinate and execute the next mission).

[0006] In collaborative communication applications, relay communication is often an effective way to improve throughput, reliability, and extend communication range. Depending on the service recipients and transmission services, UAV relay applications can be mainly divided into two categories: relay UAV positioning-based area coverage communication and relay UAV moving area coverage communication; relay UAV static area coverage communication; single relay UAV dynamic area coverage communication; and multi-relay UAV dynamic area coverage communication. Currently, most research on UAV communication relay focuses on ground users. However, in the complex environment of the battlefield, relay users are no longer stationary or moving at low speeds on the ground, but rather face UAV swarms with wide coverage and high-speed movement. UAV swarms are divided into different groups to perform tasks in different areas, requiring not only communication between UAVs and ground stations but also information exchange between UAVs themselves. Therefore, when to dispatch relay UAVs and how to plan their routes become essential considerations. Summary of the Invention

[0007] The technical problem to be solved by this invention is: in view of the technical problems existing in the prior art, this invention provides a relay communication control method for inter-UAV cluster networking that is simple to implement, has good cluster network communication performance and communication quality, and is highly flexible, and can realize intelligent access planning and autonomous path planning for relay UAVs.

[0008] To solve the above-mentioned technical problems, the technical solution proposed by this invention is as follows:

[0009] A relay communication control method for inter-UAV swarm networking includes the following steps:

[0010] S01. During the access planning phase, based on the flight path planning and flight speed of each mission UAV, the communication relay requirements of the mission UAVs are analyzed to obtain the positional relationships of the mission UAVs that need to be relayed and to preliminarily determine the minimum number of relay UAVs required; based on the obtained positional relationships of the mission UAVs that need to be relayed and the preliminarily determined minimum number of relay UAVs required, the final number of relay UAVs required and the time and location of the first access are determined.

[0011] S02. During the online planning phase, the current and historical location information of all mission drones are obtained and fused for estimation to predict the motion state of each mission drone. The path of each relay drone is planned with the goal of optimizing global connectivity, and the operating status output of each relay drone is obtained to control each relay drone.

[0012] Furthermore, in step S01, the step of analyzing the communication relay requirements of the mission UAV includes:

[0013] S101. Based on the speed of each mission UAV, acquire the corresponding waypoint position information along the route at the same time step to form a waypoint database for the mission UAV.

[0014] S102. Access the waypoint database of all UAVs in order of time step, and generate minimum spanning trees at different times according to the time step;

[0015] S103. Traverse each connecting edge of each minimum spanning tree, calculate the connectivity value of each connecting edge, and if the connectivity value of the target connecting edge is greater than a preset threshold, obtain the target connecting edge and the corresponding two task drones to form the required relay drones and their timing. Based on whether the timing of the required relay drones contains the same connection point, preliminarily determine the minimum number of required relay drones.

[0016] Furthermore, in step S103, a relay link requirement time series library is constructed by connecting edges with connectivity values ​​greater than a preset threshold, corresponding times, and the topological relationships between corresponding UAVs.

[0017] Furthermore, in step S01, the steps of finally determining the required number of relay drones and the time and location of the first access include:

[0018] S111. Read the data in the relay link demand time sequence library and determine whether the preset calling conditions are met. If yes, proceed to step S112; otherwise, return to step S111.

[0019] S112. Select the network node location of a relay drone in the cluster task area;

[0020] S113. Calculate the minimum spanning tree network topology based on the current position of the mission drone;

[0021] S114. Calculate and sort the global network link connectivity values;

[0022] S115. Determine whether the current worst link connectivity value is greater than the preset threshold. If so, add 1 relay drone and return to step S112 to select the location of the next relay drone. Otherwise, output the current number of relay drones and the access location.

[0023] Furthermore, step S111 includes: determining whether there are common connection points at the same time in the input relay drone node pair sequence; if there are no common connection points, it is determined that the calling condition is met; if there are common connection points, the shared relay drone method scheduling is executed first; if the connectivity value of the connecting edge is still greater than the preset threshold after the shared relay drone method scheduling is executed, a relay drone is added and then the process proceeds to step S112 to start the relay drone access planning.

[0024] Furthermore, in step S02, the Kalman filter method is used to perform fusion estimation of the motion state of the mission UAV in order to predict the UAV's motion state, including:

[0025] In the prediction phase, the state at the current time is predicted based on the optimal estimate of the state at time k-1, where the motion of the mission UAV is estimated using an acceleration dynamics model;

[0026] During the observation update phase, the predicted values ​​are corrected using the actual measured state values ​​obtained by the mission UAV, based on the actual measured state values, to obtain the optimal state estimate at the current time k.

[0027] Furthermore, the discrete system equations of the acceleration dynamics model are expressed as follows:

[0028] x m (k+1)=F k x m (k)+η k

[0029] in, The state of the mission UAV at time k includes the UAV's coordinates, velocity, and acceleration in the x and y directions, η. k The process noise representing acceleration characteristics, σ 2 F represents the variance of the Gaussian white noise used. k The motion state transition matrix for the mission-critical UAV is as follows:

[0030]

[0031] in, α is a preset coefficient value based on the type of moving object to be simulated.

[0032] Furthermore, a distributed model predictive control (DMPC) method is used to plan the path of the relay UAV online. Each relay UAV independently predicts its future state based on the estimated position of the mission UAV and the positions of other UAVs, and optimizes its own control input based on the prediction results.

[0033] Furthermore, in the online path planning of the relay UAV using the Distributed Model Predictive Control (DMPC) method, when constructing the assumed control input and output states for each UAV node, it is assumed that the control input is the result of shifting the optimal solution of the local optimum problem in the previous time step. The optimal estimate is calculated using a rolling time domain method. Each time the window slides for one cycle / step, the first value in the current sliding window is removed and the input value of the next time step is added to the last position.

[0034] Furthermore, in the Distributed Model Predictive Control (DMPC) method, the locally optimal control problem of the relay UAV m is:

[0035]

[0036]

[0037]

[0038]

[0039]

[0040]

[0041]

[0042]

[0043] Where t is the current time, k is the time step, and m represents the current relay drone. Indicates the predicted motion state, X represents the assumed state of motion. re For each relay drone, form a set of states, X ts The relay drone is used to predict the location of the mission drone through Kalman filtering. Indicates the predicted control input, d represents the assumed control input. safe To maintain a safe distance between drones.

[0044] Compared with the prior art, the advantages of the present invention are as follows:

[0045] 1. This invention analyzes the positional relationships and time sequences of the drones requiring relay based on the flight path planning results of each drone in the cluster, determines the minimum number of drones requiring relay, and then simulates the initial access of the relay drones. Based on the performance of the cluster network, it determines whether additional relay nodes are needed, and finally determines the number of relay drones and their access locations. This enables global planning for the deployment of relay drone access.

[0046] 2. This invention obtains and fuses the current and historical location information of all mission UAVs to predict the motion state of each mission UAV, and plans the path of each relay UAV with the goal of optimizing global connectivity. This enables autonomous route planning of relay UAVs for stable relay communication in multi-aircraft collaborative missions.

[0047] 3. Furthermore, this invention further improves the accuracy and flexibility of autonomous route planning for relay UAVs by using Kalman filtering for motion state estimation under the condition of only obtaining the position information of the task UAV accessing the relay link, with the goal of establishing good connectivity of the relay link, and by combining the method of distributed model predictive control (DMPC) to realize online planning of relay UAV paths. This can further improve the accuracy and flexibility of autonomous route planning for relay UAVs and further improve the communication performance of the cluster network. Attached Figure Description

[0048] Figure 1 This is a schematic diagram illustrating the implementation process of the relay communication control method for inter-drone networking in this embodiment.

[0049] Figure 2 This is a schematic diagram illustrating the process of analyzing and implementing the communication relay requirements of the mission UAV in this embodiment.

[0050] Figure 3 This is a schematic diagram of the implementation process of the initial access location planning of the UAV in this embodiment.

[0051] Figure 4 This is a schematic diagram of the online planning process for the next UAV in this embodiment.

[0052] Figure 5 This is a schematic diagram illustrating the basic process principle for constructing the assumed control input in this embodiment. Detailed Implementation

[0053] The present invention will be further described below with reference to the accompanying drawings and specific preferred embodiments, but this does not limit the scope of protection of the present invention.

[0054] like Figure 1 As shown, the steps of the relay communication control method for inter-UAV cluster networking in this embodiment include:

[0055] S01. During the access planning phase, the ground control station analyzes the communication relay requirements of the mission UAVs based on the flight path planning and flight speed of each mission UAV, obtains the positional relationship of the mission UAVs to be relayed, and preliminarily determines the number of relay UAVs required; based on the obtained positional relationship of the mission UAVs to be relayed, the preliminarily determined number of relay UAVs required, and the performance of the cluster network, the final number of relay UAVs required and the time and location of the first access are determined.

[0056] S02. During the online planning phase, the current and historical location information of all mission drones are obtained and fused for estimation to predict the motion state of each mission drone. The path of each relay drone is planned with the goal of optimizing global connectivity, and the operating status output of each relay drone is obtained to control each relay drone.

[0057] This embodiment first constructs an evaluation model for inter-machine links in the cluster: based on the minimum spanning tree method of graph theory, all task UAVs and relay UAVs constitute the vertices of the graph, and the communication links between each pair of UAVs constitute the edges of the graph. A global optimized link topology relationship between the cluster UAVs is constructed. At the same time, the weight of the link edge is set to be negatively correlated with the probability of successful transmission of the link message. The minimum sum of the edge weights is the maximum sum of the probability of successful transmission of the link message corresponding to the minimum spanning tree. The weights of the fully connected graph are minimized. The maximum sum of the transmission probabilities of all link messages in the inter-machine communication network is taken as the optimization target after relay access.

[0058] Assuming there are N mission drones, let p be the set of drone positions at any given time during the decision-making and deployment relay. ts ={p ts,n |n=1,…,N},p ts,n =(x ts,n ,y ts,n ) T Deploy M relay drones at location p re ={p re,m |m=1,…,M},p re,m =(x re,m ,y re,m ) T All mission drones and relay drones are connected into a graph, and the probability of successful message transmission for each link is calculated based on the distance between each pair of nodes. Then, the weights of the corresponding edges are calculated. The weight matrix W∈R forms the graph. (N+M)×(N+M) The Kruskal algorithm is used to optimize the solution of the connection matrix A = {a} of the spanning tree by minimizing the sum of edge weights of all connected nodes. ij |i,j=1,…,M+N}, where a ij=1 indicates that the ij link is a branch of the spanning tree; the UAV forms the connection topology of the entire network according to the minimum spanning tree, and the sum of the weights of all edges of the spanning tree is the sum of the probabilities that the message is transmitted to all UAV nodes through the spanning tree.

[0059] This embodiment specifically uses global message connectivity and worst-case link connectivity as evaluation metrics. The calculation expression for the global message connectivity evaluation metric is as follows:

[0060]

[0061] Where N is the number of mission drones, M is the number of relay drones, and matrix element a ij =1 indicates that the direct link between drone i and drone j is a branch of the minimum spanning tree;

[0062] The expression for calculating the evaluation metric for worst-case link connectivity is:

[0063]

[0064] like Figure 2 As shown, in step S01 of this embodiment, the step of analyzing the communication relay requirements of the mission UAV includes:

[0065] S101. Based on the speed of each mission UAV, acquire the corresponding waypoint position information along the route at the same time step to form a waypoint database for the mission UAV.

[0066] S102. Access the waypoint database of all UAVs in order of time step, and generate minimum spanning trees at different times according to the time step;

[0067] S103. Traverse each connecting edge of each minimum spanning tree, calculate the connectivity value of each connecting edge, and if the connectivity value of the target connecting edge is greater than a preset threshold, obtain the target connecting edge and the corresponding two task drones to form the required relay drones and their timing. Based on whether the timing of the required relay drones contains the same connection point, preliminarily determine the minimum number of required relay drones.

[0068] In step S103 above, a relay link requirement time series library is constructed by connecting edges with connectivity values ​​greater than a preset threshold, corresponding times, and the topological relationships between corresponding UAVs.

[0069] In a specific application embodiment, the algorithm for analyzing the communication relay needs of the task UAVs is configured according to the above steps. This algorithm takes into account the trajectory planning results of the task UAVs and the flight speeds of each aircraft, and outputs the sequence of positional relationships and times of the task UAVs requiring relay, as well as the minimum number of relay UAVs required. In this algorithm, firstly, according to the headings of each task UAV, a step size (step_time) is set, and the minimum spanning tree between the task UAVs at different step times in the task flow is calculated. Then, each connecting edge of each minimum spanning tree is traversed; if the connectivity is greater than a threshold, it is recorded as <(node_pair(i,j)). pos(pos_i,pos_j)step_time_stamp)>, where node_pair is the node pair formed by two task drones i and j at the two ends of the connecting edge, forming the drones and time sequence that need communication relay; then, by analyzing whether there are common connection points among the time sequence sequences of each drone node_pair that needs communication relay, if there are common connection points, it can be considered that a relay communication needs to be added, which will change the topology and achieve connectivity less than the threshold; if there are no common connection points, it indicates that different drones may be needed to meet the communication between different pairs. Based on the above analysis, the minimum number of relay drones required can be preliminarily determined.

[0070] like Figure 3 As shown, in step S01 of this embodiment, the steps of finally determining the required number of relay drones and the time and location of the first access include:

[0071] S111. Read the data in the time sequence library of relay link requirements according to the time step, and determine whether the preset calling conditions are met. If they are met, proceed to step S112; otherwise, return to step S111.

[0072] S112. Select the network node location of a relay drone in the cluster task area;

[0073] S113. Calculate the minimum spanning tree network topology based on the current position of the mission drone;

[0074] S114. Calculate and sort the global network link connectivity values;

[0075] S115. Determine whether the current worst link connectivity value is greater than the preset threshold. If so, add 1 relay drone and return to step S112 to select the location of the next relay drone. Otherwise, output the current number of relay drones and the access location.

[0076] Step S111 above includes: determining whether there are common connection points at the same time in the input relay drone node pair sequence; if there are no common connection points, it is determined that the calling condition is met; if there are common connection points, the shared relay drone method scheduling is executed first; if the connectivity value of the connecting edge is still greater than the preset threshold after the shared relay drone method scheduling is executed, a relay drone is added and then the process proceeds to step S112 to start the relay drone access planning.

[0077] In a specific application embodiment, the access planning algorithm for relay drones is configured according to the above steps to achieve access planning for relay drones. The algorithm takes into account the sequence of positional relationships and times of the task drones requiring relay, and the minimum number of relay drones needed. It outputs the number of relay drones and the time and location of the first access. The calling condition in this algorithm is set as follows: whether the initially input node_pair sequence has a shared connection point at the same time. If there is no shared connection point, the access planning algorithm for that relay drone is called. If there is a shared connection point, the shared relay drone scheduling algorithm is first used to achieve scheduling based on the shared relay drone. If, after the algorithm calculation, there are still edge connectivity values ​​greater than a threshold, an additional relay drone is added before calling the access planning algorithm for the relay drones again. The specific execution flow of the above relay drone access planning algorithm includes:

[0078] (1) Relay drone location selection: Traverse the network node locations of relay drones in the cluster task area, and select locations outside the safe zone of each drone;

[0079] (2) Minimum spanning tree network topology: Each time the location of the relay UAV is obtained, the location of the task UAV at the current time is calculated to obtain the minimum spanning tree network topology;

[0080] (3) Calculate and sort the global link connectivity;

[0081] (4) Continue traversing the next relay drone and return to step (1) until all relay drones have been traversed.

[0082] In the above-mentioned access planning algorithm for relay drones, the specific steps for determining whether to add a relay drone are as follows: Select the network topology with the best global connectivity, and determine whether the connectivity of all links is greater than the threshold. If not, the calculation ends, and the currently determined relay drone and its access location are output. If it is, it indicates that the number of relays is insufficient, and one more relay drone needs to be added. The access planning algorithm is then called again, so that the number of relay drones required can be obtained in the end, ensuring that the worst-case link connectivity is less than the threshold.

[0083] like Figure 4As shown, in this embodiment, during the online autonomous route planning of the UAV, the current position of all task UAVs is obtained, along with the position information of the previous time step and earlier task UAVs, to predict the motion trend of the task UAVs in the next time step. Specifically, the Kalman filter method is used to fuse and estimate the motion state of the task UAVs. The task UAV motion estimation based on Kalman filter specifically includes:

[0084] In the prediction phase, the state at the current moment is predicted based on the optimal estimate of the state at time k-1, where the motion of the mission UAV is estimated using an acceleration dynamics model;

[0085] During the observation update phase, the predicted values ​​are corrected using the actual measured state values ​​obtained by the mission UAV, based on the actual measured state values, to obtain the optimal state estimate at the current time k.

[0086] Considering information security, relay UAVs can only obtain the position and status information of the mission UAVs that are connected to the relay, but not the flight path information. Therefore, it is necessary to predict the motion trend of the mission UAVs first. This embodiment uses Kalman filtering for motion state estimation, which is mainly divided into two stages. The first stage is the prediction stage, which predicts the current state based on the optimal estimate of the state at time k-1. However, the predicted empirical value may have a certain error, which can be regarded as Gaussian white noise. That is, we obtain the "predicted value" and "error" of the previous time. The second stage is the observation update stage, which corrects the predicted value based on the actual measured state value. There is also a measurement error in the actual measurement. The measured value is used to correct the predicted value to obtain the optimal state estimate at time k.

[0087] For the prediction phase, this embodiment uses an acceleration dynamics model to estimate the motion of the mission UAV. By defining acceleration as a correlation process with a decaying exponential autocorrelation function, it is implied that if an acceleration exists at time k, it is very likely to be exponentially correlated at some time. The discrete system equations (A Jerk Model) of the acceleration dynamics model used in this embodiment are expressed as follows:

[0088] x m (k+1)=F k x m (k)+η k (3)

[0089] in, Let η be the state of the mission UAV at time k, including the coordinates, velocity, and acceleration of the mission UAV in the x and y directions. k The process noise representing acceleration characteristics, specifically in this embodiment, has a mean of 0 and a variance of σ. 2 Gaussian white noise.

[0090] F kThe motion state transition matrix for the mission-specific UAV:

[0091]

[0092] in, T s The sampling time (i.e., the distance between the sampling and the sampling time) is α, which is a preset coefficient. The value of α is related to the type of moving object being simulated. When the moving object moves slowly, the value of α is small, while when the object moves fast, the value of α is relatively large.

[0093] For the second observation update phase, this embodiment equips the mission UAV with GPS, and the obtained self-position coordinates can be transmitted to the relay UAV. These position coordinates are actual measured values, which also have measurement errors, namely:

[0094] z(k)=Hx(k)+v k (5)

[0095] Where z(k) represents the measurement vector, x(k) represents the state vector, and v k Represents measurement noise with a mean of 0 and a covariance matrix R. k for σ x and σ y Let be the standard deviations of the x and y coordinates. H is the measurement matrix, specifically:

[0096]

[0097] To ensure information security, relay UAVs are typically configured to only acquire the position and performance data of the mission UAV; waypoints and other mission-related information are not available. After reaching the initial deployment position, the relay UAV follows the mission UAV to maintain link stability. Based on this, this embodiment, under the condition of predicting the mission UAV's motion state, uses a relay UAV autonomous route planning algorithm based on the DMPC model to plan the relay UAV waypoint for the next moment with the goal of optimizing global connectivity. That is, it employs online path planning for the relay UAV based on distributed model predictive control (DMPC). The DMPC algorithm combines a finite time window with the nonlinear model of the UAV system, considering the motion and link connectivity throughout the prediction process, and discretizes the time window into H... p By using the same time step, the best possible combination of control inputs is determined through optimization, and the optimization is iterated repeatedly until the relay drone mission is completed.

[0098] In this embodiment, during autonomous route planning for relay UAVs based on a distributed prediction model, each relay UAV independently optimizes its control input according to the estimated position of the mission UAV and the predictions of other UAVs regarding future states, based on preset results. This effectively ensures network communication quality. A key part of the DMPC algorithm is how to construct assumed control input and output states for each UAV node. For example... Figure 5 As shown, when DMPC constructs the assumed control input and output states for each UAV node, the assumed control input is the result of shifting the optimal solution of the local optimum problem in the previous time step. The optimal estimate is calculated using a rolling time domain method, which means that a window with a fixed step size slides forward between cycles. Each time the window slides for one cycle / step, the first value in the current sliding window is removed and the input value of the next time step is added to the last position. The optimal estimate is calculated, which can ensure that the aircraft flies smoothly, that is, the changes in speed and angular velocity are controlled within a certain range.

[0099] Set the planning time domain step size (i.e., the rolling time domain step size) of DMPC to H. p There are N fixed-wing UAVs cooperating to perform a mission, and we only consider their motion in a two-dimensional plane. The current time is t, the k-th step size in the planning time domain is denoted as (k|t), and the state set is denoted as X. ts (k|t)={x ts,n If (k|t)|n=1,…,N}, then the state of the nth mission drone can be written as:

[0100]

[0101] If M drones are configured as relay communication drones, then at the k-th time step, the relay state of the m-th drone is set as follows:

[0102] x re,m (k|t)=[x re,m (k|t),y re,m (k|t),ψ re,m (k|t),v re,m (k|t),ω re,m (k|t)] T (8)

[0103] Then all relay drones form a set X at k-step states. re (k|t), denoted as X re (k|t)={x re,m (k|t)m=1,…,M}. The input control quantity for each relay is:

[0104]

[0105] This embodiment plans in the time domain [t, t+H]p Within this section, three types of relay UAV states are defined: predicted motion state. Optimal state of motion and the assumed state of motion This represents the output motion state, which is parameterized from the local optimal control problem in the planning time domain. This represents the optimal solution obtained by numerically solving a local problem. The assumed output state, to be transmitted to other relay drones, is shifted one step forward for optimization in the next time domain of the remaining relay drones. Simultaneously, three types of control inputs are defined: predicted control inputs... Optimal control input and assumed control input

[0106] Based on the above definition, this embodiment constructs the local optimal control problem for the relay UAV m as follows:

[0107]

[0108]

[0109]

[0110]

[0111]

[0112]

[0113] Where a represents the assumed input, p represents the predicted value, v0 represents the standard speed of the drone, and u v,max u represents the maximum speed difference between the drone's speed and the standard speed. ω,max This represents the maximum value of the angular velocity scalar of the UAV, where i represents the label of the relay UAV, and Hp represents the rolling time step. The assumed state for the remaining relay drones, specifically calculated in the time domain of the previous planning, d safe To maintain a safe distance between drones, any two drones must remain at least a safe distance from each other. ts The location of the mission drone is predicted by Kalman filtering for the relay drone. (C) re ∈R 2×5 and C ts ∈R 2×6 The coordinates used to extract the x and y directions from the relay and mission UAVs are as follows:

[0114]

[0115] In this embodiment, the network connectivity metric is selected from one of three network performance metrics based on the actual task requirements. JI(·) is used to refer to the connectivity metric in general, and the specific description is as follows:

[0116]

[0117] φ(X re (H p |t),X ts (H p |t))=p c JI(C re X re (H p |t),C ts X ts (H p |t)). (12)

[0118] Here, l(·) comprises two parts: the network connectivity index and the energy consumption from the control input. φ(·) is the network connectivity index at the last moment in the planning time domain, and p c q c r v r ω These are the weighting coefficients.

[0119] This invention addresses the problem of poor inter-drone communication caused by distance or terrain obstruction during the use of drone swarms. It constructs a collaborative communication layer using relay drones and employs a multi-relay drone collaborative communication planning approach that combines global deployment and local optimization. During the global planning of relay drone access deployment, the ground control station obtains drone swarm status information to plan the number of relay drones and their access locations. Then, during online route planning for the relay drones, a Kalman filter method is used to fuse and estimate the motion state of the mission drones, predicting their motion state. A distributed model predictive control (DMPC) method is then used to plan the relay drone paths online with communication link stability as the objective, achieving efficient relay collaborative communication.

[0120] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention in any way. Although the present invention has been disclosed above with reference to preferred embodiments, it is not intended to limit the invention. Therefore, any simple modifications, equivalent changes, and alterations made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention should fall within the protection scope of the present invention.

Claims

1. A relay communication control method for a UAV cluster inter-machine networking, characterized by the steps of Comprise: S01. In the access planning phase, the communication relay demand of the task unmanned aerial vehicle is analyzed according to the flight path planning and flight speed of each task unmanned aerial vehicle, the position relationship of the required relay task unmanned aerial vehicle is obtained, and the minimum number of required relay unmanned aerial vehicles is preliminarily determined; according to the position relationship of the required relay task unmanned aerial vehicle and the minimum number of required relay unmanned aerial vehicles preliminarily determined, the number of required relay unmanned aerial vehicles and the time and position of first access are finally determined; S02. In the online planning phase, the current position information and historical position information of all task unmanned aerial vehicles are fused and estimated to predict the motion state of each task unmanned aerial vehicle, and the path of each relay unmanned aerial vehicle is planned with the global connectivity optimization as the target to obtain the running state output of each relay unmanned aerial vehicle to control each relay unmanned aerial vehicle. 2.The method of claim 1, wherein, In the step S01, the step of analyzing the communication relay demand of the task unmanned aerial vehicle comprises: S101. According to the speed of each task unmanned aerial vehicle, the position information of the corresponding waypoint on the route is obtained in the same time step to form a waypoint database of the task unmanned aerial vehicle; S102. Access the waypoint database of all unmanned aerial vehicles in time step order, and generate the minimum spanning tree at different time according to the time step; S103. Traverse each connection edge of each minimum spanning tree, calculate the connectivity value of each connection edge, if the connectivity value of the target connection edge is greater than the preset threshold, the target connection edge and the corresponding two task unmanned aerial vehicles form the required relay unmanned aerial vehicle and the time sequence, according to whether the time sequence of the required relay unmanned aerial vehicle contains the same connection point, preliminarily determine the minimum number of required relay unmanned aerial vehicles. 3.The method of claim 2, wherein, In the step S103, the topological relationship between the connection edge with the connectivity value greater than the preset threshold and the corresponding time and unmanned aerial vehicle is constructed to form a relay link demand time sequence database. 4.The method of claim 1, wherein, In the step S01, the step of finally determining the number of required relay unmanned aerial vehicles and the time and position of first access comprises: S111. Read the data in the relay link demand time sequence database, judge whether the preset calling condition is met, if yes, go to step S112, otherwise return to step S111; S112. Select a relay unmanned aerial vehicle at the network node position in the cluster task area; S113. Calculate the minimum spanning tree network topology according to the current time task unmanned aerial vehicle position; S114. Calculate the global network link connectivity value and sort it; S115. Judge whether the current worst link connectivity value is greater than the preset threshold, if yes, increase 1 relay unmanned aerial vehicle and return to step S112 to select the position of the next relay unmanned aerial vehicle, otherwise output the current obtained number of relay unmanned aerial vehicles and the access position. 5.The method of claim 4, wherein, The step S111 comprises: judging whether the input relay unmanned aerial vehicle node pair sequence has a common connection point at the same time, and if not, determining that the calling condition is met, and if yes, executing a common relay unmanned aerial vehicle method scheduling first, and if the connectivity value of the connection edge is greater than a preset threshold after executing the common relay unmanned aerial vehicle method scheduling, adding a relay unmanned aerial vehicle and then entering the step S112 to start relay unmanned aerial vehicle access planning.

6. The method according to any one of claims 1 to 5, wherein In the step S02, a Kalman filtering method is used to perform fusion estimation on the motion state of the task unmanned aerial vehicle to predict the motion state of the unmanned aerial vehicle, comprising: In the prediction stage, the state at the current time is predicted according to the optimal estimation value of the state at the k-1 time, wherein the acceleration dynamics model is used to estimate the motion of the task unmanned aerial vehicle; In the observation update stage, the predicted value is corrected by using the actual measurement value obtained by the task unmanned aerial vehicle according to the actual measured state value, to obtain the optimal state estimation at the current k time. 7.The method of claim 6, wherein, The discrete system equation of the acceleration dynamics model is represented as: x m (k+1) = F k x m (k) + η k wherein, is the state of the task UAV at time k, which includes the task UAV's x and y coordinates, velocity, and acceleration, η k is the process noise representing the acceleration characteristics, σ 2 is the variance of the Gaussian white noise employed, F k is the task UAV motion state transition matrix, which is given by: wherein, a is a coefficient value preset according to a type of a moving object of a simulation desired.

8. The method according to any one of claims 1 to 5, wherein In the step S02, a distributed model predictive control (DMPC) method is used to plan the path of the relay unmanned aerial vehicle online, wherein each relay unmanned aerial vehicle independently predicts the future state according to the estimated position of the task unmanned aerial vehicle and the positions of other unmanned aerial vehicles, and optimizes the control input of the current relay unmanned aerial vehicle according to the prediction result. 9.The method of claim 8, wherein, In the step of planning the path of the relay unmanned aerial vehicle by using the distributed model predictive control (DMPC) method, when constructing the assumed control input and output state for each unmanned aerial vehicle node, it is assumed that the control input quantity is the result of shifting the optimal solution of the local optimal problem at the last time, and the optimal estimation value is calculated by using the rolling horizon method, and each time the cycle / step is slid, the first value in the current sliding window is removed, and the input value at the next time is added at the last position. 10.The method of claim 8, wherein, In the distributed model predictive control (DMPC) method, the constructed local optimal control problem of the relay unmanned aerial vehicle m is: where t is the current time, k is the time step number, m represents the current relay UAV, denotes the predicted motion state, denotes the assumed motion state, X re is the state formation set of all relay UAVs, X ts is the task UAV position predicted by the relay UAV through Kalman filtering, denotes the predicted control input, denotes the assumed control input, d safe is the safety distance between UAVs.

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