A multi-unmanned aerial vehicle cooperative communication relay path planning method

Abstract

The present application relates to the field of unmanned aerial vehicle communication, in particular to a multi-unmanned aerial vehicle cooperative communication relay path planning method, the multi-unmanned aerial vehicle cooperative communication relay path planning method includes the establishment of the visibility model and the description and solution of the optimization problem, the relay unmanned aerial vehicle is introduced between the task unmanned aerial vehicle and the ground station, the communication relay link between the relay unmanned aerial vehicle and the task unmanned aerial vehicle and the ground station is constructed, and the communication transmission between the task unmanned aerial vehicle and the ground station is maintained; based on the minimum energy consumption criterion, a multi-step optimization model considering the visibility constraint and the unmanned aerial vehicle maneuverability is established, and the optimization problem is solved by a particle swarm optimization algorithm. The present application solves the problem of unmanned aerial vehicle communication interruption in complex mountainous environment, avoids the communication link interruption caused by the task unmanned aerial vehicle flying through the corner of the mountain in a short time, and ensures the smooth communication link in complex mountainous environment by applying the method to multi-unmanned aerial vehicle cooperative relay communication.

Description

Technical Field

[0001] This invention relates to the field of unmanned aerial vehicle (UAV) communication, and more specifically to a relay path planning method for multi-UAV collaborative communication. Background Technology

[0002] The Yarlung Tsangpo River is one of the highest-altitude plateau rivers in the world, with undulating canyon terrain on both banks. When geological disasters such as glacial landslides occur, rescue personnel and equipment cannot reach the target location, necessitating the deployment of drones to survey the disaster area. Due to the undulating mountainous terrain along the Yarlung Tsangpo River, the mission drones must fly at low altitudes along the river surface. When the mission drones fly over bends in the mountains, the communication link with the ground station may be interrupted due to the obstruction of the mountains.

[0003] To address this issue, one approach is to consider the physical layer. UAVs can use a fixed altitude to find the optimal relay location between base stations to facilitate communication. However, when performing beyond-line-of-sight observation missions in complex mountainous areas such as plateaus, the mission UAVs often have to fly through winding mountain terrain, making it difficult for the relay UAV to maintain its relay function at a fixed location throughout the entire mission. To enable UAVs to dynamically adjust their relay positions, a free-space optical relay communication system can be constructed. This involves UAVs carrying laser transmitters or receivers and dynamically adjusting their spatial position according to the channel environment to achieve relay functionality. However, free-space optical relay communication systems require strict line-of-sight links and are significantly affected by weather. The above methods study the communication strength between UAV nodes from the perspective of communication channel models. However, for complex mountainous environments, the obstruction of mountains and climate changes render free-space optical relay communication systems unsuitable, and the communication channel models in mountainous environments are complex and difficult to construct. Summary of the Invention

[0004] The problem solved by this invention is: to address the issue of communication link interruption between multi-rotor UAVs and ground stations caused by mountain obstruction during low-altitude flight in complex mountainous areas. This invention proposes a relay path planning method for collaborative communication among multiple UAVs, which solves the problem of communication link interruption between multi-rotor UAVs and ground stations during low-altitude flight in complex mountainous areas.

[0005] The solution of this invention is: a relay path planning method for multi-UAV cooperative communication, comprising the following steps:

[0006] Step 1: Data Acquisition and Preprocessing: Extract latitude, longitude, and elevation information from the specified offline map, and select any point as the origin to transform the latitude and longitude coordinate system into the local coordinate system using the Miller projection method;

[0007] Step 2, Line-of-sight model establishment: Based on the local coordinate system and elevation information, construct the communication relay line-of-sight links between the relay UAV and the mission UAV, and between the relay UAV and the ground station;

[0008] Step 3, Optimize Problem Description: Abstract the multi-UAV cooperative communication problem into a relay path planning problem. Based on the flight distance minimization criterion, establish a multi-step optimization model with the relay UAV's movement step length as the objective function, considering line-of-sight constraints and UAV maneuverability.

[0009] Step 4, Relay Path Planning: Based on the multi-step optimization model, combined with the mission UAV path and ground station location, the relay UAV path is obtained through particle swarm optimization.

[0010] Step 2 includes the following steps:

[0011] Step 2.1: Construct the spatial equation of the straight line connecting the relay UAV and the ground station or mission UAV.

[0012]

[0013] Among them, (x m ,y m ,z m (x) is the location of the ground station or mission drone. r ,y r ,z r ) represents the location of the relay drone, and t0 is a proportionality constant;

[0014] Step 2.2: Construct the parametric equation of the spatial line using t0 as the parameter.

[0015]

[0016] Step 2.3: Using the location of the ground station or mission UAV as the initial point and the location of the relay UAV as the endpoint, take one point at intervals of interval, for a total of n points. The value of n is obtained by the following formula:

[0017]

[0018] The function INT() means rounding down to the nearest integer, discarding the decimal.

[0019] Step 2.4: Take (x) i ,y i ,z i ) i∈1,2,3,…,n, where i represents the i-th point on the spatial connection between the ground station or mission UAV and the relay UAV, and parameter t i We obtain it from the following formula:

[0020]

[0021] Step 2.5: Calculate the altitude value z of the i-th point on the spatial line connecting the ground station or mission UAV and the relay UAV. i We obtain the following formula:

[0022] z i =z m +t i ×(z r -z m );

[0023] Step 2.6: Calculate the height difference Δz at the i-th point. i We obtain the following formula:

[0024] Δz i =z i -z D (x i ,y i )

[0025] Among them, z D (x i ,y i ) represents the coordinates (x, y) in the elevation information. i ,y i The corresponding surface elevation value;

[0026] Step 2.7: Obtain the line-of-sight between the relay UAV and the ground station, using the following formula:

[0027] IVD=min{Δz1,Δz2,…,Δz n}

[0028] Wherein, IVD represents the line-of-sight between the relay UAV and the ground station or mission UAV, and is the minimum value among n altitude differences; when IVD≥0, it is a communication relay line-of-sight link, that is, a line-of-sight state; when IVD<0, it is a non-communication relay line-of-sight link, that is, a non-line-of-sight state.

[0029] Step 3 includes the following steps:

[0030] Step 3.1: Construct the objective function. The objective function of the multi-step optimization model is established as the sum of the multi-step movement distances of the relay UAV, obtained through the following formula:

[0031]

[0032] The time interval between each moment is 10 seconds, t represents the time variable, t = 0, 10, 20, ..., T-10, T represents the total flight time, k = 1, 2, 3, ..., j, j represents the maximum number of prediction steps;

[0033] Step 3.2: Set line-of-sight constraints: The line-of-sight constraints between the relay UAV and the ground station at time t+10k are obtained by the following formula:

[0034]

[0035] The line-of-sight constraint between the relay UAV and the mission UAV at time t+10k is obtained by the following formula:

[0036]

[0037] in, Let represent the altitude difference between the relay UAV and the ground station at the i-th point, and the altitude difference between the relay UAV and the mission UAV at the i-th point, respectively.

[0038] Step 3.3: Set ellipsoidal constraints: The relay position relative to the current time and the next time step must be reachable within 10 seconds, obtained through the following formula:

[0039]

[0040] Where a, b, and c represent the maximum flight distances of the relay drone along the X, Y, and Z directions within 10 seconds, respectively.

[0041] Step 3.4: Establish a multi-step optimization model: Considering the step size, visibility constraint, and ellipsoid constraint at time t+10k, the following formula is used:

[0042]

[0043] Step 4 includes the following steps:

[0044] The relay UAV path is treated as a particle, and the fitness is the objective function of the multi-step optimization model. Combined with the pre-planned mission UAV path and ground station location, the optimal relay UAV path is obtained through particle swarm optimization.

[0045] A relay path planning system for multi-UAV cooperative communication includes:

[0046] The data acquisition and preprocessing module is used to extract latitude, longitude and elevation information from a specified offline map, and to transform the latitude and longitude coordinate system into the local coordinate system using the Miller projection method with the first point as the origin.

[0047] The line-of-sight model building module is used to construct communication relay line-of-sight links between relay UAVs and mission UAVs, and between relay UAVs and ground stations;

[0048] The optimization problem description module is used to abstract the multi-UAV cooperative communication problem into a relay path planning problem. Based on the flight distance minimization criterion, a multi-step optimization model is established with the relay UAV's movement step length as the objective function, taking into account the line-of-sight constraints and the UAV's maneuverability.

[0049] The relay path planning module is used to combine the initial positions of the mission UAV and the ground station and solve the optimization problem using the particle swarm optimization algorithm to calculate the relay path that satisfies the relay function.

[0050] A relay path planning system for multi-UAV cooperative communication includes a memory and a processor; the memory is used to store a computer program; the processor is used to implement the relay path planning method for multi-UAV cooperative communication when the computer program is executed.

[0051] A computer-readable storage medium storing a computer program that, when executed by a processor, implements a relay path planning method for multi-UAV cooperative communication as described above.

[0052] The present invention has the following advantages and beneficial effects:

[0053] 1. This invention extracts latitude, longitude, and elevation information from a specified offline map using data acquisition and preprocessing methods, and transforms the latitude and longitude coordinate system into a local coordinate system using the Miller projection method with the first point as the origin. This method eliminates the need to store map information, freeing up memory and improving algorithm efficiency.

[0054] 2. This invention establishes a line-of-sight model to make the communication relay links between the relay UAV and the mission UAV, and between the relay UAV and the ground station, line-of-sight links, thereby enhancing communication quality and meeting communication requirements. This method uses line-of-sight instead of signal strength, eliminating the need to construct channel models for complex mountainous environments and is unaffected by adverse weather conditions.

[0055] 3. This invention establishes a multi-step optimization model based on the flight distance minimization criterion, comprehensively considering line-of-sight constraints and UAV maneuverability. This method innovatively abstracts the multi-UAV cooperative communication problem into a relay path planning problem, enabling the planning of relay UAV flight paths that satisfy relay functions, thus preventing communication link interruptions caused by mission UAVs flying over mountain bends in a short time. Attached Figure Description

[0056] Figure 1 This is a flowchart illustrating the task of autonomously planning communication relay paths according to the present invention. Detailed Implementation

[0057] The specific embodiments of the present invention will be described in further detail below with reference to the accompanying drawings and specific embodiments.

[0058] This invention relates to the field of unmanned aerial vehicle (UAV) communication, specifically to a relay path planning method for multi-UAV cooperative communication. The method includes establishing a line-of-sight model and describing and solving an optimization problem. A relay UAV is introduced between the mission UAV and the ground station, constructing communication relay links between the relay UAV and the mission UAV, and between the relay UAV and the ground station, maintaining communication transmission between the mission UAV and the ground station. Based on the minimum energy consumption criterion, a multi-step optimization model considering line-of-sight constraints and UAV maneuverability is established, and the optimization problem is solved using a particle swarm optimization algorithm. This relay path planning method solves the problem of UAV communication interruption in complex mountainous environments. The establishment of the line-of-sight model ensures that the relay UAV remains in a communication relay link state, and the establishment of the multi-step optimization model effectively plans the flight path of the relay UAV, preventing communication link interruption caused by the mission UAV flying over mountain bends in a short time. Applying this method to multi-UAV cooperative relay communication ensures uninterrupted communication links in complex mountainous environments.

[0059] This invention proposes a relay path planning method for multi-UAV collaborative communication. The main features of this method are: 1) the relay UAV autonomously finds the relay location to avoid communication link interruption caused by the mission UAV flying over the mountain bend in a short time; 2) line of sight is used instead of signal strength, eliminating the need to build a channel model in complex mountainous environments and being unaffected by severe weather; 3) no need to store a large amount of map information, freeing up memory and improving algorithm efficiency.

[0060] A relay path planning method for multi-UAV cooperative communication, through the establishment of a line-of-sight model and the description and solution of the optimization problem, realizes the planning of relay paths for UAV communication, including the following steps:

[0061] Data Acquisition and Preprocessing: Extract latitude, longitude, and elevation information from the specified offline map, and transform the latitude and longitude coordinate system into the local coordinate system using the Miller projection method with the first point as the origin; Line-of-Sight Model Establishment: Construct a line-of-sight communication relay link between the relay UAV and the mission UAV, and between the relay UAV and the ground station; Optimization Problem Description: Abstract the multi-UAV cooperative communication problem into a relay path planning problem. Based on the flight distance minimization criterion, establish a multi-step optimization model with the relay UAV's movement step length as the objective function, considering line-of-sight constraints and UAV maneuverability; Relay Path Planning: Combine the initial positions of the mission UAV and the ground station, and solve the optimization problem using the particle swarm optimization algorithm to calculate the relay path that satisfies the relay function.

[0062] The establishment of the visibility model includes the following steps:

[0063] Step 1: Construct the spatial equation of the straight line connecting the relay UAV and the ground station.

[0064]

[0065] Among them, (x m ,y m ,z m (x) is the location of the ground station. r ,y r ,z r ) represents the location of the relay drone, and t0 is a proportionality constant;

[0066] Step 2: Construct the parametric equation of the spatial line using t0 as the parameter.

[0067]

[0068] Step 3: Using the ground station location as the initial point and the relay UAV location as the endpoint, take one point at intervals of interval, for a total of n points. The value of n is obtained by the following formula:

[0069]

[0070] The function INT() means rounding down to the nearest integer, discarding the decimal.

[0071] Step 4: Take (x) i ,y i ,z i Let i ∈ 1, 2, 3, ..., n be any point on the spatial line connecting the ground station and the relay UAV, and let parameter t be an arbitrary point. i We obtain it from the following formula:

[0072]

[0073] Step 5: Calculate the altitude value z of the i-th point on the spatial connection between the ground station and the relay UAV. i We obtain the following formula:

[0074] z i =z m +t i ×(z r -z m );

[0075] Step 6: Calculate the height difference Δz at the i-th point. i We obtain the following formula:

[0076] Δz i =z i -z D (x i ,y i )

[0077] Among them, z D (x i,y i ) represents the coordinates (x, y) in the actual elevation information. i ,y i The corresponding surface elevation value;

[0078] Step 7: Calculate the line-of-sight between the relay UAV and the ground station, obtained using the following formula:

[0079] IVD=min{Δz1,Δz2,…,Δz n}

[0080] IVD represents the line-of-sight between the relay UAV and the ground control terminal, and is the minimum value among n altitude differences; when IVD≥0, it is a line-of-sight link, i.e., a line-of-sight state; when IVD<0, it is a non-line-of-sight link, i.e., a non-line-of-sight state.

[0081] The description of the optimization problem includes the following steps:

[0082] Step 1: Construct the objective function. To save energy and minimize the distance the relay UAV travels at each moment, the objective function of the multi-step optimization model is established as the sum of the multi-step travel distances of the relay UAV, obtained through the following formula:

[0083]

[0084] The time interval between each moment is 10 seconds, t represents the time variable t=0,10,20,…,T-10, T represents the total flight time, k=1,2,3,…,j, j represents the maximum number of prediction steps;

[0085] Step 2: Set line-of-sight constraints. Considering the impact of mountain obstruction, to ensure the relay UAV fulfills its relay function, the relay UAV must maintain line-of-sight with both the mission UAV and the ground station at all times, i.e., the line-of-sight must be greater than or equal to 0. Therefore, the line-of-sight constraint between the relay UAV and the ground station at time t+10k is obtained by the following formula:

[0086]

[0087] The line-of-sight constraint between the relay UAV and the mission UAV at time t+10k is obtained by the following formula:

[0088]

[0089] Let $i$ and $j$ represent the altitude difference between the relay UAV and the ground station at the i-th point, and $i$ and $j$ represent the altitude difference between the relay UAV and the mission UAV at the i-th point, respectively. The altitude difference between the relay UAV and the mission UAV is obtained similarly, using the same principle as calculating the altitude difference and line-of-sight between them; simply replace the ground station's location with the mission UAV's location.

[0090] Step 3: Set ellipsoidal constraints, considering the maneuverability of the relay UAV. The relay position at the next moment must be reachable within 10 seconds, which is obtained through the following formula:

[0091]

[0092] Where a, b, and c represent the maximum flight distances of the relay drone along the X, Y, and Z directions within 10 seconds.

[0093] Step 4: Establish a multi-step optimization model, considering the step size, visibility constraint, and ellipsoid constraint at time t+10k, and obtain the following formula:

[0094]

[0095] This invention proposes a relay path planning method for multi-UAV cooperative communication. The method includes establishing a visibility model, describing and solving the optimization problem, and having the relay UAV autonomously plan its communication relay path. This solves the problem of communication link interruption between multi-rotor UAVs and ground stations caused by mountain obstruction during low-altitude flight in complex mountainous areas. The specific steps are as follows:

[0096] (1) Data acquisition and preprocessing

[0097] Extract latitude, longitude, and elevation information from a specified offline map, and transform the latitude and longitude coordinate system into a local coordinate system using the Miller projection method with the first point as the origin.

[0098] (2) Establishment of the visibility model

[0099] Within the nominal communication range, the signal strength of the line-of-sight link is sufficient to meet the communication transmission between the UAV and the ground station. Therefore, a line-of-sight model is established to determine the signal propagation mode between the UAV and the ground station, so that the communication relay link between the relay UAV and the mission UAV, and between the relay UAV and the ground station, is a line-of-sight link.

[0100] (3) Optimize the problem description

[0101] The problem of multi-UAV cooperative communication is abstracted into a relay path planning problem. Based on the flight distance minimization criterion, a multi-step optimization model is developed with the relay UAV's movement step length as the objective function, taking into account line-of-sight constraints and UAV maneuverability.

[0102] (4) Relay path planning

[0103] By combining the initial positions of the mission UAV and the ground station, and using the particle swarm optimization algorithm to solve the optimization problem, a relay path that satisfies the relay function is calculated. The relay UAV path is treated as a particle, and the fitness is the objective function of the multi-step optimization model. The objective function describes the constraints between the relay UAV, the mission UAV, and the ground station. Combining the pre-planned mission UAV path and the ground station position, the optimal relay UAV path is obtained through the particle swarm optimization algorithm.

[0104] The mission drone path and ground station location are pre-planned, fixed, and known, and will not change throughout the particle swarm optimization (PSO) process. To solve for the relay drone path, given the known mission drone path and ground station location, the PSO algorithm can directly plan the relay drone path.

[0105] Specific examples are as follows:

[0106] The geographic information used in the selected simulation area was sampled from the actual Yarlung Tsangpo River region. Elevation data was collected at 5-meter intervals across the mission area. The actual area included the mission UAV, relay UAV, and a ground station. The mission UAV and relay UAV simultaneously departed from the ground station. The mission UAV flew along a predetermined path to the mission endpoint to perform a search and scan mission. The relay UAV flew along a planned relay path to maintain uninterrupted communication with the mission UAV throughout the mission. The mission UAV flew along 150 pre-planned mission path points. Taking a three-step optimization model as an example, 150 relay path points were planned based on the mission UAV path and the ground station location. The relay UAV flew along the relay path, and the total mission time was 1500 seconds.

[0107] See Figure 1 The task flowchart for autonomously planning communication relay paths is as follows: First, extract latitude, longitude, and elevation information from a specified offline map, and transform the latitude and longitude coordinate system into the local coordinate system using the Miller projection method with the first point as the origin. Second, construct line-of-sight communication relay links between the relay UAV and the mission UAV, and between the relay UAV and the ground station. Next, based on the criterion of minimizing flight distance, establish a multi-step optimization model with the relay UAV's movement step length as the objective function, considering line-of-sight constraints and UAV maneuverability. Finally, combine the initial positions of the mission UAV and the ground station, and solve the optimization problem using the particle swarm optimization algorithm to plan a relay path that satisfies the relay function.

[0108] This invention proposes an autonomous relay path planning method for multiple unmanned aerial vehicles (UAVs). A line-of-sight model is established to ensure that the communication relay link is a line-of-sight link. A multi-step optimization model for autonomous planning of the communication relay path by the relay UAV is designed, solving the problem of communication link interruption for the mission UAV. Results show that the proposed method can effectively and autonomously plan relay paths that meet the relay function, and the relay UAV maintains uninterrupted communication for the mission UAV throughout its communication relay journey.

[0109] The specific embodiments of the present invention have been described above with reference to the accompanying drawings. However, these descriptions should not be construed as limiting the scope of the present invention. The scope of protection of the present invention is defined by the appended claims, and any modifications based on the claims of the present invention are within the scope of protection of the present invention.

Claims

1. A relay path planning method for multi-UAV cooperative communication, characterized in that, Includes the following steps: Step 1: Data Acquisition and Preprocessing: Extract latitude, longitude, and elevation information from the specified offline map, and select any point as the origin to transform the latitude and longitude coordinate system into the local coordinate system using the Miller projection method; Step 2, Line-of-sight model establishment: Based on the local coordinate system and elevation information, construct the communication relay line-of-sight links between the relay UAV and the mission UAV, and between the relay UAV and the ground station; Step 3, Optimize Problem Description: Abstract the multi-UAV cooperative communication problem into a relay path planning problem. Based on the flight distance minimization criterion, establish a multi-step optimization model with the relay UAV's movement step length as the objective function, considering line-of-sight constraints and UAV maneuverability. Step 4, Relay Path Planning: Based on the multi-step optimization model, combined with the mission UAV path and ground station location, the relay UAV path is obtained through particle swarm optimization.

2. The relay path planning method for multi-UAV cooperative communication according to claim 1, characterized in that, Step 2 includes the following steps: Step 2.1: Construct the spatial equation of the straight line connecting the relay UAV and the ground station or mission UAV. Among them, (x m ,y m ,z m (x) is the location of the ground station or mission drone. r ,y r ,z r ) represents the location of the relay drone, and t0 is a proportionality constant; Step 2.2: Construct the parametric equation of the spatial line using t0 as the parameter. Step 2.3: Using the location of the ground station or mission UAV as the initial point and the location of the relay UAV as the endpoint, take one point at intervals of interval, for a total of n points. The value of n is obtained by the following formula: The function INT() means rounding down to the nearest integer, discarding the decimal. Step 2.4: Take (x) i ,y i ,z i ) i∈1,2,3,…,n, where i represents the i-th point on the spatial line connecting the ground station or mission UAV and the relay UAV, and parameter t i We obtain it from the following formula: Step 2.5: Calculate the altitude value z of the i-th point on the spatial line connecting the ground station or mission UAV and the relay UAV. i We obtain the following formula: z i =z m +t i ×(z r -z m ); Step 2.6: Calculate the height difference Δz at the i-th point. i We obtain the following formula: Δz i =z i -z D (x i ,y i ) Among them, z D (x i ,y i ) represents the coordinates (x, y) in the elevation information. i ,y i The corresponding surface elevation value; Step 2.7: Obtain the line-of-sight between the relay UAV and the ground station, using the following formula: IVD=min{Δz1,Δz2,…,Δz n } Wherein, IVD represents the line-of-sight between the relay UAV and the ground station or mission UAV, and is the minimum value among n altitude differences; when IVD≥0, it is a communication relay line-of-sight link, that is, a line-of-sight state; when IVD<0, it is a non-communication relay line-of-sight link, that is, a non-line-of-sight state.

3. The relay path planning method for multi-UAV cooperative communication according to claim 1, characterized in that, Step 3 includes the following steps: Step 3.1: Construct the objective function. The objective function of the multi-step optimization model is established as the sum of the multi-step movement distances of the relay UAV, which is obtained through the following formula: The time interval between each moment is 10 seconds, t represents the time variable, t = 0, 10, 20, ..., T-10, T represents the total flight time, k = 1, 2, 3, ..., j, j represents the maximum number of prediction steps; Step 3.2: Set line-of-sight constraints: The line-of-sight constraints between the relay UAV and the ground station at time t+10k are obtained by the following formula: The line-of-sight constraint between the relay UAV and the mission UAV at time t+10k is obtained by the following formula: in, Let represent the altitude difference between the relay UAV and the ground station at the i-th point, and the altitude difference between the relay UAV and the mission UAV at the i-th point, respectively. Step 3.3: Set ellipsoidal constraints: The relay position relative to the current time and the next time step must be reachable within 10 seconds, obtained through the following formula: Where a, b, and c represent the maximum flight distances of the relay drone along the X, Y, and Z directions within 10 seconds, respectively. Step 3.4: Establish a multi-step optimization model: Considering the step size, visibility constraint, and ellipsoid constraint at time t+10k, the following formula is used:

4. The relay path planning method for multi-UAV cooperative communication according to claim 1, characterized in that, Step 4 includes the following steps: The relay UAV path is treated as a particle, and the fitness is the objective function of the multi-step optimization model. Combined with the pre-planned mission UAV path and ground station location, the optimal relay UAV path is obtained through particle swarm optimization.

5. A relay path planning system for multi-UAV cooperative communication, characterized in that, include: The data acquisition and preprocessing module is used to extract latitude, longitude and elevation information from a specified offline map, and to transform the latitude and longitude coordinate system into the local coordinate system using the Miller projection method with the first point as the origin. The line-of-sight model building module is used to construct communication relay line-of-sight links between relay UAVs and mission UAVs, and between relay UAVs and ground stations; The optimization problem description module is used to abstract the multi-UAV cooperative communication problem into a relay path planning problem. Based on the flight distance minimization criterion, a multi-step optimization model is established with the relay UAV's movement step length as the objective function, taking into account the line-of-sight constraints and the UAV's maneuverability. The relay path planning module is used to combine the initial positions of the mission UAV and the ground station and solve the optimization problem using the particle swarm optimization algorithm to calculate the relay path that satisfies the relay function.

6. A relay path planning system for multi-UAV cooperative communication, characterized in that, It includes a memory and a processor; the memory is used to store a computer program; the processor is used to implement, when executing the computer program, a relay path planning method for multi-UAV cooperative communication as described in any one of claims 1-4.

7. A computer-readable storage medium, characterized in that, The storage medium stores a computer program, which, when executed by a processor, implements a relay path planning method for multi-UAV cooperative communication as described in any one of claims 1-4.

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