A trajectory planning method, device and equipment of a UAV and a medium

Abstract

The application discloses a trajectory planning method and device of a UAV, equipment and a medium. It relates to the field of UAVs. The method determines an edge UAV according to the number of planned nodes and the number of collaborative planning nodes of the UAV, takes a task point as the last trajectory node of the edge UAV when the edge UAV is in the planning range of the corresponding task point, and determines whether there is a planable UAV among the remaining UAVs. If there is, the edge UAV among the remaining UAVs is determined. When there is not, the trajectory planning of each UAV is determined when each UAV is planned. It can be seen that this method solves the problem of multiple edge UAVs in space, ensures the safety of UAV cooperative combat by making each UAV reach the designated location at the same time, meets the conditions of stealth, avoids being discovered by the opponent's radar, and thus better performs combat tasks.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of unmanned aerial vehicles, and particularly to a trajectory planning method and device for unmanned aerial vehicles, equipment and a medium. BACKGROUND

[0002] Under the background of intelligent combat, edge unmanned aerial vehicle (UAV) technology is becoming mature. Due to the low cost, simple take-off and landing, and flexible operation of edge unmanned aerial vehicles, their importance in the military field is increasing. However, as the combat environment becomes more complex and the combat tasks become more diverse, a single edge unmanned aerial vehicle cannot complete the combat task alone, and the research on cooperative combat of multiple edge unmanned aerial vehicles is becoming increasingly important. Among them, cooperative trajectory planning is a key link in the combat of multiple edge unmanned aerial vehicles, and is an important prerequisite for improving the survival probability of edge unmanned aerial vehicles and enhancing the combat capability of edge unmanned aerial vehicles.

[0003] Traditional edge unmanned aerial vehicle trajectory planning cannot meet the operation in multi-dimensional space, or can meet the operation in multi-dimensional space but cannot meet the stealth technology of unmanned aerial vehicles, and is easily discovered by the opponent's radar.

[0004] In view of the above technical problems, seeking a trajectory planning method with cooperative stealth is a problem to be solved by those skilled in the art. SUMMARY

[0005] The purpose of the present application is to provide a trajectory planning method, device, equipment and medium for unmanned aerial vehicles, which can meet the stealth technology of unmanned aerial vehicles and is not easily discovered by the opponent's radar.

[0006] To solve the above technical problems, the present application provides a trajectory planning method for unmanned aerial vehicles, comprising:

[0007] obtaining the number of planned nodes of each unmanned aerial vehicle and the number of cooperative planning nodes of the unmanned aerial vehicles;

[0008] determining the edge unmanned aerial vehicle according to the number of planned nodes and the number of cooperative planning nodes;

[0009] judging whether the edge unmanned aerial vehicle is within the planning step range of the corresponding task point;

[0010] if yes, taking the task point as the last trajectory node of the edge unmanned aerial vehicle;

[0011] judging whether there is a planable unmanned aerial vehicle among the remaining unmanned aerial vehicles;

[0012] if yes, returning to the step of determining the edge unmanned aerial vehicle according to the number of planned nodes and the number of cooperative planning nodes;

[0013] if no, judging whether all the unmanned aerial vehicles are planned;

[0014] If the trajectories of all unmanned aerial vehicles are planned, the trajectory planning of all unmanned aerial vehicles is determined.

[0015] Preferably, before obtaining the number of planned nodes of all unmanned aerial vehicles and the number of cooperative planning nodes of unmanned aerial vehicles, it further comprises:

[0016] Determine the flight performance parameters of all unmanned aerial vehicles and the initial environmental information data, wherein the initial environmental information data at least includes terrain data and radar threat data, and the flight performance parameters at least include the maximum climb angle, the maximum dive angle, the maximum horizontal angle, the maximum flight height, the maximum flight trajectory distance and the maximum flight speed of the unmanned aerial vehicle;

[0017] Determine the basic parameters of all unmanned aerial vehicles according to the flight performance parameters and the initial environmental information data, wherein the basic parameters at least include the planning step, the planning times and the collision distance between all unmanned aerial vehicles;

[0018] According to the basic parameters, a cost function is constructed to determine the next trajectory node of all unmanned aerial vehicles.

[0019] Preferably, obtaining the number of planned nodes of all unmanned aerial vehicles and the number of cooperative planning nodes of unmanned aerial vehicles comprises:

[0020] Setting an Open table and a Closed table for all unmanned aerial vehicles;

[0021] Marking the planning mark of all unmanned aerial vehicles as true;

[0022] Taking the initial position of all unmanned aerial vehicles as the first planning node and storing it in the Open table;

[0023] Obtaining the number of planned nodes of all unmanned aerial vehicles and the number of cooperative planning nodes of unmanned aerial vehicles according to the Open table.

[0024] Preferably, determining the edge unmanned aerial vehicle according to the number of planned nodes and the number of cooperative planning nodes comprises:

[0025] Judging whether there is a unmanned aerial vehicle with a planning mark of true;

[0026] If yes, the unmanned aerial vehicle with the smallest number of planned nodes is taken as the edge unmanned aerial vehicle;

[0027] If no, entering the step of judging whether all unmanned aerial vehicles are planned.

[0028] Preferably, if the edge unmanned aerial vehicle is not within the planning step range of the corresponding task point, it further comprises:

[0029] Obtaining the current trajectory node of the edge unmanned aerial vehicle;

[0030] determining a set of next trajectory nodes of the edge UAV according to the current trajectory node and the cost function;

[0031] deleting the trajectory nodes in the set which do not satisfy the flight performance parameter;

[0032] putting the trajectory nodes in the Open table into the Closed table;

[0033] judging whether the set is an empty set;

[0034] if yes, entering a step of judging whether the edge UAV is in the planning step range of the corresponding task point in the case that the Open table is not empty;

[0035] if no, determining the next trajectory node of the edge UAV;

[0036] when the number of the planned nodes of the edge UAV and the number of the cooperatively planned nodes of the UAV are equal, entering a step of judging whether there is a UAV which can be planned to be marked true.

[0037] Preferably, if there is a UAV trajectory which is not planned to be completed, the method further comprises:

[0038] acquiring the number of times of trajectory planning failure and judging whether the number of times of trajectory planning failure is greater than a preset number of times;

[0039] if yes, determining that the trajectory planning of each UAV fails;

[0040] if no, returning to the step of determining the edge UAV according to the number of the planned nodes and the number of the cooperatively planned nodes.

[0041] Preferably, determining the trajectory planning of each UAV comprises:

[0042] determining the successfully planned trajectory nodes from the Open table of each UAV;

[0043] determining the average flight speed of each UAV and the trajectory planning through the successfully planned trajectory nodes.

[0044] To solve the above technical problems, the application further provides a UAV trajectory planning device, comprising:

[0045] an acquisition module, configured to acquire the number of the planned nodes of each UAV and the number of the cooperatively planned nodes of the UAV;

[0046] a first determination module, configured to determine the edge UAV according to the number of the planned nodes and the number of the cooperatively planned nodes;

[0047] a first judgment module, configured to judge whether the edge UAV is in the planning step range of the corresponding task point, and if yes, trigger the as module;

[0048] As a module, the task point is used as the last trajectory node of the edge unmanned aerial vehicle;

[0049] The second determining module is configured to determine whether the trajectories of the unmanned aerial vehicles are planned.

[0050] The third determining module is configured to determine whether the trajectories of the unmanned aerial vehicles are planned.

[0051] The second determining module is configured to determine whether the trajectories of the unmanned aerial vehicles are planned.

[0052] To solve the above technical problems, the application further provides a trajectory planning device of an unmanned aerial vehicle, comprising a memory for storing a computer program.

[0053] The processor is configured to implement the steps of the trajectory planning method of the unmanned aerial vehicle mentioned above when executing the computer program.

[0054] To solve the above technical problems, the application further provides a computer readable storage medium, wherein the computer readable storage medium stores a computer program, and the computer program is executed by a processor to implement the steps of the trajectory planning method of the unmanned aerial vehicle mentioned above.

[0055] The trajectory planning method of the unmanned aerial vehicle provided by the application determines the edge unmanned aerial vehicle according to the number of planned nodes and the number of cooperative planning nodes of the unmanned aerial vehicle, determines the task point as the last trajectory node of the edge unmanned aerial vehicle when the edge unmanned aerial vehicle is in the planning range of the corresponding task point, determines whether there is a planable unmanned aerial vehicle among the remaining unmanned aerial vehicles, determines the edge unmanned aerial vehicle among the remaining unmanned aerial vehicles if there is one, and determines the trajectory planning of the unmanned aerial vehicles when all the unmanned aerial vehicles are planned. It can be seen that this method solves the problem of multi-edge unmanned aerial vehicle space, ensures the safety of unmanned aerial vehicle cooperative combat by making all the unmanned aerial vehicles reach the designated location at the same time, meets the condition of stealth, avoids being discovered by the opponent's radar, and thus better performs the combat task.

[0056] The application further provides a trajectory planning device, equipment and medium of an unmanned aerial vehicle, which have the same effect. BRIEF DESCRIPTION OF DRAWINGS

[0057] To more clearly illustrate the embodiments of the application, the drawings needed in the embodiments will be briefly introduced. Obviously, the drawings in the following description are only some embodiments of the application, and other drawings can be obtained by those skilled in the art without creative labor.

[0058] Figure 1A flowchart of a trajectory planning method of a UAV provided by an embodiment of the present application;

[0059] Figure 2 A flowchart of another trajectory planning method of a UAV provided by an embodiment of the present application;

[0060] Figure 3 A structure diagram of a trajectory planning device of a UAV provided by an embodiment of the present application;

[0061] Figure 4 A structure diagram of a trajectory planning device of a UAV provided by an embodiment of the present application. DETAILED DESCRIPTION

[0062] The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative labor fall within the protection scope of the present application.

[0063] The core of the present application is to provide a trajectory planning method, device, equipment and medium of a UAV for realizing four-dimensional space and stealthy trajectory planning of a UAV. Currently, the coordination of multi-edge UAVs in time and / or space is considered as an important direction of trajectory planning research.

[0064] In recent years, stealth technology has become a high-tech of multi-disciplinary integration, and is also one of the bottleneck technologies for developing high-end edge UAVs. Taking the radar stealth technology, which is the fastest developing and most mature technology, as an example, methods such as shape stealth and material stealth are adopted to reduce the radar cross section (RCS) of the edge UAV. On this basis, in the face of the possible radar threat network in the combat environment, combined with the dynamic RCS characteristics of the edge UAV in the flight process, the radar detection range is compressed, and the penetration trajectory of the edge UAV is planned, which is an important means to improve the stealth performance of the edge UAV in the task execution process. However, with the continuous development of modern air defense systems towards high density and multi-level deployment characteristics, the traditional edge UAV trajectory planning has been unable to meet the safety of penetration flight. The traditional edge UAV trajectory planning cannot be applied in multi-dimensional space, and cannot be well stealthy, which is easy to be discovered by the enemy.

[0065] The application is directed to the problem of trajectory planning of UAVs in cooperative stealth penetration. Based on the improved sparse A* algorithm, a threat cost calculation model based on the dynamic RCS of edge UAVs, a time coordination cost calculation model, and a spatial anti-collision model of edge UAVs are proposed. An algorithm is designed and a concurrent expansion structure is proposed. A four-dimensional cooperative stealth penetration trajectory planning method for UAVs is proposed. The method improves the coordination and safety of edge UAV trajectory planning in complex planning environments, especially in high-density radar threat area coverage, and indirectly improves the cooperative combat capability of multi-edge UAVs.

[0066] To better understand the application scheme for those skilled in the art, the application will be further described in detail below in combination with the drawings and specific embodiments.

[0067] Figure 1 A flowchart of a trajectory planning method for a UAV provided by an embodiment of the application is shown in FIG. 1. Figure 1 The trajectory planning method for a UAV includes the following steps.

[0068] S10: Obtain the number of planned nodes of each UAV and the number of cooperative planning nodes of the UAV.

[0069] The specific steps of obtaining the number of planned nodes of each UAV and the number of cooperative planning nodes of the UAV include:

[0070] An Open table and a Closed table are set for each UAV.

[0071] The planning mark of each UAV is marked as true,

[0072] The initial position of each UAV is taken as the first planning node and stored in the Open table.

[0073] The number of planned nodes of each UAV and the number of cooperative planning nodes of the UAV are obtained according to the Open table.

[0074] In this embodiment, an Open table and a Closed table are set for each edge UAV. The planning marks of all edge UAVs can be initialized by referring to the Open table and Closed table structure in the SAS algorithm, and the planning marks are marked as true.

[0075] The initial position P start,i of each edge UAV is taken as the first current expansion node P expanding,i , and filled into the Open table of the edge UAV. The number of planned trajectory nodes n planned，i of each edge UAV is set to 1, and the number of cooperative planning trajectory nodes n cooperation of UAVs is set to 2.

[0076] S11: determining the edge unmanned aerial vehicle according to the planned node number and the cooperative planning node number.

[0077] The step of determining the edge unmanned aerial vehicle according to the planned node number and the cooperative planning node number specifically includes the following steps:

[0078] determining whether there is a unmanned aerial vehicle that can be planned and marked as true;

[0079] if yes, the unmanned aerial vehicle with the smallest planned node number is taken as the edge unmanned aerial vehicle;

[0080] if no, a step of determining whether all unmanned aerial vehicles are planned is entered.

[0081] If there is an edge unmanned aerial vehicle that can be planned and marked as true, the number n of planned trajectory nodes of the edge unmanned aerial vehicle (marked as true) currently participating in trajectory planning is found out. planned The edge unmanned aerial vehicle with the smallest planned trajectory node number is taken as the current trajectory expansion edge unmanned aerial vehicle. If there are multiple edge unmanned aerial vehicles with the same planned trajectory node number, one of them is randomly selected as the current trajectory expansion edge unmanned aerial vehicle. If there is no unmanned aerial vehicle that can be planned and marked as true, the planning marks of all edge unmanned aerial vehicles are set to false.

[0082] S12: determining whether the edge unmanned aerial vehicle is within the planning step range of the corresponding task point. If yes, step S13 is entered.

[0083] S13: taking the task point as the last trajectory node of the edge unmanned aerial vehicle.

[0084] S14: determining whether there is a unmanned aerial vehicle that can be planned. If yes, step S11 is returned. If no, step S15 is entered.

[0085] S15: determining whether all unmanned aerial vehicles are planned. If yes, step S16 is entered.

[0086] S16: determining the trajectory planning of each unmanned aerial vehicle.

[0087] This method solves the problem of multiple edge unmanned aerial vehicles in space, ensures the safety of unmanned aerial vehicle cooperative combat by making each unmanned aerial vehicle reach the specified location at the same time, meets the condition of stealth, avoids being discovered by the opponent's radar, and better performs the combat task.

[0088] Figure 2 Another flowchart of a trajectory planning method of an unmanned aerial vehicle provided by the embodiment of the application is provided. As a more optimal embodiment, as shown in Figure 2 before acquiring the planned node number of each unmanned aerial vehicle and the cooperative planning node number of the unmanned aerial vehicle, the following steps are further included:

[0089] S17: Determine the flight performance parameters of each UAV and the initial environmental information data. The initial environmental information data at least includes terrain data and radar threat data, and the flight performance parameters at least include the maximum climbing angle, the maximum diving angle, the maximum horizontal angle, the maximum flight height, the maximum flight trajectory distance, and the maximum flight speed of the UAV. It should be noted that the flight performance parameters and the initial environmental information data described above do not limit the flight performance parameters and the initial environmental information data in the present application, and can include more or less data than the flight performance parameters and the initial environmental information data described above. The data usage can be determined according to the specific implementation scenario to determine the implementation manner. In the present embodiment, it is assumed that there are N U The edge UAVs participate in the four-dimensional cooperative stealth penetration trajectory planning, and N U The edge UAVs can communicate with each other. The starting point of the edge UAV is denoted as P start，i , which is represented as P start,i =(x start,i ,y start,i ,z start,i ); the task point of the edge UAV is denoted as P end，i , which is represented as P end，i =(x end，i ,y end，i ,z end，i ); all positions are three-dimensional space coordinate points, wherein i=1, 2…N U .

[0090] At the same time, according to the above content, there are N Thr environmental radars in the environment to be planned, and the three-dimensional space coordinate positions are denoted as P Thr,j =(x Thr,j ,y Thr,j ,z Thr,j ), wherein j=1, 2…N Thr . The trajectory node can also be referred to as a trajectory point.

[0091] The edge UAV performance constraint model is established based on the flight performance parameters of the edge UAV, including:

[0092] Maximum climbing angle and maximum diving angle constraint: denote the maximum climbing angle of the edge UAV as γ min_climb , the maximum diving angle as γ min_dive , the current planning diving angle of the edge UAV in the planning process as γ dive,i , and the climbing angle as γ climb,i . Then the edge UAV from the trajectory point P n,i :(x n,i ,y n,i ,z n,i ) to the trajectory point P n+1,i:(x n+1,i ,y n+1,i ,z n+1,i ) need to satisfy the angle relationship as follows:

[0093]

[0094] Maximum horizontal turning angle constraint: between two adjacent trajectory segments, the edge UAV has limited turning ability in the horizontal direction, denoted as the maximum horizontal turning angle of the edge UAV as Δψ max , and the current planned horizontal turning angle of the edge UAV between adjacent trajectory segments as Δψ i , then the three trajectory points P n,i :(x n,i ,y n,i ,z n,i ), P n+1,i :(x n+1,i ,y n+1,i ,z n+1,i ), and P n+2,i :(x n+2,i ,y n+2,i ,z n+2,i ) between two adjacent trajectory segments of the edge UAV need to satisfy the angle relationship as follows:

[0095]

[0096] Flight height constraint: due to the performance limitation of the edge UAV, a certain ground clearance needs to be ensured, denoted as the minimum ground clearance of the edge UAV as Δz min , the maximum flight height of the edge UAV as z max , and the terrain height during the flight of the edge UAV as z terraini , then the planned trajectory points need to satisfy the constraint as follows:

[0097]

[0098] Maximum flight trajectory distance constraint: denoted as the maximum flight trajectory distance of the edge UAV as L max_path_length,i , then the stealth penetration trajectory planning result of the edge UAV needs to satisfy the constraint as follows:

[0099]

[0100] wherein ||x-y|| is the modulus calculation, i represents the ith UAV, P k+1,i is the node of UAV i at P k+1 , and P k,i is the node of UAV i at P k .

[0101] Maximum flight speed: the maximum flight speed is used to calculate the recommended flight speed of different edge UAVs after the cooperative stealth penetration planning. When the multi-edge UAV cooperatively plans the trajectory, the edge UAVs receive the planned environment information obtained by detection, including:

[0102] Terrain data: the terrain data adopts the terrain information drawn by the actual elevation data or the simulated terrain established by the formula, wherein the formula is as follows:

[0103]

[0104] wherein, H i is a parameter for controlling the height of the mountain peak, (A i ,B i ) represents the coordinate value of the center point of the i-th mountain peak, K i represents a parameter for controlling the steepness of the mountain slope of the i-th mountain peak, N peak represents the number of mountain peaks, and h(x, y) is the terrain height value of a certain point, and the three-dimensional terrain is simulated according to the above-mentioned parameters.

[0105] Threat data: the threat data is the radar threat information existing in the environment, and the basic radar equation is as follows:

[0106]

[0107] wherein, is the maximum detection range of the environment radar threat, P t is the power of the environment radar threat, G t is the emission antenna gain of the environment radar threat, λ is the wavelength of the environment radar threat, k is the Boltzmann constant, T0 is the receiver noise temperature represented by the absolute temperature, Δf is the bandwidth, F n is the noise factor, (SNR) min is the minimum detectable signal-to-noise ratio, L is the total loss coefficient, is the RCS value of the edge UAV.

[0108] Suppose that during the combat process, other conditions remain unchanged, then the environment radar threat Thr l is related to the maximum detection range of the edge UAV participating in the current planning and the RCS of the edge UAV at different times, and the relationship is as follows:

[0109]

[0110] wherein, is the environment radar threat Thr l of the previous time to the maximum detection range of the edge UAV, is the environment radar threat Thr l of the current time to the maximum detection range of the edge UAV, RCS value of the edge UAV at the previous moment, RCS value of the edge UAV at the current moment.

[0111] S18: Determine the basic parameters of each UAV according to the flight performance parameters and the environmental initial information data. The basic parameters at least include the planning step, the planning times, and the collision distance between each UAV. The SAS algorithm vertical division number n V and the horizontal division number n H are also included. The edge UAVs select or designate the edge UAV with sufficient computing capacity for centralized trajectory planning through mutual communication. It should be noted that the basic parameters described above do not limit the basic parameters in the present application, and more or less data than the basic parameters described above can be included, and the data usage can be determined according to the specific implementation scenario to determine the implementation mode.

[0112] S19: Construct a cost function according to the basic parameters to determine the next trajectory node of each UAV.

[0113] In the designated edge UAV task calculator, a virtual data structure is established, and the planning related algorithm parameters in the four-dimensional cooperative stealth penetration trajectory planning method of UAVs are set, including the planned trajectory length cost coefficient, the height cost coefficient, the threat cost coefficient based on the dynamic RCS of the edge UAV, the estimated trajectory length cost coefficient, and the time coordination cost coefficient, etc., to construct a cost function.

[0114] The specific process of constructing the cost function is as follows:

[0115] The planned trajectory nodes of the edge UAV i at the current state are P 1,i , P 2,i …P n,i , wherein, P 1,i =P start,i , P n+1,m,i is the m trajectory nodes to be selected generated after P n,i according to the vertical division number; and P n+1,k,i is the selected trajectory node, wherein k=1, 2…m.

[0116] The formula of the cost function is as follows:

[0117] f(P n+1,k,i ) = C1g planned_length (P n+1,k,i ) + C2g height (P n+1,k,i ) + C3g RCS_threat (P n+1,k,i ) + C4g hestimated_length (P n+1,k,i ) + C5Rcooperate (P n+1,k,i )

[0118] wherein, g planned_length (P n+1,k,i ) is the edge UAV planned trajectory length cost, g height (P n+1,k,i ) is the edge UAV flight height cost, g RCS_threat (P n+1,k,i ) is the environment threat cost calculated based on the dynamic RCS of the edge UAV, g hestimated_length (P n+1，k，i ) is the edge UAV estimated trajectory cost, R cooperate (P n+1,k,i ) is the edge UAV time coordination cost; C1, C2, C3, C4, C5 are weight coefficients of the above different costs, the weight coefficients need to be balanced in selection, and in general cases, C1+C4=1 and C1

[0119] In the present embodiment, the following settings can be made: the planned trajectory length cost coefficient is recorded as 0.3, the edge UAV flight height cost coefficient is recorded as 0.001, the environment threat cost coefficient is recorded as 0.5, the estimated trajectory cost coefficient is recorded as 0.7, and the time coordination cost coefficient is recorded as 0.05.

[0120] In the specific implementation process, multiple backup parameters can be stored according to the needs, and when some items in the cost function are not needed, the cost coefficient can be zero. The mathematical model of each cost in the present embodiment is as follows:

[0121] The planned trajectory length cost model is calculated by the sum of the distance between the current selected point of the edge UAV and the current planning node and the distance between the planned trajectory points, as shown in the following formula:

[0122]

[0123] wherein, P l is the current planned trajectory point of the UAV i, and P l+1 is the selected node of the UAV i.

[0124] The edge UAV flight height cost model is as follows:

[0125]

[0126] Environmental threat cost model: assuming that the environmental radar threat antenna cannot scan 360°, usually the scanning range is a sector, then the angle range covered by the radar is i.e. not to form a full range of detection, calculate the environmental radar threat Thr l The edge of the individual threat value of the UAV The formula is as follows:

[0127]

[0128] Wherein, C1, C2 is the radar parameter, usually take 1, is the Euclidean distance from the edge of the UAV to the environmental radar threat, is the height difference from the edge of the UAV to the environmental radar threat.

[0129] When the edge of the UAV trajectory node is expanded, the threat cost between the current node and the selected node trajectory needs to be integrated. For the sake of simplicity, it is adjusted to calculate the average value of the threat cost of several points on the corresponding leg. When the edge of the UAV is expanded in a single step, the threat cost it receives in ΔL step is approximately calculated, and ΔL step is evenly divided into q sections, i.e. q+1 end point values are divided on the corresponding leg, respectively X n,i,1 , X n,i,2 , …, X n,i,q+1 . Then the environmental radar joint threat cost of the edge of the UAV at the end point X n,i,j is calculated according to the following formula:

[0130]

[0131] The average value of the environmental radar joint threat cost of q+1 end point values is used as the environmental threat cost of the edge of the UAV trajectory expansion node n to the selected node n+1 as follows:

[0132]

[0133] Estimation of trajectory cost model: the estimation of trajectory cost is calculated by the Euclidean distance from the current selected node to the task point, and the calculation formula is as follows:

[0134] g hestimated_length (P n+1,k,i )=||P end,i -P n+1,k,i ||

[0135] Time coordination cost model: the time coordination cost is the difference function of the flight path between different edge UAVs. When N U edge UAVs plan at the same time, the edge of the UAV in the selected flight path node Pn+1,k,i The time coordination cost is as follows:

[0136]

[0137] L estimated_total_length is the total length of the estimated trajectory, and the total length of the estimated trajectory is as follows:

[0138]

[0139] L estimated_total_length,i is the length of the route that the UAV i has planned, the distance between the current point and the current unknown point to be selected, and the distance between the target point and the point to be selected, which constitute the estimated trajectory length for subsequent planning of the UAV; L estimated_total_length,j represents the estimated trajectory length for subsequent planning of other UAVs except UAV i.

[0140] On the basis of the above embodiment, as a more optimal embodiment, if the edge UAV is not within the planning step range of the corresponding task point, it further includes:

[0141] S20: Obtain the current trajectory node of the edge UAV.

[0142] S21: Determine the set of next trajectory nodes of the edge UAV according to the current trajectory node and the cost function.

[0143] S22: Delete the trajectory nodes in the set that do not meet the flight performance parameters.

[0144] S23: Put the trajectory nodes in the Open table into the Closed table.

[0145] S24: Determine whether the set is an empty set.

[0146] If yes, go to step S25: Determine whether the Open table is empty.

[0147] If the Open table is not empty, go to step S12: Determine whether the edge UAV is within the planning step range of the corresponding task point.

[0148] If the Open table is empty, go to step S26: Determine that this planning fails.

[0149] If no, go to step S27: Determine the next trajectory node of the edge UAV.

[0150] Determine whether the Open table of the current trajectory extended edge UAV is empty, if not, take the node to be selected with the minimum comprehensive cost function value from the Open table of the current trajectory extended edge UAV as the current expansion node P expanding,iAnd determine whether the UAV at the current trajectory extension edge is at its corresponding task point P. end,i Within the planned step length range.

[0151] If the current trajectory extension edge UAV is not within the planned step size of its corresponding task point, then based on the current extension node P of the current trajectory extension edge UAV... expanding,i Expand the list of candidate nodes and generate a set S of candidate nodes. i ={P choice,k,i}, where k = 1, 2, ..., n V ×n H ; Traverse the set of candidate nodes, using the flight performance of the edge drone as a constraint, delete candidate nodes that do not meet the flight conditions of the edge drone, and retain candidate nodes that do meet the flight conditions of the edge drone; Traverse the planned nodes of other edge drones, delete candidate nodes that do not meet the spatial anti-collision constraints of other edge drones; Determine the pitch and azimuth angles of the current trajectory extension edge drone at different retained candidate nodes, calculate the RCS value of the edge drone at the corresponding angles, and set the current extension node P expanding,i Remove it from the Open table of the current trajectory extended edge drone and add it to the Closed table of the current trajectory extended edge drone.

[0152] Based on typical RCS values, the RCS of large edge drones ranges from 10 to 300. A simplified radar cross-section model is established by combining the relative trajectory deflection angle and relative trajectory pitch angle of the edge drone. The model is shown below:

[0153]

[0154] in, The trajectory deflection angle of edge drones relative to environmental radar threats. The pitch angle of the trajectory of an edge drone relative to environmental radar threats. This represents the RCS value of the edge drone.

[0155] When the pitch angle and yaw angle of the edge drone's trajectory are both π / 2 rad, the RCS of the edge drone reaches its maximum value, which closely reflects the actual RCS variation pattern of the edge drone.

[0156] The pattern is as follows:

[0157] Based on other planned nodes of edge drones, if the current candidate node P of the currently planned edge drone... n+1,k,i Other edge drones have planned node P x,j (where x = 1, 2, ..., n) planned,x j = 1, 2, ..., N Uand j≠i) is within the safety distance range, according to ||P n+1,k,i -P x,j ||<L safe deems the current edge UAV's current to-be-selected node P n+1,k,i does not satisfy the spatial anti-collision constraint, the to-be-selected node is deleted.

[0158] If the current trajectory expansion edge UAV is within the planning step range of its corresponding task point, the corresponding task point P end,i is set as the last trajectory point of the current expansion edge UAV. The current trajectory expansion edge UAV's planning mark is set to false, and the trajectory planning of the current trajectory expansion edge UAV is successful, and its trajectory planning result no longer participates in other calculations except the anti-collision constraint.

[0159] If the Open table of the current trajectory expansion edge UAV is empty, the current cooperative stealth penetration trajectory planning is deemed to fail, the planning failure number is incremented by 1, and the calculation resources related to the current calculation are released.

[0160] S28: Determine whether the number of planned nodes is equal to the number of cooperative planning nodes.

[0161] When the number of planned nodes of the edge UAV and the number of cooperative planning nodes of the UAV are equal, step S12 is entered: determine whether the edge UAV is within the planning step range of the corresponding task point, and the specific steps in the steps below determine whether there is a UAV with a planning mark true.

[0162] When the number of planned nodes of the edge UAV and the number of cooperative planning nodes of the UAV are not equal, step S29 is entered: the number of cooperative planning points is incremented by 1. And return to step S12: determine whether the edge UAV is within the planning step range of the corresponding task point.

[0163] If there is a UAV whose trajectory is not planned, it also includes:

[0164] S30: Obtain the number of times of trajectory planning failure. And S31: determine whether the number of times of trajectory planning failure is greater than a preset number.

[0165] If yes, step S32 is entered: determine the trajectory planning failure of each UAV.

[0166] If no, return to step S11.

[0167] Among them, the determination of the trajectory planning of each UAV specifically includes the following steps:

[0168] Determine the successfully planned trajectory node from the Open table of each UAV;

[0169] The average flight speed of each UAV is determined by the successfully planned trajectory nodes, and the trajectory planning is performed.

[0170] It is determined whether the filtered candidate node set of the current trajectory expansion edge UAV is empty, if empty, it is determined whether the Open table is empty in step S25; if not empty, the filtered candidate node set S i The RCS values of different scalable candidate nodes are calculated, the maximum detection range of different environmental radar threats to the current trajectory expansion edge UAV is calculated, and the threat value of the environmental radar threat network suffered by the scalable candidate nodes of the current trajectory expansion edge UAV is calculated; the edge UAV cooperative time cost value is calculated, and the candidate node set S i The cost function values f of all scalable candidate nodes are put into the Open table of the current trajectory expansion edge UAV, and the number of planned trajectory nodes n planned,i is added by 1.

[0171] It is determined whether the number of planned trajectory nodes n planned,i of the current trajectory expansion edge UAV is equal to the number of cooperative trajectory planning nodes n cooperation , if not equal, it is determined whether the Open table is empty in step S25; if equal, the number of cooperative trajectory planning nodes n cooperation is added by 1.

[0172] It is determined whether the number of cooperative stealth penetration trajectory planning failures exceeds a set value, if not, the planning flags of all edge UAVs are set to true, the above-mentioned algorithm basis parameters and planning related parameters are reloaded, and the cooperative stealth penetration trajectory planning is performed again; if the number of calculation failures exceeds the set value, the planning flags of all edge UAVs are set to false, it is determined that the UAVs cooperative stealth penetration trajectory planning fails, the task demand is changed, the emergency brake is started, and the process ends.

[0173] If the planning is not successful, the current planning is set as failed, the planning failure number is increased by 1, and the computing resources related to the current planning are released. If the planning is successful, the successful planning trajectory is obtained by backtracking from the trajectory node with the minimum cost in the Open table of each edge UAV, the average flight speed of each edge UAV is calculated, the planning flag of each edge UAV is set as false, the edge UAVs fly according to the planning trajectory, the edge UAVs receive the updated combat environment information (mainly the threat information) from other edge UAVs, and when the task state or target state of an edge UAV changes, the planning flag of the edge UAV is set as true, the current position of the edge UAV is set as a new starting point P' and the changed target position is set as a new task point P', and then the online planning of the trajectory of the edge UAVs is performed. start,i end,i

[0174] If all the edge UAVs reach the corresponding task points, the planning of the trajectory of the edge UAVs is successful, the computing resources related to the edge UAVs are released, and the planning of the trajectory of the edge UAVs is ended.

[0175] The average flight speed v c,i is calculated according to the following formula:

[0176]

[0177] wherein, L total_length，i is the length of the planning trajectory of the edge UAV, v max is the maximum flight speed of the edge UAV, and L total_length，j is the length of the planning trajectory of each edge UAV except the edge UAV i.

[0178] ​​The trajectory planning method of the unmanned aerial vehicle provided in the application solves the multi-edge unmanned aerial vehicle space and time collaborative trajectory solving problem in the UAVs collaborative trajectory planning process, so that the multi-edge unmanned aerial vehicles participating in planning can collaboratively arrive at the task point in time, and a certain spatial safety range can be ensured in the flight process, thereby improving the safety of multi-edge unmanned aerial vehicle collaborative combat; the multi-edge unmanned aerial vehicle stealth penetration trajectory planning problem in a complex space is solved, so that the edge unmanned aerial vehicle with a conventional RCS characteristic can combine its flight attitude to collaboratively penetrate through the radar threat network in the combat environment and secretly arrive at the target position to perform the corresponding combat task; for the online route planning demand caused by task deployment or target state time variation, the part of the edge unmanned aerial vehicles that have adjustment requirements in the multi-edge unmanned aerial vehicles perform online trajectory calculation, thereby reducing the online trajectory planning calculation amount, ensuring the continuous stealth characteristic of the multi-edge unmanned aerial vehicles in the flight process, and improving the survival probability of the UAVs in the complex battlefield environment.

[0179] In the above embodiment, the trajectory planning method of the unmanned aerial vehicle is described in detail, and the application also provides a corresponding embodiment of the trajectory planning device of the unmanned aerial vehicle. It should be noted that the embodiments of the device part are described from two angles, one is based on the functional module angle, and the other is based on the hardware angle.

[0180] Figure 3 The structure diagram of the trajectory planning device of the unmanned aerial vehicle provided in the embodiment of the application is shown in Figure 3 The trajectory planning device of the unmanned aerial vehicle comprises:

[0181] The acquisition module 13 is configured to acquire the number of planned nodes of each unmanned aerial vehicle and the number of collaborative planning nodes of the unmanned aerial vehicle.

[0182] The first determination module 14 is configured to determine the edge unmanned aerial vehicle according to the number of planned nodes and the number of collaborative planning nodes.

[0183] The first judgment module 15 is configured to judge whether the edge unmanned aerial vehicle is within the planning step range of the corresponding task point, and if so, trigger the as module 16.

[0184] The as module 16 is configured to take the task point as the last trajectory point of the edge unmanned aerial vehicle.

[0185] The second judgment module 17 is configured to judge whether there is a planable unmanned aerial vehicle among the remaining unmanned aerial vehicles, and if so, return to the first determination module 14; and if not, trigger the third judgment module 18.

[0186] The third judgment module 18 is configured to judge whether all the unmanned aerial vehicles are planned, and if the trajectories of all the unmanned aerial vehicles are planned, trigger the second determination module 19.

[0187] The second determining module 19 is configured to determine the trajectory planning of each unmanned aerial vehicle.

[0188] The trajectory planning device of the unmanned aerial vehicle provided in the embodiment comprises an obtaining module, a first determining module, a first judging module, an assigning module, a second judging module, a third judging module and a second determining module, and is used for executing the method for trajectory planning of the unmanned aerial vehicle. According to the number of planned nodes of the unmanned aerial vehicle and the number of cooperatively planned nodes, the edge unmanned aerial vehicle is determined. When the edge unmanned aerial vehicle is in the planning range of the corresponding task point, the task point is determined as the last trajectory point of the edge unmanned aerial vehicle, and whether there is a planable unmanned aerial vehicle among the remaining unmanned aerial vehicles is determined. If there is, the edge unmanned aerial vehicle among the remaining unmanned aerial vehicles is determined. When there is not, the trajectory planning of each unmanned aerial vehicle is determined when the planning of each unmanned aerial vehicle is completed. It can be seen that the method solves the multi-edge unmanned aerial vehicle space, ensures the safety of the cooperative combat of the unmanned aerial vehicles by making each unmanned aerial vehicle reach the designated place at the same time, and can meet the condition of stealth, avoids being found by the radar of the opponent, and thus better performs the combat task.

[0189] Figure 4 The structure diagram of the trajectory planning device of the unmanned aerial vehicle provided in the embodiment of the application is shown in FIG. 1. Figure 4 The trajectory planning device of the unmanned aerial vehicle comprises a memory 20 configured to store a computer program.

[0190] The processor 21 is configured to execute the computer program to realize the steps of the trajectory planning method of the unmanned aerial vehicle mentioned in the above embodiment.

[0191] The trajectory planning device of the unmanned aerial vehicle provided in the embodiment can comprise but is not limited to a smart phone, a tablet computer, a notebook computer or a desktop computer and the like.

[0192] The processor 21 can include one or more processing cores, such as a 4-core processor, an 8-core processor, etc. The processor 21 can be implemented in at least one of a hardware form of a digital signal processor (DSP), a field-programmable gate array (FPGA), a programmable logic array (PLA), etc. The processor 21 can also include a main processor and a coprocessor. The main processor is a processor for processing data in a wake-up state, also referred to as a central processing unit (CPU). The coprocessor is a low-power processor for processing data in a standby state. In some embodiments, the processor 21 can be integrated with a graphics processing unit (GPU) for rendering and drawing content required to be displayed by a display screen. In some embodiments, the processor 21 can further include an artificial intelligence (AI) processor for processing computing operations related to machine learning.

[0193] The memory 20 can include one or more computer-readable storage media, which can be non-transitory. The memory 20 can further include a high-speed random access memory, and a nonvolatile memory such as one or more disk storage devices, flash storage devices. In this embodiment, the memory 20 is at least used to store the following computer program 201, wherein the computer program is loaded and executed by the processor 21, and can implement the related steps of the trajectory planning method of the unmanned aerial vehicle disclosed in any of the preceding embodiments. In addition, the resources stored by the memory 20 can further include an operating system 202 and data 203, etc., and the storage mode can be temporary storage or permanent storage. The operating system 202 can include Windows, Unix, Linux, etc. The data 203 can include, but is not limited to, data of the trajectory planning method of the unmanned aerial vehicle, etc.

[0194] In some embodiments, the trajectory planning device of the unmanned aerial vehicle can further include a display screen 22, an input / output interface 23, a communication interface 24, a power supply 25, and a communication bus 26.

[0195] Those skilled in the art can understand that the structure shown in the above embodiments does not constitute a limitation on the trajectory planning device of the unmanned aerial vehicle, and can include more or fewer components than those shown in the drawings. Figure 4 The structure shown in the above embodiments does not constitute a limitation on the trajectory planning device of the unmanned aerial vehicle, and can include more or fewer components than those shown in the drawings.

[0196] The trajectory planning device of the unmanned aerial vehicle provided in the embodiment comprises a memory and a processor, the memory is used for storing a program of a trajectory planning method of the unmanned aerial vehicle, and the processor is used for executing the stored program, which determines an edge unmanned aerial vehicle according to a planned node number of the unmanned aerial vehicle and a cooperative planning node number, takes a task point as a last trajectory point of the edge unmanned aerial vehicle when the edge unmanned aerial vehicle is in a planning range of the corresponding task point, and judges whether there is a planable unmanned aerial vehicle among the remaining unmanned aerial vehicles, if there is, determines an edge unmanned aerial vehicle among the remaining unmanned aerial vehicles, and when there is not, determines the trajectory planning of each unmanned aerial vehicle when each unmanned aerial vehicle is planned. It can be seen that the method solves the multi-edge unmanned aerial vehicle space, ensures the safety of the cooperative combat of the unmanned aerial vehicles by making each unmanned aerial vehicle reach the specified location at the same time, can meet the stealth condition, avoids being found by the radar of the opponent, and thus better performs the combat task.

[0197] Finally, the application also provides an embodiment corresponding to a computer readable storage medium. The computer readable storage medium stores a computer program, and the computer program is executed by a processor to implement the steps described in the above method embodiment.

[0198] It can be understood that if the method in the above embodiment is implemented in the form of a software function unit and sold or used as an independent product, it can be stored in a computer readable storage medium. Based on such understanding, the technical solutions of the application essentially or say the part that contributes to the prior art or the whole or part of the technical solutions can be embodied in the form of a software product, and the computer software product is stored in a storage medium and executes all or part of the steps of the methods described in the embodiments of the application. The foregoing storage medium includes a U disk, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, and various media that can store program codes.

[0199] The computer readable storage medium provided in the embodiment stores and executes the program for executing the trajectory planning method of the unmanned aerial vehicle, determines an edge unmanned aerial vehicle according to a planned node number of the unmanned aerial vehicle and a cooperative planning node number, takes a task point as a last trajectory point of the edge unmanned aerial vehicle when the edge unmanned aerial vehicle is in a planning range of the corresponding task point, judges whether there is a planable unmanned aerial vehicle among the remaining unmanned aerial vehicles, if there is, determines an edge unmanned aerial vehicle among the remaining unmanned aerial vehicles, and when there is not, determines the trajectory planning of each unmanned aerial vehicle when each unmanned aerial vehicle is planned. It can be seen that the method solves the multi-edge unmanned aerial vehicle space, ensures the safety of the cooperative combat of the unmanned aerial vehicles by making each unmanned aerial vehicle reach the specified location at the same time, can meet the stealth condition, avoids being found by the radar of the opponent, and thus better performs the combat task.

[0200] The above describes in detail the trajectory planning method, device, equipment and medium of the unmanned aerial vehicle provided in the application. The embodiments in the specification are described in a progressive manner, and each embodiment mainly explains the difference from other embodiments. The same or similar parts of each embodiment can be understood by referring to each other. For the device disclosed in the embodiments, since it corresponds to the method disclosed in the embodiments, the description is relatively simple, and the related parts can be understood by referring to the method part. It should be pointed out that for ordinary technical personnel in the technical field, without departing from the principle of the application, the application can be improved and modified, and these improvements and modifications also fall within the protection scope of the claims of the application.

[0201] It should also be noted that in the specification, the relationship terms such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between the entities or operations. Moreover, the terms "include", "contain" or any other variants are intended to cover non-exclusive inclusion, so that the process, method, article or equipment including a series of elements not only includes those elements, but also includes other elements not explicitly listed or inherent to such process, method, article or equipment. Without more limitations, the element defined by the statement "including a" does not exclude the existence of other identical elements in the process, method, article or equipment including the element.

Claims

1. A method for trajectory planning of a UAV, the method comprising: The method comprises the following steps: acquiring the number of planned nodes of each UAV and the number of cooperatively planned nodes of the UAVs; determining an edge UAV according to the number of planned nodes and the number of cooperatively planned nodes; judging whether the edge UAV is within the planning step range of the corresponding task point; if yes, taking the task point as the last trajectory node of the edge UAV; judging whether there is a UAV that can be planned among the remaining UAVs; if yes, returning to the step of determining an edge UAV according to the number of planned nodes and the number of cooperatively planned nodes; if no, judging whether the trajectories of all the UAVs are planned; if the trajectories of all the UAVs are planned, determining the trajectory planning of each UAV; the trajectory planning of the UAVs is the trajectory planning of multi-edge UAVs for stealth penetration in a complex space; before the step of acquiring the number of planned nodes of each UAV and the number of cooperatively planned nodes of the UAVs, the method further comprises the following steps: determining the flight performance parameters of each UAV and the initial environmental information data, wherein the initial environmental information data at least includes terrain data and radar threat data, and the flight performance parameters at least include the maximum climbing angle, the maximum diving angle, the maximum horizontal angle, the maximum flight height, the maximum flight trajectory distance and the maximum flight speed of the UAVs; determining the basic parameters of each UAV according to the flight performance parameters and the initial environmental information data, wherein the basic parameters at least include the planning step, the planning times and the collision distance between each UAV; constructing a cost function according to the basic parameters to determine the next trajectory node of each UAV; wherein a simplified model of radar scattering area combining the relative track deflection angle and the relative track pitch angle of the edge UAV is established, and the model is as follows: ； wherein, is the track yaw angle of the edge drone relative to the environmental radar threat, is the track pitch angle of the edge drone relative to the environmental radar threat, is the RCS value of the edge drone. then, calculating the maximum detection range of the environmental radar threat to the current trajectory extended edge UAV according to the RCS value: ； wherein, is the radar threat of the environment at the previous time instant is the maximum detection range of the edge drone, is the radar threat of the environment at the current time instant is the maximum detection range of the edge drone, is the RCS value of the edge drone at the previous time instant is the RCS value of the edge drone at the current time instant calculating the environmental threat cost based on the maximum detection range to construct a cost function including a target cost term; the target cost term includes the edge UAV planned trajectory length cost, the edge UAV height cost, the threat cost based on the dynamic RCS of the edge UAV, the edge UAV estimated trajectory length cost and the edge UAV time cooperation cost. 2.The method of claim 1, wherein, The step of acquiring the number of planned nodes of each UAV and the number of cooperatively planned nodes of the UAVs comprises the following steps: setting an Open table and a Closed table for each UAV; marking the planning mark of each UAV as true; taking the initial position of each UAV as the first planning node and storing it in the Open table; acquiring the number of planned nodes of each UAV and the number of cooperatively planned nodes of the UAVs according to the Open table. 3.The method of claim 2, wherein, The step of determining an edge UAV according to the number of planned nodes and the number of cooperatively planned nodes comprises the following steps: judging whether there is a UAV with the planning mark true; if yes, taking the UAV with the smallest number of planned nodes as the edge UAV; if no, entering the step of judging whether the trajectories of all the UAVs are planned. 4.The method of claim 3, wherein, if the edge UAV is not within the planning step range of the corresponding task point, further comprising: obtaining a current trajectory node of the edge UAV; determining a set of next trajectory nodes of the edge UAV according to the current trajectory node and the cost function; deleting trajectory nodes in the set that do not satisfy the flight performance parameter; putting trajectory nodes in the Open table into the Closed table; determining whether the set is an empty set; if yes, entering the step of determining whether the edge UAV is within the planning step range of the corresponding task point in the case that the Open table is not empty; if no, determining the next trajectory node of the edge UAV; when the number of the planned nodes of the edge UAV and the number of the cooperative planning nodes of the UAV are equal, entering the step of determining whether there is the UAV with the mark true. 5.The method of claim 1, wherein, if there is a UAV whose trajectory is not planned, further comprising: obtaining the number of times of trajectory planning failure and determining whether the number of times of trajectory planning failure is greater than a preset number of times; if yes, determining that the trajectory planning of each UAV fails; if no, returning to the step of determining the edge UAV according to the number of the planned nodes and the number of the cooperative planning nodes. 6.The method of claim 4, wherein, the determination of the trajectory planning of each UAV comprises: determining successfully planned trajectory nodes from the Open table of each UAV; determining the average flight speed and trajectory planning of each UAV through the successfully planned trajectory nodes. 7.A trajectory planning device of a UAV, characterized by, comprising: an obtaining module, configured to obtain the number of planned nodes of each UAV and the number of cooperative planning nodes of the UAV; a first determining module, configured to determine an edge UAV according to the number of the planned nodes and the number of the cooperative planning nodes; a first judging module, configured to determine whether the edge UAV is within the planning step range of the corresponding task point, if yes, triggering a serving module; the serving module, configured to take the task point as the last trajectory node of the edge UAV; a second judging module, configured to determine whether there is a UAV that can be planned among the remaining UAVs, if yes, returning to the first determining module; if no, triggering a third judging module; the third judging module, configured to determine whether the trajectory of each UAV is planned, if the trajectory of each UAV is planned, triggering a second determining module; the second determining module, configured to determine the trajectory planning of each UAV; the trajectory planning of the UAV is the trajectory planning of multi-edge UAV stealth penetration in a complex space; the device is further configured to determine flight performance parameters of each UAV and environmental initial information data, wherein the environmental initial information data at least includes terrain data and radar threat data, and the flight performance parameters at least include the maximum climbing angle, the maximum diving angle, the maximum horizontal angle, the maximum flight height, the maximum flight trajectory distance and the maximum flight speed of the UAV; Determine a basic parameter of each of the UAVs according to the flight performance parameter and the environment initial information data, wherein the basic parameter at least includes a planning step, a planning times and a collision distance between each of the UAVs; Construct a cost function according to the basic parameter to determine a next trajectory node of each of the UAVs; wherein a simplified model of a radar scattering area combining a relative trajectory yaw angle and a relative trajectory pitch angle of an edge UAV is established, and the model is: ； wherein, is the track yaw angle of the edge drone relative to the environmental radar threat, is the track pitch angle of the edge drone relative to the environmental radar threat, is the RCS value of the edge drone. Then, calculate a maximum detection range of an environment radar threat to the current trajectory extended edge UAV according to the RCS value: ； wherein, is the radar threat of the environment at the previous time instant is the maximum detection range of the edge drone, is the radar threat of the environment at the current time instant is the maximum detection range of the edge drone, is the RCS value of the edge drone at the previous time instant is the RCS value of the edge drone at the current time instant Calculate an environment threat cost based on the maximum detection range to construct a cost function including a target cost item; the target cost item includes an edge UAV planned trajectory length cost, an edge UAV height cost, a threat cost based on a dynamic RCS of the edge UAV, an edge UAV estimated trajectory length cost and an edge UAV time coordination cost. 8.A trajectory planning device of a drone, comprising: The computer readable storage medium stores a computer program, and the computer program is executed by the processor to implement the steps of the trajectory planning method of the UAV according to any one of claims 1 to 6. The computer readable storage medium stores a computer program, and the computer program is executed by the processor to implement the steps of the trajectory planning method of the UAV according to any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that, ​

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