A method and system for reconfiguring the formation of multiple aircraft dense formations

Abstract

The embodiment of the present invention discloses a method and system for reconstructing the formation of a multi-aircraft dense formation, including the following steps: converting the received UAV laser radar coordinate system into a geodetic coordinate system to obtain the geodetic rectangular coordinate system data of the UAVs in the group; calculating the UAV relative position vector and the UAV positioning vector based on the UAV data in the geodetic coordinate system, comparing the module value, angle and direction between the two vectors, matching the two vectors, and obtaining the positioning information of each UAV in the current UAV group; obtaining the positioning information of each UAV in the current UAV group, and reallocating the position and route of the UAV according to the initial formation formation and the required formation formation. The aircraft can rearrange the formation into a new formation during the actual formation flight, and the position control of the aircraft is more accurate, which solves the problem of low adaptability of the formation to the environment and tasks in the reconstruction in the prior art.

Description

Technical Field

[0001] The invention belongs to the technical field of unmanned aerial vehicle flight, and relates to a method and system for reconstructing the formation of a dense formation of multiple aircraft. Background Art

[0002] The formation reconstruction of multiple drones requires determining the interval distance, position, and transition process of the initial formation structure to the final structure of the unmanned equipment. The formation reconstruction of unmanned equipment includes formation switching and the reconstruction of the new formation when formation members are added or missing. In addition, during the reconstruction process, how to avoid collisions, change to a new formation at the lowest cost, and be able to adapt to changes in tasks and environment are all issues that need to be considered in drone formation. Summary of the invention

[0003] The purpose of the present invention is to solve the problems in the prior art and to provide a method and system for reconfiguring the formation of a multi-aircraft dense formation.

[0004] In order to achieve the above object, the present invention adopts the following technical solutions:

[0005] A method for reconstructing a formation of multiple aircraft in a dense formation comprises the following steps:

[0006] S1: Convert the received UAV laser radar coordinate system into the geodetic coordinate system to obtain the geodetic rectangular coordinate system data of the UAVs in the fleet;

[0007] S2: Based on the data of the drone in the geodetic coordinate system, calculate the drone relative position vector and the drone positioning vector, compare the module value, angle and direction between the two vectors, match the two vectors, and obtain the positioning information of each drone in the current drone fleet; (obtain the drone number corresponding to the drone positioning vector)

[0008] S3: Obtain the positioning information of each drone within the current drone fleet, and reallocate the positions and routes of the drones according to the initial formation and the required formation.

[0009] The further improvement of this method is:

[0010] The S1 comprises the following steps:

[0011] The Euclidean distance of each UAV is calculated and sorted using the quick sort method until the minimum Euclidean distance is obtained. The coordinates of the minimum distance between the own UAV and the UAV scanned by the lidar are determined based on the minimum Euclidean distance, and the coordinates are converted into geodetic rectangular coordinate system data.

[0012] The S1 includes the following steps:

[0013] Determine the Euclidean distance between a drone and each other drone:

[0014] The detection results of the drone detecting the surrounding drones through the laser radar are recorded as C k =(x k ,y k ,z k ), where (x k ,y k ,z k ) is the position coordinate of the point, and the Euclidean distance from the detected drone to the fixed-wing drone carrying the laser radar is:

[0015]

[0016] Where k is the number of a point in the three-dimensional point cloud data of the UAV detected by the laser radar, j is the number of the UAV carrying the laser radar, and Xrj is the position of the UAV carrying the laser radar with number j in the x direction;

[0017] Convert the received UAV lidar coordinate system into the earth coordinate system:

[0018] (1) Conversion from geodetic coordinate system O to geodetic rectangular coordinate system G:

[0019]

[0020] in,

[0021] e represents the first eccentricity of the ellipsoid; a represents the major radius of the ellipsoid;

[0022] (2) Conversion from the geodetic rectangular coordinate system G to the geographic coordinate system N

[0023]

[0024] Among them, (x0, y0, z0) are the coordinates of the origin of the N system coordinate system in the G system, and L0 and B0 are the longitude and latitude of the origin of the N system coordinate system;

[0025] (3) Conversion from geographic coordinate system N to aircraft coordinate system B around X n The transformation matrix of the axis rotation φ:

[0026]

[0027] After rotation, Y n The transformation matrix for the rotation around γ:

[0028]

[0029] After rotation Z n The transformation matrix of the axis rotation θ:

[0030]

[0031] Then the transformation from geographic coordinate system N to aircraft coordinate system B is:

[0032]

[0033] (4) Conversion from the carrier coordinate system B to the base coordinate system T:

[0034] First, consider the installation error between the carrier coordinate system and the base coordinate system.

[0035] Rotate Δφ around the Xb axis I The transformation matrix is:

[0036]

[0037] Rotate Δγ around the Yb axis after rotation I The transformation matrix is:

[0038]

[0039] The transformation matrix of the rotation Δ around the rotated Zb axis:

[0040]

[0041] Transformation from the carrier coordinate system B to the base coordinate system T:

[0042]

[0043] After the above steps, the conversion of the laser radar positioning data coordinate system to the earth coordinate system is realized.

[0044] The S2 comprises the following steps:

[0045] Based on the position information of the UAV in the geodetic rectangular coordinate system, assuming that the deviation between the actual position of the UAV and the preset position is R, the actual position of the UAV is within a circle with the preset position as the center and the position deviation R as the radius. Based on this, the relative position vector of the UAV in the geodetic coordinate system is calculated. At the same time, the laser radar measures the relative position between the UAVs to obtain the laser radar positioning vector.

[0046] The S2 comprises the following steps:

[0047] Assume that the coordinates of the planned position of the drone route are (X2, Y2, Z2); the coordinates of the position located by the laser radar are (X12, Y12, Z12), and the coordinates of the drone's position are (X1, Y1, Z1).

[0048] Then the route positioning vector is (X2-X1, Y2-Y1, Z2-Z1), the lidar positioning vector is (X12, Y12, Z12), and the preset algorithm parameters δ1 are 1.5 and δ2 are 0.4;

[0049] (1) The difference between the two vector moduli is:

[0050]

[0051] Vector matching needs to satisfy D<δ1;

[0052] (2) The difference between the two vector angles is:

[0053] Projection on the XY plane:

[0054]

[0055] Projection on the XZ plane:

[0056]

[0057] Projection on the YZ plane:

[0058]

[0059] When α+β+γ<δ2;

[0060] (3) Symbols with the same value

[0061] Vector matching must meet the following requirements:

[0062] (X12, Y12, Z12) and (X2-X1, Y2-Y1, Z2-Z1) have the same sign, that is, {X12*(X2-X1)>0&Y12*(Y2-Y1)>0&Z12*(Z2-Z1)>0} are all true, then the two vectors are considered to match and the drone identification is completed.

[0063] The S3 comprises the following steps:

[0064] Establish the weight matrix D∈D n*n , where n is the number of drones in the formation, D ij Indicates that the original formation position F i Fly to the new formation P i the cost required;

[0065] Then find n D in different rows and columns so that these n D ij The sum of is the smallest, that is:

[0066] min∑D ij (16)

[0067] Among them, n Dij The position of each drone in the new formation is specified, and D is changed according to the requirements of different tasks. ij The definition of can obtain the location allocation results based on different optimization conditions.

[0068] The S3 comprises the following steps:

[0069] Use the ant colony search algorithm to allocate operations to specific locations:

[0070] Assign each value D in the weight matrix ij As a single node v ij , we get an undirected complete graph, where any two points in the graph can be connected, and the drone numbers and the numbers of the positions in the new formation are used as the two-dimensional coordinates of the nodes, indicating that the i-th position is occupied by the j-th drone, and each node corresponds one-to-one to the elements in the weight matrix;

[0071] The ant colony algorithm is further used to allocate the positions of UAVs, and the change vector of the flight trajectory of the UAVs in three-dimensional space when reconstructed relative to the flight trajectory when there is no formation reconstruction is obtained, and the matched relative route is further obtained.

[0072] An aircraft multi-aircraft dense formation formation reconstruction system, including a laser radar data calibration preprocessing module, a UAV positioning and identification module, and a UAV formation formation reconstruction module;

[0073] The laser radar data calibration preprocessing module is used to convert the received UAV laser radar coordinate system into the geodetic coordinate system to obtain the geodetic rectangular coordinate system data of the UAVs in the fleet;

[0074] The UAV positioning and identification module calculates the relative position vector and positioning vector of the UAV based on the data of the UAV in the geodetic coordinate system, compares the module value, angle and direction between the two vectors, matches the two vectors, and obtains the positioning information of each UAV in the current UAV fleet; (obtains the UAV number corresponding to the UAV positioning vector)

[0075] The UAV formation reconstruction module is used to obtain the positioning information of each UAV within the current UAV fleet, and reallocate the position and route of the UAV according to the initial formation and the required formation.

[0076] A terminal device comprises a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the method according to any one of claims 1 to 7 when executing the computer program.

[0077] A computer-readable storage medium stores a computer program, wherein the computer program, when executed by a processor, implements the steps of the method according to any one of claims 1 to 7.

[0078] Compared with the prior art, the present invention has the following beneficial effects:

[0079] The present invention discloses a method and system for reconfiguring the formation of a multi-aircraft dense formation, which can realize the rearrangement of the formation into a new formation during the actual formation flight of the aircraft. It proposes a method of using a laser radar ranging method to determine the real-time position of each aircraft, thereby achieving more precise position control of the aircraft and stronger adaptability to the environment and tasks. The present invention can achieve formation stability before and after the reconstruction of the multi-aircraft formation, and is suitable for the reconstruction of various formations. BRIEF DESCRIPTION OF THE DRAWINGS

[0080] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for use in the embodiments are briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present invention and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without creative work.

[0081] Figure 1 This is an example diagram of co-location;

[0082] Figure 2 Calculated for the first round;

[0083] Figure 3 Calculated for the second round;

[0084] Figure 4 It is an iterative calculation process ((a) is the third round of calculation; (b) is the fourth round of calculation; (c) is the fifth round of calculation; (d) is the sixth round of calculation);

[0085] Figure 5 Two examples of formations are shown ((a) is the first type of UAV formation; (b) is the second type of UAV formation);

[0086] Figure 6 It is the relative trajectory in formation reconstruction. DETAILED DESCRIPTION

[0087] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Generally, the components of the embodiments of the present invention described and shown in the drawings here can be arranged and designed in various different configurations.

[0088] Therefore, the following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the invention claimed for protection, but merely represents selected embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0089] It should be noted that similar reference numerals and letters denote similar items in the following drawings, and therefore, once an item is defined in one drawing, further definition and explanation thereof is not required in subsequent drawings.

[0090] In the description of the embodiments of the present invention, it should be noted that if the terms "upper", "lower", "horizontal", "inner", etc. indicate an orientation or positional relationship based on the orientation or positional relationship shown in the drawings, or the orientation or positional relationship in which the product of the invention is usually placed when in use, it is only for the convenience of describing the present invention and simplifying the description, and does not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as a limitation on the present invention. In addition, the terms "first", "second", etc. are only used to distinguish the description, and cannot be understood as indicating or implying relative importance.

[0091] In addition, if the term "horizontal" appears, it does not mean that the component must be absolutely horizontal, but can be slightly tilted. For example, "horizontal" only means that its direction is more horizontal than "vertical", which does not mean that the structure must be completely horizontal, but can be slightly tilted.

[0092] In the description of the embodiments of the present invention, it is also necessary to explain that, unless otherwise clearly specified and limited, the terms "set", "install", "connect", and "connect" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal connection of two components. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0093] The present invention is further described in detail below in conjunction with the accompanying drawings:

[0094] The embodiment of the present invention discloses a method for reconfiguring a formation of multiple aircraft in a dense formation, comprising the following steps:

[0095] Step 1: LiDAR data calibration preprocessing;

[0096] First: Each drone in the drone formation detects other drones in the drone formation except itself through laser radar, and determines the Euclidean distance between other drones and each drone. The specific performance is:

[0097] The drone uses its own laser radar to detect the surrounding drones. The result of the laser radar detection is 3D point cloud data. Each detected drone will generate multiple point cloud data, denoted as C. k =(x k ,y k ,z k ), where (x k ,y k ,z k ) is the position coordinate of the point. The laser radar point cloud data processor calculates the Euclidean distance from the drone to each point based on the point cloud data. The Euclidean distance from the detected drone to the fixed-wing drone carrying the laser radar is:

[0098]

[0099] Among them, k is the number of a point in the three-dimensional point cloud data of the detected drone, j is the number of the drone carrying the laser radar, and Xrj is the position of the drone carrying the laser radar with number j in the x direction.

[0100] Secondly: Based on the Euclidean distance calculated by the UAV’s LiDAR point cloud data processor, determine the minimum position of each UAV detected by the LiDAR relative to the UAV carrying the LiDAR. The minimum position is taken to ensure that the distance between the UAVs is large enough to ensure the flight safety of the UAVs in the formation.

[0101] The quick sorting method is used to sort the surrounding drones to obtain the minimum distance between each drone and itself. The specific process of the quick sorting method is as follows:

[0102] The Euclidean distance between the drone and its own drone is calculated based on the point cloud data of all the drones scanned by the laser radar, and the distance array {d 11 ,d 12 ,d 1j ,…,d kj ,…}, randomly select an element d in the distance array ab Take as the base element, traverse the array from front to back, and when encountering an element smaller than the base element, place it to the left of the base element; when encountering an element larger than the base element, place it to the right of the base element.

[0103] Then, take all elements smaller than the benchmark element as a new distance array, randomly select an element in the new distance array as the benchmark element, traverse the entire new distance array again, place elements smaller than the benchmark element on the left, and elements larger than the benchmark element on the right.

[0104] Iterate according to the above method until the minimum Euclidean distance is obtained, which is the minimum distance d between the drone scanned by the laser radar and its own drone. min , the coordinates of the point with the minimum distance between the drone itself and the drone scanned by the laser radar are P obs =(x o ,y o ,z o ).

[0105] The geodetic coordinate system takes the center of the earth as its origin, Z o The axis is the North Pole direction, X o The Y axis is the direction from the center of the earth to the intersection of the Greenwich meridian plane and the earth's equator. o Axis and X o , Z o The two axes are perpendicular to each other and form a right-handed system. The coordinates of each point in the geodetic coordinate system can be expressed as: (B, L, H), which represent the latitude, longitude and elevation of the point respectively. Latitude refers to the line angle between a point and the center of the earth and the equatorial plane of the earth, and its value is between 0° and 90°; longitude refers to the angle between a point and the two poles and the plane where the 0° longitude is located; geodetic elevation is the distance from a point in space to the reference ellipsoid surface along the normal direction of the reference ellipsoid.

[0106] The geodetic rectangular coordinate system coincides with the geodetic coordinate system. The coordinates of any point in the geodetic rectangular coordinate system can be expressed by the projection of the point on each coordinate axis of this coordinate system: (x g ,y g ,z g ).

[0107] In the geographic coordinate system, the origin is the position of the center of the aircraft at a certain moment, Z n Pointing to due north, X n Vertically pointing to the sky from the ground, Y n With Z n , X n They are perpendicular to each other and form a right-hand system, pointing due east. Figure 1 The coordinates of each point in the geographic coordinate system can be expressed as (x n ,y n ,z n ).

[0108] The origin of the carrier coordinate system is the center of the carrier navigation system, Y b Represents the horizontal axis of the aircraft, Z b Represents the longitudinal axis of the aircraft, X b From the belly to the back of the aircraft. The aircraft heading angle Φ, aircraft pitch angle γ, and aircraft roll angle θ represent the three attitude angles of the coordinate system relative to the geographic coordinate system. When the attitude angles are all zero, the three-axis orientation of the aircraft coordinate system coincides with the three-axis orientation of the geographic coordinate system. The coordinates of any point in the aircraft coordinate system are expressed as: (x b ,y b ,z b ).

[0109] Due to the initial installation error, the carrier coordinate system and the base coordinate system do not completely coincide. At the same time, since the base of the optoelectronic reconnaissance platform and the carrier are connected by a shock absorber, the vibration of the shock absorber will cause errors during operation. The three installation error angles of the base coordinate system relative to the carrier coordinate system are defined as Δφ I , Δγ I , Δθ I The three vibration error angles of the base coordinate system relative to the carrier coordinate system are defined as Δφ v , Δγ V , Δθ V .

[0110] This patent requires that the coordinates of the laser radar in the base coordinate system be converted into the geodetic coordinate system. The conversion steps are as follows:

[0111] (1) Conversion from geodetic coordinate system O to geodetic rectangular coordinate system G

[0112]

[0113] in,

[0114] e represents the first eccentricity of the ellipsoid; a represents the major radius of the ellipsoid;

[0115] (2) Conversion from the geodetic rectangular coordinate system G to the geographic coordinate system N

[0116]

[0117] Among them, (x0, y0, z0) are the coordinates of the origin of the N system coordinate system in the G system, and L0 and B0 are the longitude and latitude of the origin of the N system coordinate system.

[0118] (3) Conversion from geographic coordinate system N to aircraft coordinate system B

[0119] Around X n Transformation matrix for axis rotation φ

[0120]

[0121] After rotation, Y n The transformation matrix for the rotation around γ:

[0122]

[0123] After rotation Z n The transformation matrix of the axis rotation θ:

[0124]

[0125] Then the transformation from geographic coordinate system N to aircraft coordinate system B is:

[0126]

[0127] (4) Conversion from the carrier coordinate system B to the base coordinate system T

[0128] First, consider the installation error between the carrier coordinate system and the base coordinate system

[0129] Rotate Δφ around the Xb axis I The transformation matrix is:

[0130]

[0131] Rotate Δγ around the Yb axis after rotation I The transformation matrix is:

[0132]

[0133] The transformation matrix of the rotation Δ around the rotated Zb axis:

[0134]

[0135] The conversion of vibration error between the carrier coordinate system and the base coordinate system is similar to the conversion of installation error from the carrier coordinate system B to the base coordinate system T:

[0136]

[0137] After the above four calculation steps, the coordinate conversion from the laser radar positioning data coordinate system to the geodetic coordinate system is completed.

[0138] Step 2: Positioning and identifying the aircraft;

[0139] After data preprocessing and filtering, the format of the lidar measurement data is as follows: (x, y, z, reflectivity), where x, y, and z are the coordinates in the base coordinate system, and reflectivity is the reflectivity value, ranging from 0 to 255.

[0140] LiDAR can measure the position between drones, but it cannot know the number of the drone being measured. Since LiDAR can sense the color of the surface of an object, the reflectivity of the LiDAR measurement data will be different for different object colors. Specifically, if the surface of the object is black, the reflectivity value is below 70. If the surface of the object is white, the reflectivity value is above 180.

[0141] In the embodiment of the present invention, the leader and wingman are respectively in black and white colors, so that during formation flight, the laser radar carried by the UAV can regard the positioning with a reflectivity return value less than 70 as the leader, and the return value with a reflectivity greater than 180 as the wingman positioning data. In this way, the leader can be identified in advance, and further, different wingman data can be distinguished through the proposed vector matching algorithm.

[0142] UAV collaborative positioning-matching algorithm: Through the pre-set formation flight route, the position information of the UAV in the geodetic coordinate system at each moment can be known. Due to the error in the flight process, there will be a deviation between the actual position of the UAV and the preset position. Let the position deviation be R, and the actual position of the UAV is within the circle with the preset position as the center and the position deviation R as the radius. From this, the relative position vector of the UAV in the geodetic coordinate system can be calculated (the position of the UAV equipped with the laser radar points to the position of the UAV detected by the laser radar); on the other hand, the laser radar can measure the relative position between UAVs and obtain the laser radar positioning vector. By comparing the modulus, angle, and direction between the two vectors, the most matching pair of vectors is selected, and the UAV number corresponding to the laser radar positioning vector can be known.

[0143] The specific identification and positioning process is as follows:

[0144] Matching is performed based on the modulus and azimuth of the position vector obtained from the laser radar and the predetermined position of the route:

[0145] See also Figure 1 It can be seen that the two vectors are the route positioning vector (X2-X1, Y2-Y1, Z2-Z1) and the lidar positioning vector (X12, Y12, Z12). The preset algorithm parameters δ1 are 1.5 and δ2 are 0.4.

[0146] (1) The difference between the two vector moduli:

[0147]

[0148] Vector matching should satisfy D<δ1

[0149] (2) The difference between the angles of two vectors

[0150] Projection on the XY plane:

[0151]

[0152] Projection on the XZ plane:

[0153]

[0154] Projection on the YZ plane:

[0155]

[0156] When α+β+γ<δ2;

[0157] (3) Symbols with the same value

[0158] During installation, set the axis and angle directions of the laser radar geodetic coordinate system to be the same. For example, if the vectors match, the following conditions should be met: (X12, Y12, Z12) and (X2-X1, Y2-Y1, Z2-Z1) have the same sign.

[0159] That is, {X12*(X2-X1)>0&Y12*(Y2-Y1)>0&Z12*(Z2-Z1)>0} are true at the same time.

[0160] When the difference in modulus, angle, and sign all meet the conditions, the two vectors are considered to match and the recognition is completed.

[0161] For example, the laser radar carried by the drone can measure two sets of points, denoted as d1(a 12 ,a 13 ), the other 49 drones can get 49 sets of route positioning reference data: pos0, pos2, pos3, …, pos 50

[0162] Match these j groups of points with the 50 groups of points respectively, and perform at most 2*50=100 operations to obtain matching pairs of position vectors, that is, a12 matches pos2, and a13 matches pos3.

[0163] For 50 drones, the positions pos1, pos2, pos3, …, pos in the geodetic coordinate system can be obtained by route planning. 50

[0164] Among them are: pos i (x i ,y i ,z i )

[0165] Due to the distance and occlusion limitations, the laser radars carried by the 50 drones will measure the relative position information of multiple drones located around them, expressed as: d1, d2, d3, ..., d 50

[0166] where d k It is the relative position information of j drones around the kth drone:

[0167] d k (a 1k ,a 2k ,a 3k ,…,a jk ) (16)

[0168] Among them there is a km Indicates the relative coordinates of the position of the mth UAV collected by the Kth UAV's airborne laser radar:

[0169] a km (x km ,y km ,z km ) (17)

[0170] The distance matching function is defined as:

[0171]

[0172] Define the angle matching function:

[0173]

[0174]

[0175]

[0176] Define a symbolic comparison function:

[0177]

[0178] In the actual calculation process, the pos coordinates and their corresponding aircraft numbers are known information, the radar measurement data is known, and k is the drone with a determined position and number (starting with the lead drone No. 0), that is, the distance a needs to be determined mk The m in the figure corresponds to the actual aircraft number.

[0179] When the conditions are met:

[0180] D(a km ,pos i )<δ1 (23)

[0181] φ1(a km ,pos i )+φ2(a km ,pos i )+φ3(a km ,pos i)<δ2 (24)

[0182] At this time, q=1 is defined, where δ1 and δ2 are extremely small thresholds.

[0183] It can be assumed that the UAV numbered i is located at a distance a from the UAV numbered k. km That is to say, the distance a of the UAV numbered k is km The drone at is the wingman numbered i. For multiple drones, in the drone formation flight, the algorithm for matching the wingman drone with the distance scanned by the laser radar is:

[0184] Drones equipped with laser radar can scan drones within a certain range. Drones beyond the certain range cannot be identified. In addition, drones located in the same scanning angle cannot be identified due to occlusion, or only a small amount of point cloud data can be collected due to partial occlusion, and part of the small amount of point cloud data is discarded.

[0185] Identification steps:

[0186] See also Figure 2 In the first round of calculation, UAV 0 is the leader. The laser radar carried by UAV 0 can only determine that there are three UAVs at P1, P2 and P3. Figure 2 , and give the distance information of these three points relative to UAV 0, and use P 0-1 (x1, y1, z1), P 0-2 (x2, y2, z2), P 0-3 (x3, y3, z3) coordinates, but the numbers of the drones at these three locations cannot be determined. All drones can obtain their own geodetic coordinate information according to the route planning, so as to obtain the relative coordinates of all drones relative to drone No. 0, and the matching algorithm can be used to calculate which drone is at P1, P2, and P3.

[0187] See also Figure 3 In the second round of calculation, since the number of the drone at P1 has been determined, the drone at P1 is used as the standard. The laser radar carried by the drone at P1 can scan drones at P0, P2, P3, and P4, and P 1-0 , P 1-2 , P 1-3 , P 1-4 Four distance coordinate information, through the P determined in step 2 0-1 Coordinates, according to the position vector calculation method and the coordinate information of the four points scanned in this round, the four distance information relative to UAV No. 0 can be calculated, which are P 0-0 , P 0-2 , P 0-3 , P 0-4 , where P0-0 represents the number 0 itself, P 0-2 , P 0-3 The drone numbers at P2 and P3 have been determined in steps 2 and 3. So in this round, we only need to determine P 0-4 The drone number represented by the drone. The actual number of the unknown drone is determined by the matching algorithm.

[0188] See also Figure 4 ( Figure 4 a- Figure 4 d) In the third round of calculation, since the drone number at P2 has been determined, the second round of calculation process is repeated. The drone number at P6 can be determined. The subsequent process repeats the second round of calculation method in step 4 to calculate the positions of all drones in the formation and the corresponding drone numbers. After the above steps, the positioning information of each drone in the current drone fleet is obtained and sent to the flight controller for formation control.

[0189] Step 3: Determine the new formation position of the multi-aircraft formation:

[0190] (1) Position allocation in formation reconstruction

[0191] See also Figure 5 ,in Figure 5 In a, F1 to F4 represent the drone formation consisting of four drones before reconstruction; Figure 5 In b, P1~P4 represent the formations that need to be converted. In the formation reconstruction problem, the first thing to solve is to calculate the number of drones F in the original formation. i Assign a position P in a new formation i .

[0192] First, establish the weight matrix D∈D n*n , n is the number of drones in the formation, D ij Indicates that the original formation position F i Fly to the new formation P i Then find n D in different rows and columns so that these n D ij The sum of is the smallest, that is:

[0193] min∑D ij (25)

[0194] These n D ij This indicates the position of each drone in the new formation. Considering different mission requirements, changing D ij The definition of can obtain the location allocation results based on different optimization conditions.

[0195] When the number of drones in the formation increases, the scale of the position assignment problem will increase significantly, and the optimal method for formation reconstruction will require a lot of calculations. If you also want to consider issues such as collision avoidance, you need a faster algorithm. In this regard, this patent uses an ant colony search algorithm to search for the optimal solution to perform specific position allocation operations.

[0196] First, each value D in the weight matrix ij Each as a separate node v ij , we get an undirected complete graph, where any two points in the graph are connected. The drone number and the position number in the new formation are used as the two-dimensional coordinates of the node, indicating that the i-th position is occupied by the j-th drone, and each node corresponds one-to-one to the elements in the weight matrix.

[0197] Then the ant colony algorithm is used to perform specific location allocation operations, including initialization, strategy selection, pheromone update, local search, and finding the global optimal solution.

[0198] (2) Instruction calculation in formation reconstruction

[0199] The position allocation based on the ant colony algorithm finally obtains matching pairs such as F1→P4, F2→P1, etc. These matching pairs represent the change vectors of the flight trajectory of the drone in the three-dimensional space when it is reconstructed relative to the flight trajectory when there is no formation reconstruction, see Figure 6 , the straight line T1T2 represents the flight trajectory of the drone from time T1 to time T2 when there is no formation reconstruction. Due to the formation reconstruction, the actual trajectory becomes the straight line T1T2′, and the actual trajectory of the drone has a relative change shown by the straight line T1T1′. T1T1′ is the route represented by the matching pair. For the convenience of expression, these routes are defined as relative routes.

[0200] The control strategy proposed in this patent is to superimpose the relative route obtained by the decision with the original route to obtain a new route, analyze the constraints of the formation change, calculate and give appropriate instructions, so that multiple drones can fly along the new route and arrive at the new position at the required time.

[0201] For a formation of n drones, there are n such straight lines. A basic principle of drone formation reconstruction is that all drones start configuration reconstruction at time T1 and complete the reconstruction at the same time at time T2.

[0202] The embodiment of the present invention discloses a system for reconstructing a formation of multiple aircraft in a dense formation, including a laser radar data calibration preprocessing module, a UAV positioning and identification module, and a UAV formation reconstruction module.

[0203] The laser radar data calibration preprocessing module is used to convert the received UAV laser radar coordinate system into the geodetic coordinate system to obtain the geodetic rectangular coordinate system data of the UAVs in the fleet;

[0204] The UAV positioning and identification module calculates the relative position vector and positioning vector of the UAV based on the data of the UAV in the geodetic coordinate system, compares the module value, angle and direction between the two vectors, matches the two vectors, and obtains the positioning information of each UAV in the current UAV fleet; (obtains the UAV number corresponding to the UAV positioning vector)

[0205] The UAV formation reconstruction module is used to obtain the positioning information of each UAV within the current UAV fleet, and reallocate the position and route of the UAV according to the initial formation and the required formation.

[0206] A schematic diagram of a terminal device provided in an embodiment of the present invention. The terminal device of this embodiment includes: a processor, a memory, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps in the above-mentioned method embodiments are implemented. Alternatively, when the processor executes the computer program, the functions of the modules / units in the above-mentioned device embodiments are implemented.

[0207] The computer program may be divided into one or more modules / units, and the one or more modules / units are stored in the memory and executed by the processor to accomplish the present invention.

[0208] The terminal device may be a computing device such as a desktop computer, a notebook, a PDA, a cloud server, etc. The terminal device may include, but is not limited to, a processor and a memory.

[0209] The processor can be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc.

[0210] The memory may be used to store the computer programs and / or modules, and the processor implements various functions of the terminal device by running or executing the computer programs and / or modules stored in the memory and calling the data stored in the memory.

[0211] If the module / unit integrated in the terminal device is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present invention implements all or part of the processes in the above-mentioned embodiment method, and can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium, and the computer program can implement the steps of the above-mentioned various method embodiments when executed by the processor. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, U disk, mobile hard disk, disk, optical disk, computer memory, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), electric carrier signal, telecommunication signal and software distribution medium. It should be noted that the content contained in the computer-readable medium can be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable media do not include electric carrier signals and telecommunication signals.

[0212] The above are only preferred embodiments of the present invention and are not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A method for reconfiguring a formation of multiple aircraft in a dense formation, characterized in that: The following steps are involved: S1: Convert the received UAV laser radar coordinate system into the geodetic coordinate system to obtain the geodetic rectangular coordinate system data of the UAVs in the fleet; S2: Based on the data of the drone in the geodetic coordinate system, the drone relative position vector and the drone positioning vector are calculated, the module value, angle and direction between the two vectors are compared, the two vectors are matched, the positioning information of each drone in the current drone fleet is obtained, and the drone number corresponding to the drone positioning vector is obtained; S3: Obtain the positioning information of each drone in the current drone fleet, and reallocate the positions and routes of the drones according to the initial formation and the required formation; The S2 comprises the following steps: Based on the position information of the drone in the geodetic rectangular coordinate system, assuming that the deviation between the actual position of the drone and the preset position is R, the actual position of the drone is within a circle with the preset position as the center and the position deviation R as the radius. Based on this, the relative position vector of the drone in the geodetic coordinate system is calculated. At the same time, the laser radar measures the relative position between the drones to obtain the laser radar positioning vector. The S2 comprises the following steps: Assume that the coordinates of the planned position of the drone route are (X2, Y2, Z2); the coordinates of the position located by the laser radar are (X12, Y12, Z12), and the coordinates of the drone's position are (X1, Y1, Z1). Then the route positioning vector is (X2-X1, Y2-Y1, Z2-Z1), the lidar positioning vector is (X12, Y12, Z12), and the preset algorithm parameters δ1 are 1.5 and δ2 are 0.4; (1) The difference between the two vector moduli is: Vector matching must satisfy D<δ1; (2) The difference between the two vector angles is: Projection on the XY plane: Projection on the XZ plane: Projection on the YZ plane: When α+β+γ<δ2; (3) Symbols with the same value Vector matching must meet the following requirements: (X12, Y12, Z12) and (X2-X1, Y2-Y1, Z2-Z1) have the same sign, that is, {X12*(X2-X1)>0&Y12*(Y2-Y1)>0&Z12*(Z2-Z1)>0} are all true, then the two vectors are considered to match and the drone identification is completed.

2. A method for reconfiguring a formation of multiple aircraft in a dense formation according to claim 1, characterized in that: The S1 comprises the following steps: The Euclidean distance of each UAV is calculated and sorted using the quick sort method until the minimum Euclidean distance is obtained. The coordinates of the minimum distance between the own UAV and the UAV scanned by the lidar are determined based on the minimum Euclidean distance, and the coordinates are converted into geodetic rectangular coordinate system data.

3. The method for reconfiguring a formation of multiple aircraft in a dense formation according to claim 2, characterized in that: The S1 includes the following steps: Determine the Euclidean distance between a drone and each other drone: The detection results of the drone detecting the surrounding drones through the laser radar are recorded as C k =(x k ,y k ,z k ), where (x k ,y k ,z k ) is the position coordinate of the point, and the Euclidean distance from the detected drone to the fixed-wing drone carrying the laser radar is: Where k is the number of a point in the three-dimensional point cloud data of the UAV detected by the laser radar, j is the number of the UAV carrying the laser radar, and Xrj is the position of the UAV carrying the laser radar with number j in the x direction; Convert the received UAV lidar coordinate system to the earth coordinate system: (1) Conversion from geodetic coordinate system O to geodetic rectangular coordinate system G: in, e represents the first eccentricity of the ellipsoid; a represents the major radius of the ellipsoid; (2) Conversion from the geodetic rectangular coordinate system G to the geographic coordinate system N Among them, (x0, y0, z0) are the coordinates of the origin of the N system coordinate system in the G system, and L0 and B0 are the longitude and latitude of the origin of the N system coordinate system; (3) Conversion from geographic coordinate system N to aircraft coordinate system B Around X n The transformation matrix of the axis rotation φ: After rotation, Y n The transformation matrix for the rotation around γ: After rotation Z n The transformation matrix of the axis rotation θ: Then the transformation from geographic coordinate system N to aircraft coordinate system B is: (4) Conversion from the carrier coordinate system B to the base coordinate system T: First, consider the installation error between the carrier coordinate system and the base coordinate system, and rotate Δφ around the Xb axis I The transformation matrix is: Rotate Δγ around the Yb axis after rotation I The transformation matrix is: The transformation matrix of the rotation Δ around the rotated Zb axis: Transformation from the carrier coordinate system B to the base coordinate system T: After the above steps, the conversion of the laser radar positioning data coordinate system to the earth coordinate system is realized.

4. The method for reconfiguring a formation of multiple aircraft in a dense formation according to claim 1, characterized in that: The S3 comprises the following steps: Establish the weight matrix D∈D n*n , where n is the number of drones in the formation, D ij Indicates that the original formation position F i Fly to the new formation P i the cost required; Then find n D in different rows and columns so that these n D ij The sum of is the smallest, that is: min∑D ij (16) Among them, n D ij The position of each drone in the new formation is specified, and D is changed according to the requirements of different tasks. ij The definition of can obtain the location allocation results based on different optimization conditions.

5. A method for reconfiguring a formation of multiple aircraft in a dense formation according to claim 4, characterized in that: The S3 comprises the following steps: Use the ant colony search algorithm to allocate operations to specific locations: Assign each value D in the weight matrix ij As a single node v ij , we get an undirected complete graph, where any two points in the graph can be connected, and the drone numbers and the numbers of the positions in the new formation are used as the two-dimensional coordinates of the nodes, indicating that the i-th position is occupied by the j-th drone, and each node corresponds one-to-one to the elements in the weight matrix; The ant colony algorithm is further used to allocate the positions of UAVs, and the change vector of the flight trajectory of the UAVs in three-dimensional space when reconstructed relative to the flight trajectory when there is no formation reconstruction is obtained, and the matched relative route is further obtained.

6. A system for reconfiguring the formation of a multi-aircraft dense formation, used to execute the method for reconfiguring the formation of a multi-aircraft dense formation as claimed in claim 1, characterized in that: It includes a laser radar data calibration preprocessing module, a UAV positioning and identification module, and a UAV formation reconstruction module; The laser radar data calibration preprocessing module is used to convert the received UAV laser radar coordinate system into the geodetic coordinate system to obtain the geodetic rectangular coordinate system data of the UAVs in the fleet; The UAV positioning and identification module calculates the relative position vector and positioning vector of the UAV based on the data of the UAV in the geodetic coordinate system, compares the module value, angle and direction between the two vectors, matches the two vectors, obtains the positioning information of each UAV in the current UAV fleet, and obtains the UAV number corresponding to the UAV positioning vector; The UAV formation reconstruction module is used to obtain the positioning information of each UAV within the current UAV fleet, and reallocate the position and route of the UAV according to the initial formation and the required formation.

7. A terminal device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 5 are implemented.

8. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 5 are implemented.

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