A method, system, device and storage medium for assigning a landing site to a drone cluster

Abstract

The application discloses a kind of unmanned aerial vehicle cluster landing point allocation method, system, equipment and storage medium, it is related to landing point allocation technical field, including steps: obtaining P in unmanned aerial vehicle landing area Landing point, if the number of landing points P is greater than the number of unmanned aerial vehicles N, select the highest priority N landing points as unmanned aerial vehicle landing point, if the x-axis distance of two adjacent unmanned aerial vehicle landing point coordinates is less than threshold value, then one of unmanned aerial vehicle landing point is adjusted as: when the unmanned aerial vehicle can land in unmanned aerial vehicle landing area, the y-axis position is spaced apart from the y-axis position of adjacent unmanned aerial vehicle by more than the threshold value Landing point;Check whether all unmanned aerial vehicles and landing point connecting line intersect, when intersect, exchange landing point of unmanned aerial vehicle and subsequent number unmanned aerial vehicle, obtain the final landing point allocation scheme.The application adjusts the correspondence relationship between unmanned aerial vehicle and unmanned aerial vehicle landing point under the principle of ensuring that the flight path of unmanned aerial vehicle does not intersect, obtains the allocation scheme of unmanned aerial vehicle and unmanned aerial vehicle landing point, improves the guiding effect of landing point when multiple unmanned aerial vehicles.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of landing point allocation, in particular to a UAV cluster landing point allocation method, system, device and storage medium. BACKGROUND

[0002] The rapid and accurate autonomous landing technology of UAVs can be applied to the fields of object distribution and autonomous recovery of UAVs, and is also a basic link and necessary function for UAVs to perform flight tasks autonomously.

[0003] In actual landing point allocation, the information of the landing point area of the UAVs may not be complete and accurate. Incomplete and inaccurate fuzzy information of the landing point area of the UAVs may lead to decision errors, task failures and resource waste. Therefore, how to deal with the problem of accurate guidance and landing of the UAV cluster with uncertainty caused by inaccurate information has important research value.

[0004] The existing UAV landing point allocation method only allocates the landing points and the UAVs one by one in different task stages of landing point allocation. In actual allocation, when multiple UAVs land in the same area at the same time, route competition conflicts between the UAVs may occur, leading to task failures and resource waste. SUMMARY

[0005] The present application aims to solve the problem of the prior art that only allocates the landing points and the UAVs one by one, which may cause route competition conflicts between the UAVs when multiple UAVs land in the same area at the same time, leading to task failures and resource waste.

[0006] The present application specifically provides the following technical solutions:

[0007] A UAV cluster landing point allocation method comprises the following steps:

[0008] Obtaining P landing points in the landing point area of the UAVs;

[0009] When the number P of landing points is greater than the number N of UAVs, selecting the N landing points with the highest priority as the UAV landing points, and sorting the UAV landing points in ascending order according to the x-axis coordinate size according to the index of total energy consumption being lower than a threshold value; after the ascending order sorting, if the x-axis distance between the coordinates of two adjacent UAV landing points is less than a threshold value, adjusting one of the UAV landing points to be a landing point with a y-axis position having a distance greater than a threshold value from the y-axis position of the adjacent UAV when the UAV lands in the landing point area of the UAVs.

[0010] After adjusting the landing points of all drones, obtain the connection between all drones and their landing points, check if all connections intersect, and if they intersect, swap the landing points of the current drone with the drone of the next sequence number to obtain the final drone landing point allocation scheme.

[0011] Preferably, the step of obtaining the lines connecting all drones and their landing points, and checking whether all lines intersect, includes the following steps:

[0012] Obtain the projected coordinates M1(x) of two of the drones on the ground. a y a ) and M2(x c y c ), and obtain the landing coordinates D1(x) of the two ground drones. b y b ) and D2(x d y d ), and obtain the connection lines M1D1 and M2D2 from the two drones to their corresponding drone landing points;

[0013] Obtain the M1D1 expression f(x, y) connecting the first drone to its corresponding drone landing point; the specific expression is:

[0014] f(x, y) = (yy) a )*(x b -x a )-(y b -y a )*(xx a ) = 0.

[0015] If M1D1 and M2D2 intersect, then substitute the coordinates of points M2 and D2 into f(x, y) to obtain a value less than or equal to zero; the specific expression is:

[0016] f(x c y c )*f(x d y d )≤0.

[0017] Similarly, the expression g(x, y) for M2D2 is obtained; the specific expression is:

[0018] g(x, y) = (yy) c )*(x d -x c )-(y d -y c )*(xx c ) = 0.

[0019] When M1D1 and M2D2 intersect, the specific expression is:

[0020] g(x a , y a ) * g(x b , y b ) < 0.

[0021] When M1D1 and M2D2 intersect, and M1D1 intersects M2D2, two lines of two unmanned aerial vehicles to their corresponding landing points intersect; wherein, x and y are horizontal and vertical coordinates of the unmanned aerial vehicle on the ground projection coordinates, x a and y a are horizontal and vertical coordinates of projection coordinate M1, x c and y c are horizontal and vertical coordinates of projection coordinate M2, x b and y b are horizontal and vertical coordinates of projection coordinate D1, x d and y d are horizontal and vertical coordinates of projection coordinate D2.

[0022] Preferably, when M1D1 and M2D2 intersect, and M1D1 intersects M2D2, two lines of two unmanned aerial vehicles to their corresponding landing points intersect, specifically:

[0023] When the coordinates of two unmanned aerial vehicles and their corresponding unmanned aerial vehicle landing points satisfy:

[0024]

[0025] Specifically:

[0026]

[0027] That is, when the above conditions are satisfied, two lines of two unmanned aerial vehicles to their corresponding unmanned aerial vehicle landing points intersect.

[0028] Preferably, the P landing points in the unmanned aerial vehicle landing area are obtained, comprising the following steps:

[0029] Randomly generate a plurality of sample points in the unmanned aerial vehicle landing area;

[0030] Randomly divide the coordinate data of the sample points into P groups, and randomly select one sample point in each group, and take the coordinates of the P sample points as the initial P clustering centers;

[0031] Obtain the distance between each sample point and each clustering center, and assign each sample point to the nearest clustering center;

[0032] Iterate the assignment process, record the clustering result each time, and when the maximum iteration number is reached, obtain the minimum sum of squares of the distances of all sample points to their clustering centers, and select the clustering result when the minimum sum of squares is obtained.

[0033] Select P sample points obtained in the clustering result as selected landing points, and obtain the coordinates of the P landing points.

[0034] Preferably, the step of assigning each sample point to the nearest cluster center further comprises:

[0035] In the step of assigning sample points, the cluster centers are reacquired according to the existing sample points in the cluster after each sample point is assigned; wherein the cluster centers and the sample points assigned to them represent a cluster.

[0036] The present application provides a kind of unmanned aerial vehicle cluster landing point allocation system, comprising:

[0037] Data acquisition module, for obtaining P landing points in unmanned aerial vehicle landing area;

[0038] Landing point adjustment module, for selecting the highest priority N landing points as unmanned aerial vehicle landing point when the number P of landing points is greater than the number N of unmanned aerial vehicles, and sorting unmanned aerial vehicle landing point in ascending order according to the size of x-axis coordinate according to the index that total energy consumption is lower than threshold value;After ascending order sorting, if the x-axis distance of the coordinates of two adjacent unmanned aerial vehicle landing points is less than threshold value, then one of the unmanned aerial vehicle landing points is adjusted to: the y-axis position of the unmanned aerial vehicle landing point is greater than the threshold value from the y-axis position of adjacent unmanned aerial vehicle when the unmanned aerial vehicle can land in unmanned aerial vehicle landing area;

[0039] Distribution scheme acquisition module, for obtaining the connection line of all unmanned aerial vehicles and unmanned aerial vehicle landing points after adjusting all unmanned aerial vehicle landing points, checking whether all connection lines intersect, when intersecting, exchanging unmanned aerial vehicle landing points between current unmanned aerial vehicle and unmanned aerial vehicle with next serial number, to obtain final unmanned aerial vehicle landing point allocation scheme.

[0040] The present application provides a kind of computer equipment, including memory and processor, the memory has stored program, the program is executed by the processor, so that the processor executes the steps of the kind of unmanned aerial vehicle cluster landing point allocation method.

[0041] The present application provides a kind of storage medium, which stores computer program, the computer program is executed by processor to realize the steps of the kind of unmanned aerial vehicle cluster landing point allocation method.

[0042] Compared with prior art, the present application has the following obvious advantages:

[0043] This invention acquires P landing points, and when the number of drones is less than P, it selects the highest priority drone landing point. To avoid drone landing points being too close together, it adjusts adjacent drone landing points so that the distance between them on the y-axis is greater than a threshold. Furthermore, to ensure that drone flight paths do not intersect, it performs cross-judgment based on the lines connecting drones and their landing points. When a cross-judgment occurs, it adjusts the correspondence between drones and their landing points to obtain a drone landing point allocation scheme. By adjusting drone landing points and the cross-judgment of the lines connecting drones and their landing points, the invention improves the guidance effect and resource utilization of landing points when there are multiple drones, and greatly reduces the probability of drone landing mission failure due to guidance errors. Attached Figure Description

[0044] Figure 1 This is a schematic diagram of a cluster of drones landing missions provided by the present invention;

[0045] Figure 2 This invention provides a schematic diagram of drone collision avoidance when the x-axis distance between the landing points is relatively close.

[0046] Figure 3 This is a flowchart of a method for allocating landing points for a drone swarm according to the present invention. Detailed Implementation

[0047] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the protection scope of the present invention.

[0048] The purpose of this invention is to provide a method, system, device, and storage medium for allocating landing points for a swarm of drones. Without loss of generality, this invention considers a ground-based drone landing area (such as a cargo drone delivery point, an autonomous landing drone recovery point, or a drone charging station) where N drones simultaneously take off and land in that area. Figure 1 As shown. Before takeoff, the UAV obtains approximate landing point information provided by the navigation system. Assuming there are several approximate landing points, the UAV re-identifies the detailed landing point information through an onboard detection device as it approaches the landing point. The requirement is that the UAVs do not collide with each other, and the total energy consumption should be minimized. This invention analyzes the uncertainty of the landing point location caused by the inaccuracy of the detection information. First, based on the detection uncertainty during the mission execution, a pre-allocation algorithm is provided. Then, after the UAV approaches the landing point area, a re-allocation is performed based on the detailed landing point information provided by the onboard detection device.

[0049] According to the task requirements, the performance index of the problem is extracted as: the total energy consumption of all unmanned aerial vehicles flying to the corresponding landing points is as small as possible. The constraint condition of the problem is: during the process of each unmanned aerial vehicle to the corresponding landing point, there is no mutual collision.

[0050] As shown in Figure 3 The present application provides a method for assigning landing points to a cluster of unmanned aerial vehicles, which specifically comprises the following steps:

[0051] Step S1: Obtain P landing points in the unmanned aerial vehicle landing area.

[0052] S1 specifically comprises:

[0053] The landing points are obtained by a navigation system during pre-allocation, and are obtained by an unmanned aerial vehicle detector during re-allocation; the landing points include cargo unmanned aerial vehicle distribution points, autonomous landing unmanned aerial vehicle recovery points and unmanned aerial vehicle charging piles.

[0054] Before the unmanned aerial vehicle takes off, the landing point information obtained by the navigation system is subjected to cluster analysis (using the K-Means clustering algorithm) to obtain P landing point coordinates, comprising the following steps:

[0055] Randomly generate a number of sample points in the unmanned aerial vehicle landing area.

[0056] Randomly divide the coordinate data of the sample points into P groups, and randomly select one sample point in each group, and take the coordinates of the P sample points as the initial P cluster centers.

[0057] Obtain the distance between each sample point and each cluster center, and assign each sample point to the cluster center closest to it.

[0058] Iterate the assignment process, record the result of each clustering, and when the maximum number of iterations is reached, obtain the minimum sum of squares of the distances of all sample points to their cluster centers, and select the clustering result at the minimum sum of squares.

[0059] Select the P sample points obtained in the clustering result as the selected landing points, and obtain the coordinates of the P landing points.

[0060] Assign each sample point to the cluster center closest to it, which further comprises the following steps:

[0061] When assigning a sample point, the cluster center of the cluster is reacquired according to the existing sample points in the cluster; wherein the cluster center and the sample points assigned to them represent a cluster.

[0062] Step S2: identify P landing point information in the UAV landing area, when the number of landing points P is greater than the number of UAVs N (i.e. P>N), select the N landing points with the highest priority as the UAV landing points, and sort the N UAVs and their corresponding UAV landing points in ascending order according to the x-axis coordinate size according to the index of total energy consumption being lower than the threshold value; after ascending order sorting, based on the constraint condition of preventing flight path intersection, if the x-axis distance of the coordinates of two adjacent UAV landing points is less than the threshold value, then one of the UAV landing points is adjusted to a landing point whose y-axis position is more than the threshold value away from the y-axis position of the adjacent UAV when the UAV lands in the UAV landing area. As shown in Figure 2

[0063] S2 is specifically:

[0064] Select the N landing points with the highest priority from the P sample points obtained by clustering as the UAV landing points. The UAV landing points are obtained by a navigation system during pre-allocation, and are obtained by a UAV detector during re-allocation.

[0065] Step S3: after adjusting all UAV landing points, obtain the lines connecting all UAVs and UAV landing points, check whether all lines intersect, and when they intersect, exchange the UAV landing points of the current UAV and the UAV with the next serial number to obtain the final UAV landing point allocation scheme.

[0066] S3 is specifically:

[0067] In order to more accurately ensure that the flight paths do not intersect, check whether the corresponding UAV-landing point line intersects, if it intersects, exchange the landing points of the UAV and the UAV with the next serial number, and check in this way until all UAV-landing point lines do not have any intersection points.

[0068] Obtain the lines connecting all UAVs and UAV landing points, check whether all lines intersect, including the following steps:

[0069] Obtain the projection coordinates M1(x a , y a ) and M2(x c , y c ) of the two UAVs on the ground, and obtain the UAV landing point coordinates D1(x b , y b ) and D2(x d , y d ) of the two UAVs on the ground, and obtain the lines M1D1 and M2D2 connecting the two UAVs to their corresponding UAV landing points.

[0070] Obtain the expression f(x, y) of the line M1D1 connecting the first UAV to its corresponding UAV landing point; the specific expression is:

[0071] ​f(x, y) = (y - y a )*(x b -x a )-(y b -y a )*(x-x a ) = 0.

[0072] If M1D1 and M2D2 intersect, then the coordinates of M2 and D2 are substituted into f(x, y) to obtain a value less than or equal to zero; the specific expression is:

[0073] f(x c , y c )*f(x d , y d )≤0.

[0074] Similarly, the expression g(x, y) of M2D2 is obtained; the specific expression is:

[0075] g(x, y) = (y - y c )*(x d -x c )-(y d -y c )*(x-x c ) = 0.

[0076] When M1D1 and M2D2 intersect, the specific expression is:

[0077] g(x a , y a )*g(x b , y b )≤0.

[0078] When M1D1 and M2D2 intersect, and M1D1 and M2D2 intersect, the two lines connecting the two unmanned aerial vehicles to the respective corresponding landing points intersect; wherein x and y are the horizontal and vertical coordinates of the unmanned aerial vehicle on the ground projection coordinates, x a and y a are the horizontal and vertical coordinates of the projection coordinate M1, x c and y c are the horizontal and vertical coordinates of the projection coordinate M2, x b and y b are the horizontal and vertical coordinates of the projection coordinate D1, and x d and y d are the horizontal and vertical coordinates of the projection coordinate D2.

[0079] When M1D1 and M2D2 intersect, and M1D1 and M2D2 intersect, the two lines connecting the two unmanned aerial vehicles to the respective corresponding landing points intersect, and the specific expression is:

[0080] When the two unmanned aerial vehicles and the coordinates of the corresponding unmanned aerial vehicle landing points satisfy:

[0081]

[0082] Specifically:

[0083]

[0084] That is, when the above conditions are met, the two lines of two unmanned aerial vehicles to the corresponding unmanned aerial vehicle landing points intersect.

[0085] Based on the above method, the application provides an unmanned aerial vehicle cluster landing point allocation system, comprising a data acquisition module, a landing point adjustment module and an allocation scheme acquisition module.

[0086] The data acquisition module is used to acquire P landing points in the unmanned aerial vehicle landing area; the landing point adjustment module is used to select the N landing points with the highest priority as the unmanned aerial vehicle landing points when the number of landing points P is greater than the number of unmanned aerial vehicles N, and sort the unmanned aerial vehicle landing points in ascending order according to the x-axis coordinate size according to the index that the total energy consumption is lower than the threshold value; after ascending sorting, if the x-axis distance of the coordinates of two adjacent unmanned aerial vehicle landing points is less than the threshold value, one of the unmanned aerial vehicle landing points is adjusted to a landing point whose y-axis position is spaced apart from the y-axis position of the adjacent unmanned aerial vehicle by a distance greater than the threshold value when the unmanned aerial vehicle lands in the unmanned aerial vehicle landing area; the allocation scheme acquisition module is used to acquire the lines of all unmanned aerial vehicles and unmanned aerial vehicle landing points after adjusting all unmanned aerial vehicle landing points, check whether all lines intersect, exchange the unmanned aerial vehicle landing points of the current unmanned aerial vehicle and the unmanned aerial vehicle with the next serial number when the lines intersect, and obtain the final unmanned aerial vehicle landing point allocation scheme.

[0087] The application also provides a computer device comprising a memory and a processor, the memory storing a program, and the program being executed by the processor to make the processor execute the steps of the unmanned aerial vehicle cluster landing point allocation method.

[0088] According to the disclosed embodiments, the computer device can communicate with one or more external devices (such as a keyboard, a pointing device, a Bluetooth communication, etc.), or with any device (such as a router, a demodulator, etc.) that enables the computer device to communicate with one or more other computer devices.

[0089] The application also provides a storage medium having a computer program stored thereon, and the computer program is executed by the processor to implement the unmanned aerial vehicle cluster landing point allocation method.

[0090] According to the disclosed embodiments, the storage medium can be a non-transitory computer-readable storage medium, which can include, but is not limited to, a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In this application, a storage medium can be any tangible media that contains or stores a program used or produced by an instruction execution system, apparatus, or device.

[0091] The various embodiments in the specification are described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the various embodiments can be mutually referred to.

[0092] The principles and implementation manners of the present application are described by using specific examples. The above description of the embodiments is only used to help understand the method of the present application and its core idea. Meanwhile, for those skilled in the art, the specific implementation manners and application regions can be changed according to the idea of the present application. In summary, the content of the specification should not be understood as a limitation of the present application.

Claims

1. A method for allocating landing points in a drone swarm, characterized in that, Comprise: acquiring a landing point in a landing area of the unmanned aerial vehicle P the landing point; Number of landing points P More than the number of drones N When selecting, choose the one with the highest priority. N The landing points are designated as the drone landing points, and the drone landing points are further classified according to the criterion that the total energy consumption is below a threshold. x The axis coordinates are sorted in ascending order; after ascending sorting, if the coordinates of two adjacent drone landing points are... x If the axial distance is less than the threshold, then the landing point of one of the drones will be adjusted to: the landing point area where that drone can land. y Axis position relative to adjacent drones y Points where the axis position spacing is greater than the threshold; After adjusting all the unmanned aerial vehicle landing points, obtain all the lines connecting the unmanned aerial vehicles and the unmanned aerial vehicle landing points, check whether all the lines intersect, and exchange the unmanned aerial vehicle landing points of the current unmanned aerial vehicle and the unmanned aerial vehicle with the next serial number when intersecting to obtain a final unmanned aerial vehicle landing point allocation scheme.

2. The method of claim 1, wherein, The step of obtaining all the lines connecting the unmanned aerial vehicles and the unmanned aerial vehicle landing points and checking whether all the lines intersect comprises the following steps: acquire the projection coordinates of the two drones on the ground with and acquire the landing coordinates of the two drones on the ground with and acquire the connecting lines of the two drones to their corresponding drone landing points with ; acquiring a line connecting the first drone to its corresponding drone landing point expression ; The specific expression is: ； like and When they intersect, then... and Substitute the coordinates of the two points To obtain a value less than or equal to zero; the specific expression is: ； By analogy, the expression ; the specific expression is:​ ； In With the specific expression is: ； When the following conditions are met and , the two lines connecting the two unmanned aerial vehicles to their respective corresponding unmanned aerial vehicle landing points intersect; wherein, and are the horizontal and vertical coordinates of the ground projection coordinates of the unmanned aerial vehicle, and are the horizontal and vertical coordinates of the projection coordinates , and are the horizontal and vertical coordinates of the projection coordinates , and are the horizontal and vertical coordinates of the projection coordinates , and are the horizontal and vertical coordinates of the projection coordinates . 3.The method of claim 2, wherein, The in meeting And When, two unmanned aircraft to the respective corresponding two lines of intersection of the drop point, specifically: When the coordinates of two unmanned aerial vehicles and their corresponding unmanned aerial vehicle landing points satisfy: ； Specifically: ； That is, when the above conditions are met, the two lines connecting the two unmanned aerial vehicles to their corresponding unmanned aerial vehicle landing points intersect.

4. The method of claim 1, wherein, The acquisition of the drone landing area P The landing point includes the following steps: Randomly generate a number of sample points in the unmanned aerial vehicle landing point area; The coordinate data of the sample points are randomly divided into P groups, and one sample is randomly selected in each group, and the coordinates of the P samples are taken as the initial P cluster centers; Obtain the distance between each sample point and each cluster center, and assign each sample point to the cluster center closest to it; Iterate through the assignment process, record the clustering results each time, and obtain the minimum sum of squares of the distances from all sample points to their cluster centers when the maximum number of iterations is reached, and select the clustering result at the minimum sum of squares; Select the sample point obtained in the clustering result as the selected landing point, and obtain the coordinates of the landing point. P P the coordinates of the landing point.​ 5. The method of claim 4, wherein, The step of assigning each sample point to the cluster center closest to it further comprises: When assigning sample points, re-obtain the cluster centers of the clusters according to the existing sample points in the clusters each time a sample point is assigned; wherein the cluster centers and the sample points assigned to them represent a cluster.

6. A UAV swarm homing allocation system, characterized in that, Comprise: The data acquisition module is configured to acquire a landing point in a landing area of the UAV. P The data acquisition module is configured to acquire a landing point in a landing area of the UAV. The landing point adjustment module is configured to select, when the number of landing points is greater than the number of UAVs P , the landing point with the highest priority as the UAV landing point, and sort the UAV landing points in ascending order according to the axis coordinate size, and after the ascending order sorting, if the distance between the axis coordinates of two adjacent UAV landing points is less than a threshold value, adjust one of the UAV landing points to be a landing point at which the UAV can land in the UAV landing point region N , and the position of the axis is greater than the threshold value from the position of the axis of the adjacent UAV N . x x y y ​​​​ The allocation scheme acquisition module is configured to, after adjusting all the unmanned aerial vehicle landing points, obtain all the lines connecting the unmanned aerial vehicles and the unmanned aerial vehicle landing points, check whether all the lines intersect, and exchange the unmanned aerial vehicle landing points of the current unmanned aerial vehicle and the unmanned aerial vehicle with the next serial number when intersecting to obtain a final unmanned aerial vehicle landing point allocation scheme.

7. A computer device, characterized by The computer program is executed by the processor to implement the steps of the unmanned aerial vehicle cluster landing point allocation method of any one of claims 1 to 5.

8. A storage medium having stored thereon a computer program, characterized in that The computer program is executed by the processor to implement the steps of the unmanned aerial vehicle cluster landing point allocation method of any one of claims 1 to 5.

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