Unmanned aerial vehicle cluster drop point distribution method, system and device and storage medium

Abstract

The invention discloses an unmanned aerial vehicle cluster landing point distribution method, system and device and a storage medium, and relates to the technical field of landing point distribution, and the method comprises the steps: obtaining P landing points in an unmanned aerial vehicle landing point region, and if the number P of landing points is greater than the number N of unmanned aerial vehicles, selecting N landing points with the highest priority as unmanned aerial vehicle landing points, if the x-axis distance between two adjacent unmanned aerial vehicle landing point coordinates is smaller than a threshold value, one unmanned aerial vehicle landing point is adjusted to be a landing point where the distance between the y-axis position and the y-axis position of the adjacent unmanned aerial vehicle is larger than the threshold value when the unmanned aerial vehicle can land in the unmanned aerial vehicle landing point area; whether all the unmanned aerial vehicles intersect with the drop point connecting lines or not is checked, and when the unmanned aerial vehicles intersect with the drop point connecting lines, drop points are exchanged between the unmanned aerial vehicles and the unmanned aerial vehicles of the next serial number, and a final drop point distribution scheme is obtained. According to the invention, under the principle of ensuring that the flight paths of the unmanned aerial vehicles are not crossed, the corresponding relation between the unmanned aerial vehicles and the unmanned aerial vehicle landing points is adjusted, the distribution scheme of the unmanned aerial vehicles and the unmanned aerial vehicle landing points is obtained, and the landing point guiding effect is improved when multiple unmanned aerial vehicles exist.

Description

Technical Field

[0001] The present invention relates to the technical field of landing point allocation, and in particular to a method, system, device and storage medium for allocating landing points of a drone cluster. Background Art

[0002] The drone's fast and precise autonomous landing technology can be applied to areas such as object delivery and autonomous drone recovery. It is also a basic link and necessary function for drones to autonomously perform flight missions.

[0003] In the actual landing point allocation, we will face the phenomenon of incomplete and inaccurate information on the landing area of ​​drones. Incomplete and inaccurate information on the landing area of ​​fuzzy drones may lead to wrong decisions, mission failure, and waste of resources. Therefore, how to deal with the problem of accurate guidance of landing of drone clusters with uncertainty caused by inaccurate information is of great research value.

[0004] The existing method of allocating landing points for drones only allocates landing points and drones one by one during different mission stages of landing point allocation. In actual allocation, when multiple drones land in the same area at the same time, it will cause route competition conflicts between drones, leading to mission failure and waste of resources. Summary of the invention

[0005] The purpose of the present invention is to address the deficiencies of the above-mentioned prior art and provide a method, system, device and storage medium for allocating landing points for a drone cluster, so as to solve the problem that in the prior art, landing points and drones are only allocated one by one. In actual allocation, when multiple drones land in the same area at the same time, route competition conflicts will be caused between the drones, resulting in mission failure and waste of resources.

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

[0007] A method for allocating landing points of a drone cluster comprises the following steps:

[0008] Get P landing points in the drone landing area;

[0009] When the number of landing points P is greater than the number of drones N, select the N landing points with the highest priority as the drone landing points, and sort the drone landing points in ascending order according to the x-axis coordinate size according to the indicator that the total energy consumption is lower than the threshold; after sorting in ascending order, if the x-axis distance between the coordinates of two adjacent drone landing points is less than the threshold, adjust one of the drone landing points to: when the drone can land in the drone landing area, the y-axis position of the adjacent drone is greater than the threshold.

[0010] After adjusting the landing points of all drones, obtain the connection lines between all drones and the drone landing points, check whether all the connection lines intersect, and if they do, exchange the drone landing point of the current drone with the drone with the next serial number to obtain the final drone landing point allocation plan.

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

[0012] Get the projection coordinates M1(x a ,y a ) and M2(x c ,y c ), and obtain the coordinates of the two ground drone landing points D1(x 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] Get the M1D1 expression f(x, y) of the line from the first drone to the landing point of its corresponding drone; 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, 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) of 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 intersects M2D2, the specific expression is:

[0020] g(x a ,y a )*g(x b ,y b )≤0.

[0021] When M1D1 and M2D2 intersect, and M1D1 and M2D2 intersect, the two lines connecting the two drones to their corresponding landing points intersect; x and y are the horizontal and vertical coordinates of the drone's projection coordinates on the ground, respectively. a and a are the horizontal and vertical coordinates of the projection coordinate M1, x c and c are the horizontal and vertical coordinates of the projection coordinate M2, x b and b are the horizontal and vertical coordinates of the projection coordinate D1, x d and d are the horizontal and vertical coordinates of the projection coordinate D2 respectively.

[0022] Preferably, when M1D1 and M2D2 intersect, and M1D1 and M2D2 intersect, two lines connecting the two drones to their corresponding landing points intersect, specifically:

[0023] When the coordinates of the two drones and their corresponding drone landing points meet:

[0024]

[0025] Specifically:

[0026]

[0027] That is, when the above conditions are met, the two lines connecting the two drones to the landing points of their corresponding drones intersect.

[0028] Preferably, the step of obtaining P landing points in the drone landing area comprises the following steps:

[0029] A number of sample points are randomly generated in the drone landing area;

[0030] The coordinate data of the sample points are randomly divided into P groups, and a sample is randomly selected from each group, and the coordinates of the P samples are used as the initial P cluster centers;

[0031] Get the distance between each sample point and each cluster center, and assign each sample point to the cluster center closest to it;

[0032] Traverse the allocation process, record the results of each clustering, and when the maximum number of iterations is reached, obtain the minimum sum of squares of the distances from all sample points to their cluster centers, and select the clustering result with the minimum sum of squares;

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

[0034] Preferably, the step of assigning each sample point to the cluster center closest to it further includes:

[0035] When assigning sample points, each time a sample point is assigned, the cluster center of the cluster is re-obtained based on the existing sample points in the cluster; the cluster center and the sample points assigned to them represent a cluster.

[0036] The present invention provides a UAV cluster landing point allocation system, comprising:

[0037] A data acquisition module is used to acquire P landing points in the drone landing area;

[0038] The landing point adjustment module is used to select the N landing points with the highest priority as the landing points of the drones when the number of landing points P is greater than the number of drones N, and sort the landing points of the drones in ascending order according to the x-axis coordinate size according to the indicator that the total energy consumption is lower than the threshold; after sorting in ascending order, if the x-axis distance between the coordinates of the landing points of two adjacent drones is less than the threshold, then adjust one of the landing points of the drones to: when the drone can land in the drone landing area, the y-axis position of the drone is greater than the threshold distance from the y-axis position of the adjacent drone;

[0039] The allocation plan acquisition module is used to obtain the connection lines between all drones and drone landing points after adjusting all drone landing points, check whether all connection lines intersect, and if they do, exchange the drone landing point of the current drone with the drone with the next serial number to obtain the final drone landing point allocation plan.

[0040] The present invention provides a computer device, including a memory and a processor, wherein a program is stored in the memory, and when the program is executed by the processor, the processor executes the steps of a method for allocating landing points of a drone cluster.

[0041] The present invention provides a storage medium on which a computer program is stored. When the computer program is executed by a processor, the steps of the above-mentioned method for allocating landing points of a drone cluster are implemented.

[0042] Compared with the prior art, the present invention has the following significant advantages:

[0043] The present invention obtains P landing points, and obtains the landing point of the drone with the highest priority when the number of drones is less than P. In order to avoid the drones landing points being too close, the landing points of adjacent drones are adjusted to landing points with a y-axis position spacing greater than a threshold. In order to ensure the principle that the drone tracks do not intersect, an intersection judgment is made based on the lines connecting the drones and the drone landing points. When there is an intersection, the corresponding relationship between the drones and the drone landing points is adjusted to obtain a distribution plan for the drones and the drone landing points. Through the adjustment of the drone landing points and the intersection adjustment of the drone and drone landing point connection lines, the guidance effect and resource utilization rate of the landing points when there are multiple drones are improved, and the probability of failure of the drone landing mission due to guidance errors is greatly reduced. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] Figure 1 A schematic diagram of a cluster UAV landing mission provided by the present invention;

[0045] Figure 2 A schematic diagram of UAV collision avoidance when the x-axis distance of the landing point provided by the present invention is relatively close;

[0046] Figure 3 The present invention is a flowchart of a method for allocating landing points of a drone cluster. DETAILED DESCRIPTION

[0047] The following is a clear and complete description of the technical solutions of the embodiments of the present invention in conjunction with the drawings in the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should belong to the protection area of ​​the present invention.

[0048] The purpose of the present invention is to provide a method, system, device and storage medium for allocating landing points of drone clusters. Without loss of generality, consider a certain ground drone landing area (such as a cargo drone delivery point, an autonomous landing drone recovery point, a drone charging station, etc.), and N drones take off at the same time and land in the area. Figure 1 As shown. Before the drone takes off, it obtains the approximate landing point information provided by the navigation system. Assuming that there are several approximate landing points, the drone will identify the landing point details again through the onboard detection device when it approaches the landing point. It is required that there is no collision between drones and the total energy consumption should be as small as possible. The present invention analyzes the uncertainty of the landing point position caused by the inaccuracy of the detection information. First, according to the detection uncertainty in the task execution process, a pre-allocation algorithm is first given; then, after the drone approaches the drone landing area, it is redistributed based on the detailed landing point information provided by the onboard detection device.

[0049] According to the task requirements, the performance indicators of the problem are: the total energy consumption of all drones flying to their corresponding landing points is small. The constraint condition of the problem is: each drone cannot collide with each other during the process of flying to its corresponding landing point.

[0050] like Figure 3 As shown, the present invention proposes a method for allocating landing points of a drone cluster, which specifically includes the following steps:

[0051] Step S1: Obtain P landing points in the drone landing area.

[0052] S1 is specifically:

[0053] The landing point is acquired through the navigation system during pre-allocation and by the drone detector during redistribution; the landing points include cargo drone delivery points, autonomous landing drone recovery points and drone charging piles.

[0054] Before the drone takes off, the landing point information obtained by the navigation system is clustered (using the K-Means clustering algorithm) to obtain P landing point coordinates, including the following steps:

[0055] A number of sample points are randomly generated in the drone landing area.

[0056] The coordinate data of the sample points are randomly divided into P groups, and a sample is randomly selected from each group, and the coordinates of the P samples are used as the initial P cluster centers.

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

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

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

[0060] Assigning each sample point to the cluster center closest to it also includes the following steps:

[0061] When assigning sample points, each time a sample point is assigned, the cluster center of the cluster is re-obtained based on the existing sample points in the cluster; the cluster center and the sample points assigned to them represent a cluster.

[0062] Step S2: Identify the P landing point information in the drone landing area. When the number of landing points P is greater than the number of drones N (i.e., P>N), select the N landing points with the highest priority as the drone landing points, and sort the N drones and their corresponding drone landing points in ascending order according to the x-axis coordinate size according to the indicator that the total energy consumption is lower than the threshold; after sorting in ascending order, based on the constraint condition of preventing track intersection, if the x-axis distance between the coordinates of two adjacent drone landing points is less than the threshold, then adjust one of the drone landing points to: when the drone can land in the drone landing area, the y-axis position is greater than the threshold distance from the y-axis position of the adjacent drone. Figure 2 shown.

[0063] S2 is specifically:

[0064] The N landing points with the highest priority among the P sample points obtained by clustering results are selected as the drone landing points. The drone landing points are obtained by the navigation system during pre-allocation and by the drone detector during redistribution.

[0065] Step S3: After adjusting the landing points of all drones, obtain the connection lines between all drones and the drone landing points, check whether all the connection lines intersect, and if they do, exchange the drone landing point of the current drone with the drone with the next serial number to obtain the final drone landing point allocation plan.

[0066] S3 is specifically:

[0067] In order to more accurately ensure that the tracks do not intersect, check whether the corresponding drone-landing point lines intersect. If they do, swap the landing point of the drone with the drone with the next sequence number. Repeat this cycle until there are no intersections between all drone-landing point lines.

[0068] Get the lines connecting all drones and their landing points and check whether all lines are crossed, including the following steps:

[0069] Get the projection coordinates M1(x a ,y a ) and M2(x c ,y c ), and obtain the coordinates of the two ground drone landing points D1(x 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.

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

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

[0072] If M1D1 and M2D2 intersect, 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:

[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)=(yy c )*(x d -x c )-(y d -y c )*(xx c )=0.

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

[0077] g(x a ,y a )*g(x b ,y b )≤0.

[0078] When M1D1 and M2D2 intersect, and M1D1 intersects M2D2, the two lines from the two drones to their respective landing points intersect; x and y are the horizontal and vertical coordinates of the drone's projection coordinates on the ground, respectively. a and a are the horizontal and vertical coordinates of the projection coordinate M1, x c and c are the horizontal and vertical coordinates of the projection coordinate M2, x b and b are the horizontal and vertical coordinates of the projection coordinate D1, x d and d are the horizontal and vertical coordinates of the projection coordinate D2 respectively.

[0079] When M1D1 and M2D2 intersect, and M1D1 and M2D2 intersect, the two lines connecting the two drones to their corresponding landing points intersect, specifically:

[0080] When the coordinates of the two drones and their corresponding drone landing points meet:

[0081]

[0082] Specifically:

[0083]

[0084] That is, when the above conditions are met, the two lines connecting the two drones to the landing points of their corresponding drones intersect.

[0085] Based on the above method, the present invention provides a drone cluster landing point allocation system, including: a data acquisition module, a landing point adjustment module and an allocation plan acquisition module.

[0086] Among them, the data acquisition module is used to obtain P landing points in the drone landing area; the landing point adjustment module is used to select the N landing points with the highest priority as the drone landing points when the number of landing points P is greater than the number of drones N, and sort the drone landing points in ascending order according to the x-axis coordinate size according to the indicator that the total energy consumption is lower than the threshold; after sorting in ascending order, if the x-axis distance between the coordinates of two adjacent drone landing points is less than the threshold, one of the drone landing points is adjusted to: when the drone can land in the drone landing area, the y-axis position is greater than the threshold distance from the y-axis position of the adjacent drone; the allocation plan acquisition module is used to obtain the connection lines between all drones and drone landing points after adjusting all drone landing points, check whether all connection lines intersect, and when they intersect, exchange the drone landing point of the current drone with the drone with the next serial number to obtain the final drone landing point allocation plan.

[0087] The present invention also provides a computer device, including a memory and a processor, wherein a program is stored in the memory, and when the program is executed by the processor, the processor executes the steps of a method for allocating landing points of a drone cluster.

[0088] In accordance with the disclosed embodiments, a computing device may communicate with one or more external devices (e.g., keyboards, pointing devices, Bluetooth communications, etc.), or with any device (e.g., routers, modems, etc.) that enables a computing device to communicate with one or more other computing devices.

[0089] The present invention also provides a storage medium on which a computer program is stored. When the computer program is executed by a processor, a method for allocating landing points of a drone cluster is implemented.

[0090] According to the disclosed embodiments, the storage medium may be a non-volatile computer-readable storage medium, such as but not limited to: a portable computer disk, 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 disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof. In the present invention, the storage medium may be any tangible medium containing or storing a program that may be used by or in conjunction with an instruction execution system, apparatus, or device.

[0091] The various embodiments in this 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 referenced to each other.

[0092] The present invention uses specific examples to illustrate the principles and implementation methods of the present invention. The above examples are only used to help understand the method and core ideas of the present invention. At the same time, for those skilled in the art, according to the ideas of the present invention, there will be changes in the specific implementation methods and application areas. In summary, the content of this specification should not be understood as limiting the present invention.

Claims

1. A method for allocating landing points of drone clusters, characterized in that: include: Get P landing points in the drone landing area; When the number of landing points P is greater than the number of drones N, select the N landing points with the highest priority as the drone landing points, and sort the drone landing points in ascending order according to the x-axis coordinate size according to the indicator that the total energy consumption is lower than the threshold; after sorting in ascending order, if the x-axis distance between the coordinates of two adjacent drone landing points is less than the threshold, adjust one of the drone landing points to: when the drone can land in the drone landing area, the y-axis position of the adjacent drone is greater than the threshold. After adjusting the landing points of all drones, obtain the connection lines between all drones and the drone landing points, check whether all the connection lines intersect, and if they do, exchange the drone landing point of the current drone with the drone with the next serial number to obtain the final drone landing point allocation plan.

2. A method for allocating landing points of drone clusters as claimed in claim 1, characterized in that: The step of obtaining the connection lines between all drones and the drone landing points and checking whether all the connection lines intersect includes the following steps: Get the projection coordinates M1(x a ,y a ) and M2(x c ,y c ), and obtain the coordinates of the two ground drone landing points D1(x 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; Get the M1D1 expression f(x,y) of the line from the first drone to the landing point of its corresponding drone; the specific expression is: f(x,y)=(yy a )*(x b -x a )-(and b -and a )*(xx a )=0; If M1D1 and M2D2 intersect, 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: f(x c ,y c )*f(x d ,y d )≤0; Similarly, we can obtain the expression g(x,y) of M2D2; the specific expression is: g(x,y)=(yy c )*(x d -x c )-(and d -and c )*(xx c )=0; When M1D1 intersects M2D2, the specific expression is: g(x a ,y a )*g(x b ,y b )≤0; When M1D1 and M2D2 intersect, and M1D1 intersects M2D2, the two lines from the two drones to their respective landing points intersect; x and y are the horizontal and vertical coordinates of the drone's projection coordinates on the ground, respectively. a and a are the horizontal and vertical coordinates of the projection coordinate M1, x c and c are the horizontal and vertical coordinates of the projection coordinate M2, x b and b are the horizontal and vertical coordinates of the projection coordinate D1, x d and d are the horizontal and vertical coordinates of the projection coordinate D2 respectively.

3. A method for allocating landing points of drone clusters as claimed in claim 2, characterized in that: When M1D1 and M2D2 intersect, and M1D1 and M2D2 intersect, the two lines connecting the two drones to their respective landing points intersect, specifically: When the coordinates of the two drones and their corresponding drone landing points meet: Specifically: That is, when the above conditions are met, the two lines connecting the two drones to the landing points of their corresponding drones intersect.

4. A method for allocating landing points of drone clusters as claimed in claim 1, characterized in that: The step of obtaining P landing points in the drone landing area includes the following steps: A number of sample points are randomly generated in the drone landing area; The coordinate data of the sample points are randomly divided into P groups, and a sample is randomly selected from each group, and the coordinates of the P samples are used as the initial P cluster centers; Get the distance between each sample point and each cluster center, and assign each sample point to the cluster center closest to it; Traverse the allocation process, record the results of each clustering, and when the maximum number of iterations is reached, obtain the minimum sum of squares of the distances from all sample points to their cluster centers, and select the clustering result with the minimum sum of squares; Select P sample points obtained in the clustering results as selected landing points, and obtain the coordinates of the P landing points.

5. A method for allocating landing points of drone clusters as claimed in claim 4, characterized in that: The step of assigning each sample point to the cluster center closest to it also includes: When assigning sample points, each time a sample point is assigned, the cluster center of the cluster is re-obtained based on the existing sample points in the cluster; the cluster center and the sample points assigned to them represent a cluster.

6. A drone cluster landing point allocation system, characterized in that: include: A data acquisition module is used to acquire P landing points in the drone landing area; The landing point adjustment module is used to select the N landing points with the highest priority as the landing points of the drones when the number of landing points P is greater than the number of drones N, and sort the landing points of the drones in ascending order according to the x-axis coordinate size according to the indicator that the total energy consumption is lower than the threshold; after sorting in ascending order, if the x-axis distance between the coordinates of the landing points of two adjacent drones is less than the threshold, then adjust one of the landing points of the drones to: when the drone can land in the drone landing area, the y-axis position of the drone is greater than the threshold distance from the y-axis position of the adjacent drone; The allocation plan acquisition module is used to obtain the connection lines between all drones and drone landing points after adjusting all drone landing points, check whether all connection lines intersect, and if they do, exchange the drone landing point of the current drone with the drone with the next serial number to obtain the final drone landing point allocation plan.

7. A computer device, characterized in that: It includes a memory and a processor, wherein a program is stored in the memory, and when the program is executed by the processor, the processor executes the steps of a method for allocating landing points of a drone cluster as described in any one of claims 1 to 5.

8. A storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of a method for allocating landing points of a drone cluster as described in any one of claims 1 to 5 are implemented.

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