UAV formation flight test method and device based on GNSS simulator

Abstract

The present application relates to the field of GNSS simulator testing, and discloses a method and device for testing formation flight of unmanned aerial vehicles based on a GNSS simulator, the method comprising: determining the GNSS signal of each unmanned aerial vehicle corresponding to the GNSS simulator according to the flight parameter information of multiple unmanned aerial vehicles; the flight parameter information of multiple unmanned aerial vehicles includes the flight path, flight altitude, flight speed and relative positions between multiple unmanned aerial vehicles of each unmanned aerial vehicle; connecting the GNSS simulator and each unmanned aerial vehicle, time synchronizing the GNSS signals of all unmanned aerial vehicles, and the GNSS simulator sends the GNSS signal to the corresponding unmanned aerial vehicle; multiple unmanned aerial vehicles simulate formation flight according to the GNSS signal corresponding to each unmanned aerial vehicle according to a preset formation control algorithm, and obtain the flight status information of each unmanned aerial vehicle, and analyze and debug the flight status information of each unmanned aerial vehicle. The present application can efficiently and accurately perform flight tests on unmanned aerial vehicle formations.

Description

Technical Field

[0001] The present application relates to the technical field of GNSS simulator testing, and more specifically, to a method and device for testing formation flight of unmanned aerial vehicles based on a GNSS simulator. Background Art

[0002] Unmanned Aerial Vehicle (UAV) is an unmanned aerial vehicle that can be remotely controlled by a wireless device or automatically controlled by an onboard aircraft control device. In the development of modern drone technology, accurate positioning system is one of the key factors to ensure flight safety and successful mission execution. Global Navigation Satellite System (GNSS), such as GPS in the United States, GLONASS in Russia, Galileo in Europe and BeiDou system in China, can provide UAVs with high-precision position, speed and time information, which is essential for autonomous navigation of UAVs. However, in actual flight environments, GNSS signals of UAVs are often affected by various factors, such as signal blocking, multipath effect and signal interference, which may reduce the positioning accuracy of UAVs.

[0003] In order to evaluate and optimize the positioning system of drones in a controlled environment, developers generally use GNSS simulators for testing. GNSS simulators can simulate real satellite signals and then simulate GNSS signals under various environmental conditions, allowing researchers to test the positioning capabilities of drones in different scenarios under laboratory conditions, such as GNSS signals in cities, mountainous areas, forested areas, and open airspace. Such simulation tests not only improve the repeatability and controllability of the tests, but also greatly reduce the risks and costs of actual flight tests. In the prior art, there is no method for testing the positioning function of drone formations using GNSS simulators, which makes it difficult to conduct flight tests on drone formations efficiently and accurately. Summary of the invention

[0004] The purpose of this application is to provide a method and device for UAV formation flight testing based on a GNSS simulator, which solves the technical problem that it is difficult to conduct flight tests on UAV formations efficiently and accurately, and achieves the technical effect of conducting flight tests on UAV formations efficiently and accurately.

[0005] An embodiment of the present application provides a UAV formation flight test method based on a GNSS simulator, the method comprising: determining the GNSS signal of each UAV corresponding to the GNSS simulator according to flight parameter information of multiple UAVs; wherein the flight parameter information of the multiple UAVs includes the flight path, flight altitude, flight speed of each UAV and the relative positions between the multiple UAVs; connecting the GNSS simulator and each UAV, time-synchronizing the GNSS signals of all UAVs, and the GNSS simulator sends the GNSS signal to the corresponding UAV; the multiple UAVs simulate formation flight according to the GNSS signal corresponding to each UAV according to a preset formation control algorithm, and obtain the flight status information of each UAV, and analyze and debug the flight status information of each UAV.

[0006] In one possible implementation, a GNSS signal corresponding to each UAV of the GNSS simulator is determined based on flight parameter information of the multiple UAVs, including: when the number of the multiple UAVs is greater than a preset number, the multiple UAVs are clustered according to flight path similarity and heading proximity of their flight paths to obtain multiple categories each including the multiple UAVs; and the GNSS signal corresponding to the multiple UAVs of each category of the GNSS simulator is determined based on the flight parameter information of the multiple UAVs of each category.

[0007] In another possible implementation, when the number of the multiple drones is greater than a preset number, the multiple drones are clustered according to the flight path similarity and heading proximity of the flight paths of the multiple drones to obtain multiple categories each including the multiple drones, including: when the number of the multiple drones is greater than the preset number, the formation shape characteristics of the multiple drones at multiple times are determined according to the relative positions of the multiple drones, and the formation shape variation degree is determined according to the formation shape characteristics at multiple times; when the formation shape variation degree is greater than the preset formation shape variation degree, the multiple drones are clustered according to the flight path similarity and heading proximity of the flight paths of the multiple drones to obtain multiple categories each including the multiple drones.

[0008] In another possible implementation, the GNSS signal of the multiple UAVs of each category corresponding to the GNSS simulator is determined based on the flight parameter information of the multiple UAVs of each category, including: determining a first formation shape of the multiple UAVs of a first category, and determining the first UAV closest to the center of the first formation shape; determining the first GNSS signal of the multiple UAVs of the first category corresponding to the GNSS simulator based on the flight path, flight altitude and flight speed of the first UAV.

[0009] In another possible implementation, multiple drones simulate formation flight according to a preset formation control algorithm and a GNSS signal corresponding to each drone, and obtain flight status information of each drone, and analyze and debug the flight status information of each drone, including: performing flight status evaluation on multiple drones of each category according to the simulated flight status information of the multiple drones of each category and the flight status information determined according to the formation control algorithm, and calculating an average status evaluation value of the multiple drones of each category as the flight status evaluation value of the multiple drones of each category; when the flight status evaluation value of the multiple drones of a first category is greater than or equal to a preset flight status evaluation value, introducing a first interference test signal into the GNSS signal corresponding to the multiple drones of the first category, obtaining first flight status information of the multiple drones of the first category corresponding to the first interference test signal, and analyzing and debugging the first flight status information of the multiple drones of the first category; when the flight status evaluation value of the multiple drones of the first category is greater than the preset flight status evaluation value, stopping the test of the multiple drones of the first category.

[0010] In another possible implementation, multiple UAVs simulate formation flight according to a preset formation control algorithm and the GNSS signal corresponding to each UAV, obtain flight status information of each UAV, and analyze and debug the flight status information of each UAV, which also includes: determining the flight status evaluation value difference corresponding to the flight status evaluation values ​​of any two UAVs of different categories; when each flight status evaluation value difference is less than or equal to the preset flight status evaluation value difference, continue to test the multiple UAVs of multiple categories; when at least one flight status evaluation value difference is greater than the preset flight status evaluation value difference, stop testing the multiple UAVs of multiple categories.

[0011] In another possible implementation, multiple UAVs simulate formation flight according to a preset formation control algorithm and the GNSS signal corresponding to each UAV, obtain flight status information of each UAV, and analyze and debug the flight status information of each UAV, which also includes: determining the central UAV closest to the center of the formation shape among the multiple UAVs of each category, and determining the two central UAVs with the farthest distance among all the central UAVs, and determining the flight status evaluation values ​​of the multiple UAVs of the two categories corresponding to the two central UAVs, and determining the flight status evaluation value difference of the flight status evaluation values ​​corresponding to the two central UAVs; when the flight status evaluation value difference is less than or equal to the preset flight status evaluation value difference, continue to test the multiple UAVs of the multiple categories; when the flight status evaluation value difference is greater than the preset flight status evaluation value difference, stop testing the multiple UAVs of the multiple categories.

[0012] In another possible implementation, multiple UAVs simulate formation flight according to a preset formation control algorithm and the GNSS signal corresponding to each UAV, obtain flight status information of each UAV, and analyze and debug the flight status information of each UAV, which also includes: determining multiple target center UAVs located at corner points of multiple UAVs of multiple categories among all center UAVs, and determining target flight state evaluation values ​​of multiple UAVs of categories corresponding to any two target center UAVs, and determining the difference in flight state evaluation values ​​corresponding to any two target center UAVs; when each flight state evaluation value difference is less than or equal to a preset flight state evaluation value difference, continue to test multiple UAVs of multiple categories; when at least one flight state evaluation value difference is greater than the preset flight state evaluation value difference, stop testing multiple UAVs of multiple categories.

[0013] An embodiment of the present application also provides a UAV formation flight test device based on a GNSS simulator, comprising a unit for executing any of the methods described above.

[0014] An embodiment of the present application also provides a UAV formation flight test device based on a GNSS simulator, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements any of the methods described above when executing the computer program.

[0015] An embodiment of the present application further provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the method described in any one of the above items is implemented.

[0016] An embodiment of the present application also provides a computer program product, including a computer program, which implements the steps of any of the methods described above when executed by a processor.

[0017] Compared with the prior art, the embodiments of the present invention have the following beneficial effects:

[0018] The embodiment of the present application provides a method for testing formation flight of unmanned aerial vehicles based on a GNSS simulator, and the method includes: determining the GNSS signal corresponding to each unmanned aerial vehicle of the GNSS simulator according to the flight parameter information of multiple unmanned aerial vehicles; wherein the flight parameter information of multiple unmanned aerial vehicles includes the flight path, flight altitude, flight speed and relative position between multiple unmanned aerial vehicles of each unmanned aerial vehicle; connecting the GNSS simulator and each unmanned aerial vehicle, time synchronizing the GNSS signals of all unmanned aerial vehicles, and the GNSS simulator sends the GNSS signal to the corresponding unmanned aerial vehicle; multiple unmanned aerial vehicles simulate formation flight according to the GNSS signal corresponding to each unmanned aerial vehicle according to a preset formation control algorithm, and obtain the flight status information of each unmanned aerial vehicle, and analyze and debug the flight status information of each unmanned aerial vehicle. The embodiment of the present application determines the GNSS signal corresponding to each unmanned aerial vehicle according to the flight parameters of multiple unmanned aerial vehicles, and each unmanned aerial vehicle performs a formation control algorithm to perform formation control flight test of the unmanned aerial vehicle, thereby realizing efficient and accurate testing of the formation flight of unmanned aerial vehicles. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0020] Figure 1 A schematic diagram of a flow chart of a UAV formation flight test method based on a GNSS simulator provided in an embodiment of the present application;

[0021] Figure 2 A schematic diagram of the working process of a UAV formation flight test method based on a GNSS simulator provided in an embodiment of the present application;

[0022] Figure 3 A schematic diagram of a process of clustering multiple drones in a drone formation flight test method based on a GNSS simulator provided in an embodiment of the present application;

[0023] Figure 4 A schematic diagram of a process for calculating a formation shape in a UAV formation flight test method based on a GNSS simulator provided in an embodiment of the present application;

[0024] Figure 5 A schematic diagram of the logical structure of a UAV formation flight test device based on a GNSS simulator provided in an embodiment of the present application;

[0025] Figure 6A schematic diagram of the physical structure of a UAV formation flight test device based on a GNSS simulator provided in an embodiment of the present application. DETAILED DESCRIPTION

[0026] It should be understood that when used in the present specification and the appended claims, the term "comprising" indicates the presence of described features, wholes, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, wholes, steps, operations, elements, components and / or combinations thereof.

[0027] It should also be understood that the term “and / or” used in the specification and appended claims refers to any and all possible combinations of one or more of the associated listed items, and includes these combinations.

[0028] As used in the specification and appended claims of this application, the term "if" can be interpreted as "when" or "uponce" or "in response to determining" or "in response to detecting", depending on the context. Similarly, the phrase "if it is determined" or "if [described condition or event] is detected" can be interpreted as meaning "uponce it is determined" or "in response to determining" or "uponce [described condition or event] is detected" or "in response to detecting [described condition or event]", depending on the context.

[0029] In addition, in the description of the present application specification and the appended claims, the terms "first", "second", "third", etc. are only used to distinguish the descriptions and cannot be understood as indicating or implying relative importance.

[0030] References to "one embodiment" or "some embodiments" etc. described in the specification of this application mean that one or more embodiments of the present application include specific features, structures or characteristics described in conjunction with the embodiment. Therefore, the statements "in one embodiment", "in some embodiments", "in some other embodiments", "in some other embodiments", etc. that appear in different places in this specification do not necessarily refer to the same embodiment, but mean "one or more but not all embodiments", unless otherwise specifically emphasized in other ways. The terms "including", "comprising", "having" and their variations all mean "including but not limited to", unless otherwise specifically emphasized in other ways.

[0031] In the prior art, there is no method for testing the positioning function of a UAV formation using a GNSS simulator, which makes it difficult to conduct flight tests on a UAV formation efficiently and accurately.

[0032] Based on the above reasons, an embodiment of the present application provides a method for testing formation flight of unmanned aerial vehicles based on a GNSS simulator, and the method includes: determining the GNSS signal corresponding to each unmanned aerial vehicle of the GNSS simulator according to the flight parameter information of multiple unmanned aerial vehicles; wherein the flight parameter information of multiple unmanned aerial vehicles includes the flight path, flight altitude, flight speed and relative position between multiple unmanned aerial vehicles of each unmanned aerial vehicle; connecting the GNSS simulator and each unmanned aerial vehicle, time synchronizing the GNSS signals of all unmanned aerial vehicles, and the GNSS simulator sends the GNSS signal to the corresponding unmanned aerial vehicle; multiple unmanned aerial vehicles simulate formation flight according to the GNSS signal corresponding to each unmanned aerial vehicle according to a preset formation control algorithm, and obtain the flight status information of each unmanned aerial vehicle, and analyze and debug the flight status information of each unmanned aerial vehicle. The embodiment of the present application determines the GNSS signal corresponding to each unmanned aerial vehicle according to the flight parameters of multiple unmanned aerial vehicles, and each unmanned aerial vehicle performs a formation control algorithm to perform formation control flight test of the unmanned aerial vehicle, thereby realizing efficient and accurate testing of the formation flight of unmanned aerial vehicles.

[0033] In some scenarios, a UAV formation flight test method based on a GNSS simulator of an embodiment of the present application can be applied to UAV formation flight tests based on a GNSS simulator, which can improve the test efficiency and test accuracy of formation flights of a large number of UAVs.

[0034] The following is a detailed description of a UAV formation flight test method based on a GNSS simulator provided in an embodiment of the present application with reference to specific examples.

[0035] Figure 1 A flow chart of a UAV formation flight test method based on a GNSS simulator provided in an embodiment of the present application is shown in FIG. Figure 1 As shown, the method includes S110 to S130, and S110 to S130 are described in detail below.

[0036] S110: Determine the GNSS signal of each drone corresponding to the GNSS simulator according to the flight parameter information of the multiple drones, wherein the flight parameter information of the multiple drones includes the flight path, flight altitude, flight speed of each drone and the relative positions between the multiple drones.

[0037] When performing a flight test on a drone, it is necessary to send a corresponding GNSS signal to the drone to implement the flight test on the drone. In the embodiment of the present application, the GNSS signal corresponding to each drone of the GNSS simulator can be determined according to the flight parameter information of multiple drones, so that each drone can perform a formation flight test according to the GNSS signal.

[0038] Exemplarily, when determining the GNSS signal corresponding to each drone based on the flight parameter information of multiple drones, the flight parameter information of the multiple drones may include the flight path, flight altitude, flight speed of each drone and the relative positions between the multiple drones.

[0039] Exemplarily, the flight path, flight altitude, flight speed of each drone and the relative positions between multiple drones can be obtained through preset drone formation control information.

[0040] S120, connecting the GNSS simulator and each UAV, performing time synchronization on the GNSS signals of all the UAVs, and the GNSS simulator sends the GNSS signal to the corresponding UAV.

[0041] Figure 2 A schematic diagram of the working process of a UAV formation flight test method based on a GNSS simulator provided in an embodiment of the present application is shown in FIG. Figure 2 As shown, when conducting a formation flight test on UAVs, the GNSS simulator and each UAV can be connected, and the GNSS signals of all UAVs can be time synchronized. The GNSS simulator sends the GNSS signal to the corresponding UAV to realize a formation flight test on multiple UAVs.

[0042] Exemplarily, when the GNSS simulator sends the GNSS signal to the corresponding UAV, the GNSS signal may be sent to the corresponding UAV via an antenna, or the GNSS signal may be sent to the corresponding UAV via an RF Cable.

[0043] S130. The multiple UAVs simulate formation flight according to a preset formation control algorithm and a GNSS signal corresponding to each UAV, obtain flight status information of each UAV, and analyze and debug the flight status information of each UAV.

[0044] When conducting a UAV formation flight test, after sending the GNSS signal to the corresponding UAVs, multiple UAVs can be controlled to start simulated flight, so that multiple UAVs can simulate their flight status according to the GNSS signal respectively, thereby realizing the flight status test of the UAV formation.

[0045] When working, multiple drones can be controlled according to the preset formation control algorithm and simulated formation flight based on the GNSS signal corresponding to each drone. During the simulated flight of the drones, the flight status information of each drone can be obtained, and the flight status information of each drone can be analyzed and debugged to achieve control of the flight formation of multiple drones.

[0046] Exemplarily, when obtaining the flight status information of each drone, the flight status information of each drone can be obtained by connecting the serial port to Ethernet.

[0047] The beneficial effect brought about by the above-mentioned implementation method is that the GNSS signal corresponding to each drone is determined according to the flight parameter information of multiple drones. After the GNSS signals of all drones are time synchronized, the GNSS simulator sends the GNSS signal to the corresponding drone, and simulates formation flight according to the GNSS signal corresponding to each drone, thereby testing the effect of formation flight of multiple drones and improving the efficiency and accuracy of the formation flight test of multiple drones.

[0048] In some implementations, in the above S110, the GNSS simulator determines the GNSS signal corresponding to each UAV based on the flight parameter information of the multiple UAVs, including S111 to S112, and S111 to S112 are described in detail below.

[0049] S111. When the number of the multiple drones is greater than a preset number, the multiple drones are clustered according to flight path similarity and heading proximity of the flight paths of the multiple drones to obtain multiple categories each including the multiple drones.

[0050] The signal simulation capability of a GNSS simulator mainly depends on the hardware configuration and software design of the GNSS simulator. Existing GNSS simulators can simulate signals from multiple satellite systems, including GPS, GLONASS, Galileo, BeiDou and other satellite signals, and can support the simulation of multiple terminals at the same time.

[0051] When simulating formation flight of drones and determining the GNSS signals corresponding to multiple drones, when the number of drones is greater than the preset number, the system load of the GNSS simulator may be too large and the maximum limit of the communication channel of the GNSS signal between the GNSS simulator and the drones may be reached. Therefore, the number of GNSS signals determined by the GNSS simulator for multiple drones can be appropriately reduced.

[0052] Exemplarily, the preset number may be 30, 40 or 50 aircraft.

[0053] When reducing the number of GNSS signals determined by the GNSS simulator for multiple UAVs, the multiple UAVs can be clustered according to the flight path similarity and heading proximity of their flight paths to obtain multiple categories including multiple UAVs. The multiple UAVs corresponding to each category have similarities in flight paths and headings, and then the multiple UAVs corresponding to each category can be controlled to use the same GNSS signal, thereby achieving the purpose of reducing the number of GNSS signals.

[0054] Figure 3 A schematic diagram of a process of clustering multiple drones in a drone formation flight test method based on a GNSS simulator provided in an embodiment of the present application, such as Figure 3 As shown, by clustering multiple drones, multiple categories of drones surrounded by multiple dotted lines can be obtained, and each category includes multiple drones.

[0055] Exemplarily, when clustering multiple drones according to the flight path similarity and heading proximity of their flight paths, different weights can be assigned to the flight path similarity and heading proximity, and then the flight paths of the multiple drones can be clustered according to the different weights of the flight path similarity and heading proximity.

[0056] Exemplarily, when clustering flight paths according to flight path similarity, the flight paths of multiple drones can be clustered based on the position coordinates of the multiple drones in the flight paths at each moment, and then the position distances of the multiple drones in the flight paths at each moment are calculated through the position coordinates of the drones.

[0057] Exemplarily, when the flight paths are clustered according to flight path similarity, the flight paths of the multiple drones can also be clustered by determining the change values ​​of the time series distances of the multiple drones in the flight paths according to their position coordinates in the flight paths.

[0058] S112, determining the GNSS signals of the multiple UAVs of each category corresponding to the GNSS simulator according to the flight parameter information of the multiple UAVs of each category.

[0059] After determining the multiple drones corresponding to the multiple categories, the GNSS simulator can determine the GNSS signals of the multiple drones in each category according to the flight parameter information of the multiple drones in each category, and then test the multiple drones in each category sharing one GNSS signal.

[0060] For example, when testing that multiple drones of each category share one GNSS signal, GNSS signal transmission can be performed by having multiple drones of each category share one radio frequency line.

[0061] like Figure 3 As shown, a GNSS signal corresponding to each category can be sent to multiple drones of each category.

[0062] The beneficial effect brought about by the above-mentioned implementation method is that when the number of multiple drones is greater than the preset number, causing the system load of the GNSS simulator to be too large, or when the maximum limit of the communication channel of the GNSS signal between the GNSS simulator and the drones is reached, the multiple drones are divided into multiple categories, and multiple drones of each category share one GNSS signal to conduct drone formation flight tests, thereby reducing the system load of the GNSS simulator and being able to meet the limitations of the communication channel of the GNSS signal between the GNSS simulator and the drones.

[0063] The beneficial effect brought about by the above-mentioned implementation method is that the multiple drones corresponding to each category have similar flight paths and headings, and the multiple drones corresponding to each category use the same GNSS signal, thereby achieving the effect of reducing the number of GNSS signals.

[0064] In some implementations, in the above-mentioned S111, when the number of the multiple drones is greater than a preset number, the multiple drones are clustered according to the flight path similarity and heading proximity of the flight paths of the multiple drones to obtain multiple categories each including the multiple drones, including S111a to S111b. S111a to S111b are described in detail below.

[0065] S111a. When the number of the multiple drones is greater than a preset number, determine the formation shape characteristics of the multiple drones at multiple times according to the relative positions between the multiple drones, and determine the formation shape change degree according to the formation shape characteristics at multiple times.

[0066] When the number of the multiple drones is greater than the preset number, it indicates that the multiple drones need to be clustered and then GNSS signals need to be simulated. When the multiple drones are clustered into multiple categories of drones, the multiple drones can be dynamically clustered according to their positions to improve the accuracy of simulating the GNSS signals of the multiple drones.

[0067] When multiple drones are dynamically clustered according to their positions, the formation shape characteristics of the multiple drones at multiple times can be determined according to the relative positions of the multiple drones, and the formation shape change degree can be determined according to the formation shape characteristics at multiple times, and then the timing for clustering the multiple drones can be determined according to the formation shape change degree.

[0068] Exemplarily, the formation shape variability can be determined by a corner point recognition algorithm to determine the key corner points of the formation shape of multiple UAVs, and the length and azimuth of the line connecting all the key corner points can be used as the formation shape features of the multiple UAVs. The formation shape features can be represented by a matrix, and the difference of the matrix representing the formation shape features is used as the formation shape variability.

[0069] Figure 4 A schematic diagram of the process of calculating the formation shape in a UAV formation flight test method based on a GNSS simulator provided in an embodiment of the present application, such as Figure 4 As shown, the key corner points A1, A2, A3...An of the formation shape of the multiple UAVs can be determined, and the length and azimuth of the line connecting all the key corner points A1, A2, A3...An can be used as the formation shape features of the multiple UAVs.

[0070] For example, when determining the formation shape feature, the relative distance between each pair of drones can be calculated. Assuming there are N drones, an N×N distance matrix can be constructed, where each element dij represents the distance between drone i and drone j. A shape feature descriptor can be constructed through the distance matrix. The shape feature descriptor can be a convex hull. By calculating the convex hull of the drone formation, the vertices and area of ​​the convex hull are extracted as the formation shape feature.

[0071] S111b. When the formation shape variation degree is greater than a preset formation shape variation degree, clustering the multiple drones according to flight path similarity and heading proximity of the flight paths of the multiple drones to obtain multiple categories each including the multiple drones.

[0072] When simulating the GNSS signals of multiple UAVs, when the formation shape change degree is greater than the preset formation shape change degree, it means that the formation shape of the multiple UAVs has changed significantly, that is, the relative positions between the multiple UAVs have changed significantly, and the multiple UAVs can be reclassified into multiple categories.

[0073] When dividing multiple drones, the multiple drones can be clustered according to the flight path similarity and heading proximity of the flight paths of the multiple drones to obtain multiple categories each including the multiple drones. The GNSS signals of the multiple drones corresponding to each category of the GNSS simulator can be further determined according to the method in S112.

[0074] The beneficial effect brought about by the above-mentioned implementation method is that when the formation shape change degree is greater than the preset formation shape change degree, multiple drones are re-determined as drones of multiple categories, and the GNSS signals corresponding to the multiple drones can be adjusted in time, thereby improving the accuracy and reliability of GNSS signal simulation for multiple drones.

[0075] In some implementations, in the above S112, the GNSS simulator determines the GNSS signals of the multiple drones of each category according to the flight parameter information of the multiple drones of each category, including S112a to S112b. S112a to S112b are described in detail below.

[0076] S112a, determining a first formation shape of multiple drones of the first category, and determining a first drone that is closest to the center of the first formation shape.

[0077] like Figure 4 As shown, the multiple drones of the first category correspond to the multiple drones in the dotted box. When determining the multiple drones of the first category, the first formation shape of the multiple drones of the first category can be determined, and the first drone closest to the center of the first formation shape can be determined.

[0078] like Figure 4 As shown, multiple drones of the first category may be connected as a first formation shape. After the first formation shape is obtained, a first drone K1 closest to the center of the first formation shape may be determined.

[0079] S112b, determining, according to the flight path, flight altitude, and flight speed of the first unmanned aerial vehicle, that the GNSS simulator corresponds to first GNSS signals of multiple unmanned aerial vehicles of the first category.

[0080] When determining the GNSS signals for multiple UAVs of the first category, since the distance between the multiple UAVs of the first category is short and they have similar flight trajectories, the GNSS simulator can determine the first GNSS signal of the multiple UAVs of the first category according to the flight path, flight altitude and flight speed of the first UAV, and the first GNSS signal can simulate the GNSS signals of the multiple UAVs of the first category.

[0081] Exemplarily, after obtaining the first GNSS signal, the first GNSS signal may be sent to multiple drones corresponding to the first category.

[0082] The beneficial effect brought about by the above-mentioned implementation method is that the first formation shape of multiple UAVs of the first category is determined, and the first UAV closest to the center of the first formation shape is determined, and the corresponding first GNSS signal is determined according to the flight status of the first UAV, thereby realizing the simulation of GNSS signals of multiple UAVs of the first category and improving the accuracy of GNSS signal simulation.

[0083] In some implementations, in the above-mentioned S130, multiple UAVs simulate formation flight according to a preset formation control algorithm based on the GNSS signal corresponding to each UAV, obtain flight status information of each UAV, and analyze and debug the flight status information of each UAV, including S131 to S132. S131 to S132 are described in detail below.

[0084] S131. Perform flight status evaluation on the multiple drones of each category based on the simulated flight status information of the multiple drones of each category and the flight status information determined according to the formation control algorithm, and calculate the average status evaluation value of the multiple drones of each category as the flight status evaluation value of the multiple drones of each category.

[0085] When testing multiple drones of each category, the flight status of the multiple drones of each category can be evaluated based on the flight status information of the multiple drones of each category to obtain flight status evaluation values ​​of the multiple drones of each category. Then, the testing process of the multiple drones of each category can be adjusted based on the monitoring of the simulated flight status of the multiple drones of each category.

[0086] When evaluating the flight status, the flight status of multiple UAVs of each category can be evaluated based on their simulated flight status information and the flight status information determined according to the formation control algorithm. When evaluating a single UAV, the position error value between the simulated actual flight position of the single UAV at different times and the target flight position determined according to the formation control algorithm can be determined. Then, the reciprocal of the average value of the position error values ​​of the single UAV at different times can be calculated as the status evaluation value of the single UAV.

[0087] After obtaining the status evaluation value of a single UAV, the average status evaluation value of multiple UAVs of each category can be calculated to obtain the average status evaluation value corresponding to the multiple UAVs of each category as the flight status evaluation value of the multiple UAVs of each category.

[0088] Exemplarily, the flight status evaluation value of multiple drones of the first category may be 0.112m-1.

[0089] S132: When the flight status evaluation value of the plurality of UAVs of the first category is greater than or equal to the preset flight status evaluation value, a first interference test signal is introduced into the GNSS signals corresponding to the plurality of UAVs of the first category, and first flight status information of the plurality of UAVs of the first category corresponding to the first interference test signal is obtained, and the first flight status information of the plurality of UAVs of the first category is analyzed and debugged. When the flight status evaluation value of the plurality of UAVs of the first category is greater than the preset flight status evaluation value, the test of the plurality of UAVs of the first category is stopped.

[0090] After obtaining the flight status evaluation values ​​of multiple drones in each category, when the flight status evaluation values ​​of multiple drones in the first category are greater than or equal to the preset flight status evaluation values, it means that the flight status of the multiple drones in the first category is better, and the multiple drones in the first category can continue to be tested.

[0091] When continuing to test the multiple drones of the first category, a first interference test signal can be introduced into the GNSS signals corresponding to the multiple drones of the first category, and the first flight status information of the multiple drones of the first category corresponding to the first interference test signal can be obtained, and the first flight status information of the multiple drones of the first category can be analyzed and debugged to realize flight testing of the multiple drones of the first category under the first interference test signal.

[0092] Exemplarily, the first interference test signal may be an interference test signal caused by shielding, multipath effects, and the like.

[0093] When the flight status evaluation value of the multiple drones of the first category is greater than the preset flight status evaluation value, it means that the flight status of the multiple drones of the first category is not good, and the test of the multiple drones of the first category can be stopped to stop further testing of the multiple drones of the first category.

[0094] The beneficial effect brought about by the above-mentioned implementation method is that the flight status of each category of drones can be evaluated during the test process, avoiding stopping the test when a large error occurs in the flight status of the first category of drones. At the same time, interference test conditions can continue to be introduced for testing of multiple drones of the first category with smaller errors, thereby improving the intelligence of the test process.

[0095] The beneficial effect brought about by the above implementation method is that the test is stopped when a large error occurs in the flight state of the first category of UAVs, which can save the computing resources of the GNSS simulator and realize the intelligent control of the test process.

[0096] In some implementations, in the above-mentioned S130, multiple UAVs simulate formation flight according to a preset formation control algorithm based on the GNSS signal corresponding to each UAV, obtain flight status information of each UAV, and analyze and debug the flight status information of each UAV, which also includes S133 to S134. S133 to S134 are described in detail below.

[0097] S133: Determine a flight status evaluation value difference corresponding to the flight status evaluation values ​​of any two different categories of multiple UAVs.

[0098] After obtaining the flight status evaluation values ​​of multiple drones in each category, the flight status evaluation value difference corresponding to the flight status evaluation values ​​of multiple drones in any two different categories can be determined, and then the flight status of all drones can be evaluated according to the flight status evaluation value difference between multiple drones in any two different categories.

[0099] S134, when each flight state evaluation value difference is less than or equal to the preset flight state evaluation value difference, continue to test the multiple drones of multiple categories. When at least one flight state evaluation value difference is greater than the preset flight state evaluation value difference, stop testing the multiple drones of multiple categories.

[0100] After obtaining the difference in flight status evaluation values ​​of any two drones of different categories, when each flight status evaluation value difference is less than or equal to the preset flight status evaluation value difference, it means that the difference in flight status evaluation values ​​of any two drones of different categories is small, that is, the overall flight status of all drones is relatively stable, and then multiple drones of multiple categories can continue to be tested.

[0101] When at least one flight status evaluation value difference is greater than the preset flight status evaluation value difference, it means that the flight status evaluation values ​​of any two drones of different categories are quite different, that is, the overall flight status stability of all drones is not good, and the testing of multiple drones of multiple categories can be stopped.

[0102] The beneficial effect brought about by the above-mentioned implementation method is that by calculating the difference in flight status evaluation values ​​of any two drones of different categories, the overall flight status of all drones can be evaluated. When the overall flight status of all drones is relatively stable, the test continues. When the overall flight status of all drones is not stable enough, the test is stopped, thereby realizing the adjustment of the test process according to the overall flight status of all drones.

[0103] In some implementations, in the above-mentioned S130, multiple UAVs simulate formation flight according to a preset formation control algorithm based on the GNSS signal corresponding to each UAV, obtain flight status information of each UAV, and analyze and debug the flight status information of each UAV, which also includes S135 to S136. S135 to S136 are described in detail below.

[0104] S135. Determine the central drone closest to the center of the formation shape among the multiple drones of each category, and determine the two central drones that are farthest away among all the central drones, and determine the flight status evaluation values ​​of the multiple drones of the two categories corresponding to the two central drones, and determine the flight status evaluation value difference between the flight status evaluation values ​​corresponding to the two central drones.

[0105] When controlling the test state, the formation shape of multiple drones of each category can be determined first, and then the center of the formation shape of multiple drones of each category can be determined, and the central drone closest to the center of the formation shape among the multiple drones of each category can be determined, and then the flight status of all drones can be evaluated based on the central drone of each category.

[0106] When the flight status of all the drones is evaluated, the two center drones with the farthest distance among all the center drones can be determined, and the two center drones with the farthest distance represent the positions of multiple drones of the two farthest categories in the formation shape of all the drones.

[0107] After obtaining the two center drones with the farthest distance among all center drones, the flight status evaluation values ​​of multiple drones of two categories corresponding to the two center drones can be determined, and the flight status evaluation value difference of the flight status evaluation values ​​corresponding to the two center drones can be determined. The flight status evaluation value difference represents the degree of difference in the flight status evaluation values ​​of multiple drones of the two categories with the farthest distance.

[0108] S136: When the flight state evaluation value difference is less than or equal to the preset flight state evaluation value difference, continue to test the multiple drones of multiple categories. When the flight state evaluation value difference is greater than the preset flight state evaluation value difference, stop testing the multiple drones of multiple categories.

[0109] When conducting a flight status assessment, when the flight status assessment value difference is less than or equal to the preset flight status assessment value difference, it means that the difference in the flight status assessment values ​​of multiple drones in the two categories with the farthest distance is small, indicating that the flight status of all drones is relatively stable, and thus multiple drones of multiple categories can continue to be tested.

[0110] When conducting a flight status assessment, when the flight status assessment value difference is greater than a preset flight status assessment value difference, it indicates that the flight status assessment values ​​of multiple drones in the two categories with the farthest distances are quite different, indicating that the flight status of all drones is not stable enough, and thus the testing of multiple drones in multiple categories can be stopped.

[0111] The beneficial effect brought about by the above implementation method is that by calculating the difference in the flight status evaluation values ​​of multiple drones of the two categories with the farthest distances, the flight status of all drones is evaluated, thereby achieving accurate evaluation of the flight status of all drones and realizing intelligent control of the test process of all drones.

[0112] In some implementations, in the above-mentioned S130, multiple UAVs simulate formation flight according to a preset formation control algorithm based on the GNSS signal corresponding to each UAV, obtain flight status information of each UAV, and analyze and debug the flight status information of each UAV, which also includes S137 to S138. S137 to S138 are described in detail below.

[0113] S137. Determine multiple target center drones located at corner points of multiple drones of multiple categories among all center drones, determine target flight state evaluation values ​​of multiple drones of categories corresponding to any two target center drones, and determine the difference in flight state evaluation values ​​corresponding to any two target center drones.

[0114] When evaluating the flight status of all drones, multiple target center drones located at corner points of multiple drones of multiple categories among all center drones can be determined, and the multiple target center drones represent the positions of multiple drones of categories located at the corner points of all drones.

[0115] After obtaining multiple target center UAVs, the target flight state evaluation values ​​of multiple UAVs of the category corresponding to any two target center UAVs can be determined, and the flight state evaluation value difference corresponding to any two target center UAVs can be determined. The flight state evaluation value difference represents the difference in flight states of multiple UAVs of different categories located at the corner points.

[0116] S138, when each flight state evaluation value difference is less than or equal to the preset flight state evaluation value difference, continue to test the multiple drones of multiple categories. When at least one flight state evaluation value difference is greater than the preset flight state evaluation value difference, stop testing the multiple drones of multiple categories.

[0117] When evaluating the flight status of all drones, when the difference in each flight status evaluation value is less than or equal to the preset flight status evaluation value difference, it means that the difference in flight status of multiple drones of different categories located at the corner point is small, and multiple drones of multiple categories can continue to be tested.

[0118] When evaluating the flight status of all drones, if at least one flight status evaluation value difference is greater than the preset flight status evaluation value difference, it means that the difference in flight status of multiple drones of different categories located at the corner point is large, and the testing of multiple drones of multiple categories is stopped.

[0119] The beneficial effect brought about by the above-mentioned implementation method is that by calculating the difference in the flight status of multiple drones of different categories located at the corner points, the flight status of all drones is evaluated, thereby achieving an overall evaluation of the flight status of all drones, improving the accuracy of controlling the test process, and improving the control effect of the test process.

[0120] An embodiment of the present application also provides a UAV formation flight test device based on a GNSS simulator, comprising a unit for executing any of the methods described above.

[0121] Figure 5 A schematic diagram of the logical structure of a UAV formation flight test device based on a GNSS simulator is provided in one embodiment of the present application, such as Figure 5 As shown, the device 1 of this embodiment includes a processing unit 11, a storage unit 12 and a transceiver unit 13. The processing unit 11 is used to process data, the storage unit 12 is used to store data, and the transceiver unit 13 is used to send and receive data. The processing unit 11, the storage unit 12 and the transceiver unit 13 cooperate with each other to implement the above method. The beneficial effects brought about by the embodiment of the present application have been described in the above method and will not be repeated here.

[0122] An embodiment of the present application also provides a UAV formation flight test device based on a GNSS simulator, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements any of the methods described above when executing the computer program.

[0123] Figure 6 A schematic diagram of the physical structure of a UAV formation flight test device based on a GNSS simulator is provided in one embodiment of the present application, such as Figure 6 As shown, the device 2 of this embodiment includes: at least one processor 20 ( Figure 6 Only one processor 20 is shown in the figure), a memory 21, and a computer program 22 stored in the memory 21 and executable on the at least one processor 20, and the processor 20 implements the steps in any of the above-mentioned method embodiments when executing the computer program 22. The beneficial effects brought about by the embodiments of the present application have been described in the above-mentioned methods and will not be repeated here.

[0124] It should be noted that the information interaction, execution process, etc. between the above-mentioned devices / units are based on the same concept as the method embodiment of the present application. Their specific functions and technical effects can be found in the method embodiment part and will not be repeated here.

[0125] The technicians in the relevant field can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional units and modules is used as an example for illustration. In practical applications, the above-mentioned function allocation can be completed by different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiment can be integrated in a processing unit, or each unit can exist physically separately, or two or more units can be integrated in one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of software functional units. In addition, the specific names of the functional units and modules are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of this application. The specific working process of the units and modules in the above-mentioned system can refer to the corresponding process in the aforementioned method embodiment, which will not be repeated here.

[0126] An embodiment of the present application further provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps in the above-mentioned method embodiments can be implemented.

[0127] An embodiment of the present application provides a computer program product. When the computer program product runs on a mobile terminal, the mobile terminal can implement the steps in the above-mentioned method embodiments when executing the computer program product.

[0128] If the integrated unit 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 application implements all or part of the processes in the above-mentioned embodiment method, which can 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 can at least include: any entity or device that can carry the computer program code to the camera / terminal device, recording medium, computer memory, read-only memory (ROM, Read-Only Memory), random access memory (RAM, RandomAccess Memory), electric carrier signal, telecommunication signal and software distribution medium. For example, a USB flash drive, a mobile hard disk, a magnetic disk or an optical disk. In some jurisdictions, according to legislation and patent practice, computer-readable media cannot be electric carrier signals and telecommunication signals.

[0129] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0130] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0131] In the embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the modules or units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0132] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0133] The embodiments described above are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, a person skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. Such modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application, and should all be included in the protection scope of the present application.

Claims

1. A UAV formation flight test method based on a GNSS simulator, characterized in that: The method comprises: Determine the GNSS signal of each drone corresponding to the GNSS simulator according to the flight parameter information of the multiple drones; wherein the flight parameter information of the multiple drones includes the flight path, flight altitude, flight speed of each drone and the relative positions between the multiple drones; Connect the GNSS simulator to each drone, synchronize the GNSS signals of all drones, and the GNSS simulator sends the GNSS signals to the corresponding drones; Multiple drones simulate formation flight according to the preset formation control algorithm and the GNSS signals corresponding to each drone, obtain the flight status information of each drone, and analyze and debug the flight status information of each drone; According to the flight parameter information of multiple drones, determine the GNSS signal of each drone corresponding to the GNSS simulator, including: When the number of the multiple drones is greater than a preset number, the multiple drones are clustered according to flight path similarity and heading proximity of the flight paths of the multiple drones to obtain multiple categories including the multiple drones respectively; According to the flight parameter information of the multiple UAVs of each category, the GNSS signals of the multiple UAVs of each category corresponding to the GNSS simulator are determined.

2. The method according to claim 1, characterized in that When the number of the multiple drones is greater than a preset number, the multiple drones are clustered according to the flight path similarity and heading similarity of the flight paths of the multiple drones to obtain multiple categories including the multiple drones, including: When the number of the multiple drones is greater than a preset number, determining the formation shape features of the multiple drones at multiple times according to the relative positions between the multiple drones, and determining the formation shape change degree according to the formation shape features at multiple times; When the formation shape variation is greater than a preset formation shape variation, the multiple drones are clustered according to flight path similarity and heading proximity of the flight paths of the multiple drones to obtain multiple categories each including the multiple drones.

3. The method according to claim 2, characterized in that According to the flight parameter information of the multiple drones of each category, the GNSS signal of the multiple drones of each category corresponding to the GNSS simulator is determined, including: Determine a first formation shape of multiple drones of a first category, and determine a first drone that is closest to a center of the first formation shape; According to the flight path, flight altitude and flight speed of the first unmanned aerial vehicle, the GNSS simulator determines first GNSS signals of multiple unmanned aerial vehicles of the first category.

4. The method according to claim 3, characterized in that Multiple drones simulate formation flight according to the preset formation control algorithm and the GNSS signals corresponding to each drone, obtain the flight status information of each drone, and analyze and debug the flight status information of each drone, including: According to the simulated flight state information of the multiple UAVs of each category and the flight state information determined according to the formation control algorithm, the multiple UAVs of each category are evaluated for flight state, and an average state evaluation value of the multiple UAVs of each category is calculated as the flight state evaluation value of the multiple UAVs of each category; When the flight status evaluation values ​​of the multiple drones of the first category are greater than or equal to the preset flight status evaluation values, a first interference test signal is introduced into the GNSS signals corresponding to the multiple drones of the first category, and the first flight status information of the multiple drones of the first category corresponding to the first interference test signal is obtained, and the first flight status information of the multiple drones of the first category is analyzed and debugged; when the flight status evaluation values ​​of the multiple drones of the first category are greater than the preset flight status evaluation values, the test of the multiple drones of the first category is stopped.

5. The method according to claim 4, characterized in that Multiple drones simulate formation flight according to the preset formation control algorithm and the GNSS signals corresponding to each drone, obtain the flight status information of each drone, and analyze and debug the flight status information of each drone, which also includes: Determine the flight status evaluation value difference corresponding to the flight status evaluation values ​​of any two different categories of multiple UAVs; When each flight state evaluation value difference is less than or equal to the preset flight state evaluation value difference, continue to test multiple drones of multiple categories; when at least one flight state evaluation value difference is greater than the preset flight state evaluation value difference, stop testing multiple drones of multiple categories.

6. A UAV formation flight test device based on a GNSS simulator, characterized in that: The method comprises means for performing the method according to any one of claims 1 to 5.

7. A UAV formation flight test device based on a GNSS simulator, 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 method according to any one of claims 1 to 5 is implemented.

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

9. A computer program product, comprising 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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