Unmanned aerial vehicle cluster collaborative navigation system

Abstract

The invention relates to the technical field of unmanned aerial vehicle control, in particular to an unmanned aerial vehicle cluster collaborative navigation system. The system comprises a data acquisition module used for acquiring flight data of each unmanned aerial vehicle in an unmanned aerial vehicle cluster in real time; the signal reliability evaluation module is used for determining the signal reliability of each unmanned aerial vehicle according to the flight data in combination with unmanned aerial vehicle sensor performance parameters so as to divide reliable unmanned aerial vehicles and unreliable unmanned aerial vehicles; the reference selection module is used for analyzing the flight path correlation between the unreliable unmanned aerial vehicles and the reliable unmanned aerial vehicles and selecting a reference unmanned aerial vehicle for providing navigation support for each unreliable unmanned aerial vehicle according to the flight path correlation; and the cooperative control module is used for fusing the navigation information of the reference unmanned aerial vehicle, acquiring the estimated state information of the unreliable unmanned aerial vehicle, and controlling the unreliable unmanned aerial vehicle to adjust the flight state so as to realize unmanned aerial vehicle cluster cooperative navigation. According to the invention, the accuracy and robustness of collaborative navigation of the unmanned aerial vehicle cluster are improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of unmanned aerial vehicle control, and in particular to an unmanned aerial vehicle cluster cooperative navigation system. BACKGROUND

[0002] Unmanned aerial vehicle cluster cooperative navigation refers to a technical system in which multiple unmanned aerial vehicles complete high-precision positioning and trajectory control through "perception sharing, information interaction, and joint decision-making". As the tasks performed by unmanned aerial vehicles become increasingly diversified and complex, the capabilities of a single unmanned aerial vehicle are difficult to meet the demand, and multiple unmanned aerial vehicles have the advantages of functional distribution, high system survival rate, and high efficiency, and can better complete task objectives in complex environments, and are widely used in aerial performances, geographic mapping, emergency rescue, and other scenarios.

[0003] The premise for multiple unmanned aerial vehicles to successfully complete a task is to obtain accurate navigation information. In aerial performance scenarios, performances are often held in densely populated areas such as city squares and stadiums, and GNSS signals of unmanned aerial vehicles are easily interfered, causing each node in the cluster to be unable to obtain accurate time references, making it difficult to ensure the consistency and cooperation of data, and thus reducing the accuracy and robustness of the cooperative navigation of the cluster. SUMMARY

[0004] In order to solve the technical problem of low accuracy of cooperative navigation of an unmanned aerial vehicle cluster, the purpose of the present application is to provide an unmanned aerial vehicle cluster cooperative navigation system, and the technical solution adopted is as follows: In a first aspect, the present application provides an unmanned aerial vehicle cluster cooperative navigation system, which comprises: a data acquisition module configured to acquire flight data of each unmanned aerial vehicle in the unmanned aerial vehicle cluster in real time; a signal reliability evaluation module configured to determine the signal reliability of each unmanned aerial vehicle according to the flight data and in combination with unmanned aerial vehicle sensor performance parameters, so as to divide reliable unmanned aerial vehicles and unreliable unmanned aerial vehicles; a reference selection module configured to analyze the flight trajectory correlation between unreliable unmanned aerial vehicles and reliable unmanned aerial vehicles, and to select a reference unmanned aerial vehicle providing navigation support for each unreliable unmanned aerial vehicle according to the flight trajectory correlation; a cooperative control module configured to fuse navigation information of the reference unmanned aerial vehicle, acquire estimated state information of the unreliable unmanned aerial vehicle, and control the unreliable unmanned aerial vehicle to adjust the flight state according to the estimated state information, so as to realize cooperative navigation of the unmanned aerial vehicle cluster.

[0005] In some embodiments, the flight data includes one or more of three-dimensional position coordinates, flight speed, actual route, planned route, and number of satellite signal receptions of the unmanned aerial vehicle.

[0006] In some embodiments, the determining of the signal reliability of each unmanned aerial vehicle comprises: According to the flight data, determining a yaw-related parameter of each unmanned aerial vehicle in a preset reference period; According to the number of satellite signals received by the unmanned aerial vehicle in the flight data, calculating a signal quality parameter of each unmanned aerial vehicle in the reference period; Obtaining a sensor performance parameter of the unmanned aerial vehicle, and weighting and fusing the yaw-related parameter, the signal quality parameter and the sensor performance parameter to obtain the signal reliability of the unmanned aerial vehicle.

[0007] In some embodiments, the determining of the yaw-related parameter of each unmanned aerial vehicle in a preset reference period according to the flight data comprises: Extracting actual position coordinates of each unmanned aerial vehicle and position coordinates of a predetermined trajectory in a preset reference period from the flight data; Calculating a first spatial distance between the actual position coordinates of the unmanned aerial vehicle and the position coordinates of the predetermined trajectory at each time in the reference period, and performing normalization processing on the first spatial distance to obtain a yaw distance at each time; Determining a yaw time according to the yaw distance, and integrating the continuously adjacent yaw times into a yaw period; For each yaw period, calculating a second spatial distance between the actual position coordinates of the unmanned aerial vehicle at adjacent two times, and performing normalization processing on the second spatial distance to obtain a position deviation at the latter time in the adjacent times; Counting the number of times that the position deviation in each yaw period meets a preset jump feature, calculating the proportion of the number of times that meet the preset jump feature in the total number of times of the yaw period, and obtaining a jump time proportion; The yaw-related parameter comprises the yaw distance, the position deviation and the jump time proportion.

[0008] In some embodiments, the process of dividing reliable unmanned aerial vehicles and unreliable unmanned aerial vehicles comprises: Comparing the signal reliability value of each unmanned aerial vehicle with a preset signal reliability threshold value; If the signal reliability value of the unmanned aerial vehicle is greater than the preset signal reliability threshold value, the unmanned aerial vehicle is determined to be a reliable unmanned aerial vehicle; if the signal reliability value of the unmanned aerial vehicle is less than or equal to the preset signal reliability threshold value, the unmanned aerial vehicle is determined to be an unreliable unmanned aerial vehicle; Recording all determination results, and distinguishing and marking the reliable unmanned aerial vehicles and the unreliable unmanned aerial vehicles in the unmanned aerial vehicle cluster.

[0009] In some embodiments, the analyzing of the flight trajectory correlation between the reliable unmanned aerial vehicles and the unreliable unmanned aerial vehicles comprises: acquire a distance sequence between the unreliable unmanned aerial vehicle and the reliable unmanned aerial vehicle in a preset time interval, and calculate a mean and a variance of the distance sequence to obtain a distance stability value; acquire a flight path slope of the unreliable unmanned aerial vehicle and the reliable unmanned aerial vehicle at each moment in the time interval, and judge a positive-negative consistency of the slopes at the same moment; acquire a speed time sequence of the unreliable unmanned aerial vehicle and the reliable unmanned aerial vehicle in the time interval, and calculate a speed sequence similarity of the two; weight and fuse the distance stability value, the positive-negative consistency of the slopes, and the speed sequence similarity to obtain a flight trajectory correlation between the unreliable unmanned aerial vehicle and the reliable unmanned aerial vehicle.

[0010] In some embodiments, the system further comprises: a navigation reference value calculation module configured to calculate, in combination with a signal reliability of the reliable unmanned aerial vehicle and the flight trajectory correlation between the unreliable unmanned aerial vehicle and the reliable unmanned aerial vehicle, a navigation reference value data of each reliable unmanned aerial vehicle for each unreliable unmanned aerial vehicle.

[0011] In some embodiments, the selecting, for each unreliable unmanned aerial vehicle, a reference unmanned aerial vehicle providing navigation support comprises: retrieving the navigation reference value data; for each of the unreliable unmanned aerial vehicles, collecting navigation reference value data corresponding to the unreliable unmanned aerial vehicle from all reliable unmanned aerial vehicles; arranging all the navigation reference value data corresponding to the same unreliable unmanned aerial vehicle in descending order of numerical value; pre-setting a number of reference unmanned aerial vehicles to be selected for each unreliable unmanned aerial vehicle, and selecting, from the sorted navigation reference value data sequence, reliable unmanned aerial vehicles with a high ranking and a number meeting the preset setting; determining the selected reliable unmanned aerial vehicles as the reference unmanned aerial vehicles corresponding to the unreliable unmanned aerial vehicles.

[0012] In some embodiments, the fusing of the navigation information of the reference unmanned aerial vehicles to obtain estimated state information of the unreliable unmanned aerial vehicle comprises: collecting navigation information of each reference unmanned aerial vehicle selected for the unreliable unmanned aerial vehicle, and retrieving navigation reference value data of each reference unmanned aerial vehicle for the corresponding unreliable unmanned aerial vehicle; setting a weight for the navigation information of each reference unmanned aerial vehicle according to the navigation reference value data of the reference unmanned aerial vehicle; inputting the navigation information of each reference unmanned aerial vehicle with the set weight into a preset filtering algorithm for data fusion calculation; obtaining, through the data fusion calculation, estimated state information of the unreliable unmanned aerial vehicle.

[0013] In some embodiments, the estimated state information is used to control the unreliable unmanned aerial vehicle to adjust the flight state, thereby realizing the cooperative navigation of the unmanned aerial vehicle cluster, including: The current actual flight state data of the unreliable unmanned aerial vehicle is obtained, and the estimated state information of the unreliable unmanned aerial vehicle is compared with the current actual flight state data to analyze the deviation between them; According to the deviation analysis result and the preset flight requirement of the unmanned aerial vehicle cluster formation, a flight state adjustment instruction for the unreliable unmanned aerial vehicle is generated, and the adjustment instruction is transmitted to the corresponding unreliable unmanned aerial vehicle in real time.

[0014] In a second aspect, an electronic device is provided, including a memory and a processor, the memory stores executable code, and the processor executes the executable code to implement the embodiments of each possible implementation of the first aspect.

[0015] In a third aspect, an embodiment of the present application provides a computer program product, which includes computer program code, when the computer program code runs on a computer, the computer executes the method in the first aspect or any one of the possible implementation manners of the first aspect.

[0016] In a fourth aspect, an embodiment of the present application provides a computer readable storage medium, which stores a computer program, when the computer program is executed in a computer, the computer executes the embodiments of each possible implementation of the first aspect.

[0017] The embodiments of the present application have at least the following beneficial effects: The present application filters out a plurality of unreliable unmanned aerial vehicles and reliable unmanned aerial vehicles by analyzing the signal reliability of each unmanned aerial vehicle in real time, and selects a plurality of reference unmanned aerial vehicles for each unreliable unmanned aerial vehicle according to the flight trajectory correlation between the reliable unmanned aerial vehicles and the unreliable unmanned aerial vehicles for cooperative navigation, thereby avoiding the misalignment of the formation affecting the visual effect of the performance, reducing the flight safety risk caused by the misalignment of the positioning, and significantly improving the accuracy and robustness of the cooperative navigation of the unmanned aerial vehicle cluster. BRIEF DESCRIPTION OF DRAWINGS

[0018] In order to more clearly illustrate the technical solutions and advantages of the embodiments of the present application or the prior art, the following will briefly introduce the drawings needed in the embodiment or prior art description. Obviously, the drawings in the following description are only some embodiments of the present application, and those skilled in the art can obtain other drawings according to these drawings without creative labor.

[0019] Figure 1 A system block diagram of a cooperative navigation system of an unmanned aerial vehicle cluster provided by an embodiment of the present application; Figure 2 A structural schematic diagram of a computer device provided by an embodiment of the present application. DETAILED DESCRIPTION

[0020] In order to further clarify the technical means and effects taken by the present application to achieve the predetermined object of the application, the specific embodiments, structure, features and effects of the unmanned aerial vehicle cluster cooperative navigation system according to the present application are described in detail below in combination with the drawings and preferred embodiments.

[0021] In the following description, different 'one embodiment' or 'another embodiment' does not necessarily refer to the same embodiment. In addition, the specific features, structures or characteristics in one or more embodiments can be combined in any suitable form.

[0022] In the description of the embodiments of the present application, unless otherwise specified, ' / ' represents the meaning of or, for example, A / B can represent A or B: 'and / or' in the text only describes the association relationship of the associated objects, which means that there can be three relationships, for example, A and / or B, which can represent: A exists alone, A and B exist together, and B exists alone, in addition, in the description of the embodiments of the present application,'multiple' means two or more than two.

[0023] Hereinafter, the terms 'first','second' are only used for description purposes, and cannot be understood as implying or suggesting relative importance or implicitly indicating the number of indicated technical features. Therefore, the features defined with 'first','second' can explicitly or implicitly include one or more features.

[0024] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as understood by a person skilled in the art to which the present application belongs.

[0025] The embodiments of the present application are described below in combination with the drawings. Those skilled in the art can know that with the development of technology and the appearance of new scenes, the technical solutions provided by the embodiments of the present application are also applicable to similar technical problems.

[0026] The specific scheme of the unmanned aerial vehicle cluster cooperative navigation system provided by the present application is specifically described below in combination with the drawings.

[0027] Embodiment one: Please refer to Figure 1 which shows the system block diagram of the unmanned aerial vehicle cluster cooperative navigation system provided by an embodiment of the present application, the system includes the following modules: The embodiment of the present application provides an unmanned aerial vehicle cluster cooperative navigation system, which comprises: The data acquisition module 10 is configured to acquire flight data of each unmanned aerial vehicle in the unmanned aerial vehicle cluster in real time.

[0028] The data acquisition module is a core unit of the system for acquiring basic data of the flight state of the unmanned aerial vehicle. The core function of the data acquisition module is to acquire flight data of each unmanned aerial vehicle in the unmanned aerial vehicle cluster in real time, so as to provide data support for subsequent signal reliability evaluation and trajectory correlation analysis. The data acquisition module acquires flight data of each unmanned aerial vehicle in the unmanned aerial vehicle cluster at a preset acquisition frequency (for example, once per second) through positioning sensors (such as a global navigation satellite system receiver (GNSS)), speed sensors, attitude sensors, and a satellite signal receiving module carried by the unmanned aerial vehicle, so as to ensure that the flight state change of the unmanned aerial vehicle can be captured in real time. The acquired flight data specifically includes three-dimensional position coordinates of the unmanned aerial vehicle (covering height, longitude, and latitude, which are used to reflect the real-time position of the unmanned aerial vehicle in three-dimensional space), flight speed (including horizontal and vertical speed components, which reflect the speed and direction of the unmanned aerial vehicle), an actual route (a path trajectory in the actual flight process of the unmanned aerial vehicle), a planned route (a pre-planned flight path of the unmanned aerial vehicle, which is used as a reference for judging deviation), and a satellite signal receiving quantity (a key indicator reflecting the positioning accuracy of the GNSS, and the more the quantity, the more reliable the positioning is). These data are transmitted to the central processing unit of the system in real time for subsequent storage and analysis.

[0029] The signal reliability evaluation module 11 is configured to determine the signal reliability of each unmanned aerial vehicle according to the flight data and in combination with sensor performance parameters of the unmanned aerial vehicle, so as to divide the reliable unmanned aerial vehicle and the unreliable unmanned aerial vehicle.

[0030] Specifically, first, actual position coordinates of each unmanned aerial vehicle and position coordinates of a predetermined trajectory in a preset reference period (the reference period is a period from the current time to 60 sampling time points before the current time, for example, the previous 60 seconds) are extracted from the flight data; then, a first spatial distance between the actual position coordinates of the unmanned aerial vehicle and the position coordinates of the predetermined trajectory at each time point in the period is calculated, and the first spatial distance is normalized to obtain a deviation distance for quantifying the deviation of the actual position of the unmanned aerial vehicle from the predetermined trajectory; then, a preset deviation distance threshold (for example, 0.2) is set, and a time point at which ≥0.2 is recorded as a deviation time point, and continuously adjacent deviation time points are integrated into a deviation period; for each deviation period, a second spatial distance between actual position coordinates of the unmanned aerial vehicle at adjacent two time points is calculated, and the second spatial distance is normalized to obtain a position deviation ​​reflects the instantaneous change range of the UAV position) ; and a pre-set position deviation threshold (e.g. 0.8) is set The time point at which the position deviation is greater than or equal to 0.8 is recorded as a time point meeting the pre-set jump characteristic, the number of such time points in each yaw period is counted, and the proportion of the jump time points in the total time points in the yaw period is calculated to obtain the jump time point proportion. The yaw distance , the position deviation and the jump time point proportion are collectively referred to as yaw-related parameters.

[0031] Further, according to the number of satellite signals received by the UAV in the flight data, the signal quality parameter of each UAV in the reference period is calculated. Specifically, a pre-set satellite number threshold (e.g. 4) is set, the time points at which the number of satellite signals received in the yaw time points is greater than 4 are marked as 1, and otherwise as 0 to form a 0-1 sequence; the continuously adjacent "1" time points form a signal stable period, and otherwise a signal unstable period.

[0032] The signal quality parameter of the target UAV in the s-th yaw period is calculated as follows: wherein, represents the number of time points in the longest signal stable period in the s-th yaw period, represents the number of all time points in the s-th yaw period, represents the signal stable state duration factor in the s-th yaw period, represents the average number of satellites received by the target UAV at all time points in the corresponding period, The greater the value, the higher the positioning accuracy.

[0033] Further, the sensor performance parameters of the UAV (including the IMU drift rate, the performance coefficient corresponding to the sensor working state (normal / fault warning), which are obtained through the sensor fault detection module carried by the UAV) are obtained; then the position change stability of the target UAV in the s-th yaw period is calculated by the following formula: wherein, represents the number of jump time points in the s-th yaw period, represents the number of all time points in the s-th yaw period, represents the frequency of sudden change of the UAV position in the s-th yaw period, represents the yaw distance of the n-th jump time point in the s-th yaw period, represents the position deviation of the n-th jump time point in the s-th yaw period. ​​represents the degree of yaw mutation of the unmanned aerial vehicle in the s-th yaw period.

[0034] The signal reliability of the target unmanned aerial vehicle at the current time is calculated by the following formula : wherein, represents the number of deviation periods before the current time, represents the number of normal time points in the reference period at the current time, represents the total duration of the reference period at the current time, is the proportion of the target unmanned aerial vehicle flight state normal time, The greater the value, the less likely the target unmanned aerial vehicle will deviate during flight, the better the flight stability, and the more reliable the signal. represents the proportion of the target unmanned aerial vehicle flight deviation period, The greater the value, the more serious the flight deviation of the unmanned aerial vehicle, the worse the flight quality, and the less reliable the signal, represents the normalization processing, represents the position change stability of the target unmanned aerial vehicle in the s-th yaw period, represents the signal quality of the target unmanned aerial vehicle in the s-th yaw period.

[0035] When dividing reliable unmanned aerial vehicles and unreliable unmanned aerial vehicles, the signal reliability value of each unmanned aerial vehicle is compared with a preset signal reliability threshold (for example, 0.4): if the signal reliability value is greater than 0.4, it is determined as a reliable unmanned aerial vehicle (the self-positioning system is stable and can provide navigation reference); if the signal reliability value is less than or equal to 0.4, it is determined as an unreliable unmanned aerial vehicle (the self-positioning system is invalid or severely degraded, and needs to rely on other unmanned aerial vehicle navigation support); at the same time, all determination results are recorded, and reliable unmanned aerial vehicles and unreliable unmanned aerial vehicles are marked for subsequent module calling.

[0036] The reference selection module 12 is used to analyze the flight trajectory correlation between the unreliable unmanned aerial vehicle and the reliable unmanned aerial vehicle, and to select a reference unmanned aerial vehicle for providing navigation support for each unreliable unmanned aerial vehicle according to the flight trajectory correlation.

[0037] Specifically, first, the distance sequence between the unreliable unmanned aerial vehicle (the b-th) and the reliable unmanned aerial vehicle (the c-th) in a preset time interval (the same as the above reference period, for example, the previous 60 seconds) is obtained The mean value of the sequence is calculated And the variance The mean value reflects the average distance between the two machines, and the variance reflects the distance fluctuation degree, is compared with The distance stability value is obtained by combining the results; then the flight synchronization of the two aircraft at the current moment is calculated. : , The larger the value, the more stable the distance between the two aircraft; then, the slope of the flight path at each moment on the actual flight path of the two aircraft within the reference time period is obtained. , .when , When both values ​​are negative or both are positive at time i, the label values ​​of the b-th unreliable drone and the c-th reliable drone at time i are compared. Record it as 1, otherwise record it as 0.1. Compare the label values ​​of the b-th unreliable drone and the c-th reliable drone at time i. Synchronization of flight between the b-th unreliable UAV and the c-th reliable UAV at time i. The product of these two values ​​is denoted as the consistency of the motion trends of the b-th unreliable UAV and the c-th reliable UAV at time i. Calculate the mean of the consistency of the motion trends of the b-th unreliable UAV and the c-th reliable UAV at all times within the reference time period. Then obtain the velocity time series of the two machines within the reference time period. }as well as{ } Calculate the DTW distance between the two sequences. , Reflecting speed similarity, the smaller the value, the closer the speeds are; finally, the correlation of flight trajectories is calculated using the following formula. : High correlation of flight trajectories means that the two drones move in the same direction. The parameters of the reliable drone, such as speed, heading angle, and turning angular velocity, are highly consistent with the requirements of the unreliable drone. The unreliable drone can directly refer to the parameters of the reliable drone to adjust its own movement, ensuring synchronization with the cluster and guaranteeing visual integrity and flight safety.

[0038] Furthermore, the navigation reference value of the c-th reliable UAV for the b-th unreliable UAV is calculated using the following formula. : in, This indicates the signal reliability of the c-th reliable UAV at the current moment. This represents the trajectory correlation between the b-th unreliable UAV and the c-th reliable UAV at the current moment. This indicates normalization processing.

[0039] in, As Adjustment coefficient, The larger the value, the more satellites the c-th reliable drone receives at the current moment, resulting in higher positioning accuracy and more reliable navigation support for the b-th unreliable drone, further enhancing the overall collaborative capability of the drone swarm.

[0040] Retrieve all navigation reference value data output by the navigation reference value calculation module; for each unreliable UAV, collect the navigation reference value data of all reliable UAVs corresponding to it (i.e., the data between the unreliable UAV and each reliable UAV). Then, all navigation reference value data corresponding to the same unreliable drone are sorted in descending order of value to form a navigation reference value sequence; the number of reference drones selected for each unreliable drone is preset (e.g., 5, which can be adjusted according to the cluster size); reliable drones with the highest ranking and the number that meets the preset setting (e.g., the top 5) are selected from the sorted navigation reference value sequence; finally, the selected reliable drones are determined as the reference drones for the corresponding unreliable drones, completing the selection and allocation of reference drones.

[0041] The collaborative control module 13 is used to fuse the navigation information of the reference UAV, obtain the estimated state information of the unreliable UAV, and control the unreliable UAV to adjust its flight state according to the estimated state information, thereby realizing collaborative navigation of the UAV cluster.

[0042] Specifically, the navigation information of each reference UAV selected for the unreliable UAV is first collected. This navigation information includes the reference UAV's GNSS coordinates (3D position data), flight attitude (roll angle, pitch angle, yaw angle), flight speed (horizontal and vertical velocity components), and relative distance (spatial distance between the two aircraft) and relative attitude (attitude difference between the two aircraft) between the reference UAV and the corresponding unreliable UAV. Then, the navigation reference value of each reference UAV for the corresponding unreliable UAV is retrieved. ,according to The normalized value is used to set the weight of the navigation information of the reference UAV. The larger the value, the greater the weight, and the higher its contribution to estimating state information. Then, a preset filtering algorithm is selected according to the size of the UAV cluster and the data requirement (for example, when the size of the cluster is less than 50 and the real-time requirement for data is high, a weighted Kalman filtering algorithm is selected; when the size of the cluster is greater than or equal to 50 and the fault tolerance requirement for data is high, a distributed consensus filtering algorithm is selected); the navigation information of each reference UAV with the set weight is input into the selected filtering algorithm for data fusion calculation; through iterative calculation of the filtering algorithm, the estimated state information of the unreliable UAV is finally obtained, which includes position estimation information (corrected three-dimensional position coordinates), attitude estimation information (corrected flight attitude parameters), and speed estimation information (corrected flight speed parameters).

[0043] Further, the current actual flight state data of the unreliable UAV is obtained, the estimated state information is compared with the current actual flight state data, and the deviation (such as position deviation, attitude deviation, and speed deviation) between the two is analyzed; according to the deviation analysis result and the preset flight requirement (such as the preset formation trajectory, attitude standard, and speed range) of the UAV cluster formation, a flight state adjustment instruction for the unreliable UAV is generated, which covers a position correction instruction (for correcting the position deviation), an attitude adjustment instruction (for adjusting the flight attitude), and a speed calibration instruction (for calibrating the flight speed); The adjustment instruction is transmitted to the corresponding unreliable UAV in real time; after receiving the adjustment instruction, the flight control system of the unreliable UAV adjusts its flight parameters according to the instruction content, and performs position correction, attitude adjustment, and speed calibration operations; the cooperative control module monitors the adjusted flight state of the unreliable UAV in real time, obtains new actual flight state data, and compares it with the estimated state information again to determine whether the deviation is within the preset permission range (for example, ±0.3 meters in the height direction and ±0.2 meters / second in the horizontal direction, which can be set according to the application scenario); if the deviation is within the preset permission range, it is confirmed that the unreliable UAV has completed the flight state adjustment; if the deviation exceeds the preset permission range, the flight state adjustment process is repeated, such as deviation analysis, instruction generation and transmission, and state adjustment, until the deviation meets the requirement; The flight state adjustment process is performed on all unreliable UAVs in the UAV cluster one by one to ensure that each unreliable UAV corrects its flight state according to the estimated state information, and the formation flight state of the entire cluster is monitored in real time to ensure that the reliable UAVs and the adjusted unreliable UAVs fly according to the preset formation trajectory, and finally the cooperative navigation of the UAV cluster is realized. It should be noted that the flight state adjustment process is also the execution of the reference selection module 12 and the cooperative control module 13.

[0044] It should be noted that the device provided in the above embodiment is only used as an example for the division of the above functional modules, and in actual applications, the above functions can be completed by different functional modules according to needs, that is, the internal structure of the computer device is divided into different functional modules to complete all or part of the functions described above.

[0045] Figure 2 is a structural schematic diagram of a computer device provided by an embodiment of the present application. As shown in the example, Figure 2 the computer device 20 includes a memory 21, a processor 22, and a computer program 23 stored in the memory 21 and running on the processor 22, wherein the processor 22 executes the computer program 23, so that the computer device can execute any of the above-mentioned drone cluster cooperative navigation systems.

[0046] In addition, an embodiment of the present application also protects a device, which can include a memory and a processor, wherein the memory stores executable program code, and the processor is used to call and execute the executable program code to execute the drone cluster cooperative navigation system provided by the embodiment of the present application.

[0047] The embodiment of the present application can divide the device into functional modules according to the above method examples, for example, each functional module can be corresponding, or two or more functions can be integrated in one processing module, and the above integrated module can be realized in the form of hardware. It should be noted that the division of the modules in the present embodiment is illustrative, and is only a logical function division, and another division mode can be used in actual implementation.

[0048] It should be understood that the device provided by the embodiment of the present application is used to execute the above-mentioned drone cluster cooperative navigation system, so as to achieve the same effect as the above-mentioned implementation method.

[0049] In the case of using integrated units, the device can include a processing module and a storage module. When the device is applied to a device, the processing module can be used to control and manage the actions of the device. The storage module can be used to support the device to execute mutual program codes and the like. The processing module can be a processor or a controller, which can realize or execute various exemplary logical blocks, modules and circuits described in combination with the disclosure of the present application. The processor can also be a combination of computing functions, such as one or more microprocessor combinations, combinations of digital signal processing (Digital Signal Processing, DSP) and microprocessors, and the like, and the storage module can be a memory.

[0050] In addition, the apparatus provided by the embodiments of the present application can be a chip, a component or a module, and the chip can include a processor and a memory connected to each other. The memory is used to store instructions, and when the processor invokes and executes the instructions, the chip can perform the unmanned aerial vehicle cluster cooperative navigation system provided by the above embodiments.

[0051] The embodiments of the present application also provide a computer readable storage medium, which stores computer program codes, and when the computer program codes are run on a computer, the computer is caused to perform the above related method steps to realize the unmanned aerial vehicle cluster cooperative navigation system provided by the above embodiments.

[0052] The embodiments of the present application also provide a computer program product, and when the computer program product is run on a computer, the computer is caused to perform the above related steps to realize the unmanned aerial vehicle cluster cooperative navigation system provided by the above embodiments.

[0053] The apparatus, the computer readable storage medium, the computer program product or the chip provided by the embodiments of the present application are used to execute the corresponding method provided above, so the beneficial effects achieved by the apparatus, the computer readable storage medium, the computer program product or the chip can refer to the beneficial effects of the corresponding method provided above, which will not be repeated here. Through the above description of the implementation mode, those skilled in the art can understand that, for the convenience and brevity of description, only the above division of functional modules is taken as an example for illustration, and in actual application, the above functions can be completed by different functional modules according to needs, that is, the internal structure of the apparatus is divided into different functional modules to complete all or part of the functions described above. In the embodiments provided by the present application, it should be understood that the disclosed apparatus and method can be implemented by other ways.

[0054] The apparatus embodiments described above are only schematic, for example, the division of the modules or units is only a logical function division, and there can be another division way in actual implementation, for example, a plurality of units or components can be combined or integrated into another apparatus, or some features can be ignored or not executed. In addition, the coupling or direct coupling or communication connection between the shown or discussed mutual ones can be indirect coupling or communication connection through some interfaces, apparatuses or units, and can be electrical, mechanical or other forms.

[0055] It should also be noted that, as used in this document, the terms "comprises" or "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but can also include other elements not expressly listed or inherent to such process, method, article, or apparatus. An element proceeded by "comprises... a" does not, without more constraints, exclude the presence of additional identical elements in the process, method, article, or apparatus that comprises the element.

[0056] It should be noted that the above-mentioned order of the embodiments of the present application is only for description, and does not represent the advantages and disadvantages of the embodiments. The processes depicted in the drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multi-task processing and parallel processing are also possible or can be advantageous.

[0057] Each of the embodiments in the specification is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other. Each embodiment focuses on the difference from other embodiments.

[0058] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art can easily think of changes or replacements within the technical range disclosed by the present application, which should be covered within the protection scope of the present application.

Claims

1. A UAV swarm cooperative navigation system, characterized in that, The system comprises the following modules: a data acquisition module for acquiring flight data of each unmanned aerial vehicle in the unmanned aerial vehicle cluster in real time; a signal reliability evaluation module for determining the signal reliability of each unmanned aerial vehicle according to the flight data and in combination with the sensor performance parameters of the unmanned aerial vehicle, so as to divide reliable unmanned aerial vehicles and unreliable unmanned aerial vehicles; a reference selection module for analyzing the flight trajectory correlation between the reliable unmanned aerial vehicles and the unreliable unmanned aerial vehicles, and selecting a reference unmanned aerial vehicle for providing navigation support for each unreliable unmanned aerial vehicle according to the flight trajectory correlation; a cooperative control module for fusing the navigation information of the reference unmanned aerial vehicle, obtaining the estimated state information of the unreliable unmanned aerial vehicle, and controlling the unreliable unmanned aerial vehicle to adjust the flight state according to the estimated state information, so as to realize cooperative navigation of the unmanned aerial vehicle cluster.

2. The UAV swarm cooperative navigation system of claim 1, wherein, The flight data comprises one or more of the three-dimensional position coordinates, flight speed, actual route, planned route, and number of satellite signal receptions of the unmanned aerial vehicle.

3. The UAV swarm cooperative navigation system of claim 1, wherein, The determination of the signal reliability of each unmanned aerial vehicle comprises: determining the yaw correlation parameters of each unmanned aerial vehicle in a preset reference period according to the flight data; calculating the signal quality parameters of each unmanned aerial vehicle in the reference period according to the number of satellite signal receptions of the unmanned aerial vehicle in the flight data; obtaining the sensor performance parameters of the unmanned aerial vehicle, and fusing the yaw correlation parameters, the signal quality parameters and the sensor performance parameters by weighting to obtain the signal reliability of the unmanned aerial vehicle.

4. The UAV swarm cooperative navigation system of claim 3, wherein, The determination of the yaw correlation parameters of each unmanned aerial vehicle in a preset reference period according to the flight data comprises: extracting the actual position coordinates and the position coordinates of the predetermined trajectory of each unmanned aerial vehicle in the preset reference period from the flight data; calculating the first spatial distance between the actual position coordinates of the unmanned aerial vehicle and the position coordinates of the predetermined trajectory at each time in the reference period, and performing normalization processing on the first spatial distance to obtain the yaw distance at each time; determining the yaw time according to the yaw distance, and integrating the continuously adjacent yaw times into a yaw period; for each yaw period, calculating the second spatial distance between the actual position coordinates of the unmanned aerial vehicle at adjacent two times, and performing normalization processing on the second spatial distance to obtain the position deviation at the latter time in the adjacent times; counting the number of times of position deviation meeting the preset jump feature in each yaw period, calculating the proportion of the number of times of position deviation meeting the preset jump feature to the total number of times of the yaw period, and obtaining the proportion of the jump time; The yaw correlation parameters comprise the yaw distance, the position deviation and the proportion of the jump time.

5. The UAV swarm cooperative navigation system of claim 3, wherein, The process of dividing the reliable unmanned aerial vehicles and the unreliable unmanned aerial vehicles comprises: comparing the signal reliability value of each unmanned aerial vehicle with a preset signal reliability threshold value; if the signal reliability value of the unmanned aerial vehicle is greater than the preset signal reliability threshold value, the unmanned aerial vehicle is determined to be a reliable unmanned aerial vehicle; if the signal reliability value of the unmanned aerial vehicle is less than or equal to the preset signal reliability threshold value, the unmanned aerial vehicle is determined to be an unreliable unmanned aerial vehicle; record all the determination results, and distinguish and mark the reliable unmanned aerial vehicles and the unreliable unmanned aerial vehicles in the unmanned aerial vehicle cluster.

6. The UAV swarm cooperative navigation system of claim 1, wherein, The analysis of the flight trajectory correlation between the unreliable unmanned aerial vehicle and the reliable unmanned aerial vehicle comprises: a distance sequence between the unreliable unmanned aerial vehicle and the reliable unmanned aerial vehicle in a preset time interval is obtained, and a mean value and a variance of the distance sequence are calculated to obtain a distance stability value; a slope of an actual flight path of the unreliable unmanned aerial vehicle and the reliable unmanned aerial vehicle at each time in the time interval is obtained, and a positive-negative consistency of the slopes at the same time is determined; a speed time sequence of the unreliable unmanned aerial vehicle and the reliable unmanned aerial vehicle in the time interval is obtained, and a speed sequence similarity of the two is calculated; the distance stability value, the positive-negative consistency of the slopes and the speed sequence similarity are weighted and fused to obtain the flight trajectory correlation between the unreliable unmanned aerial vehicle and the reliable unmanned aerial vehicle.

7. The UAV swarm cooperative navigation system of claim 1, wherein, The system further comprises: a navigation reference value calculation module configured to calculate, in combination with a signal reliability of the reliable unmanned aerial vehicle and the flight trajectory correlation between the unreliable unmanned aerial vehicle and the reliable unmanned aerial vehicle, a navigation reference value data of each reliable unmanned aerial vehicle for each unreliable unmanned aerial vehicle.

8. The UAV swarm cooperative navigation system of claim 7, wherein, The selection of a reference unmanned aerial vehicle providing navigation support for each unreliable unmanned aerial vehicle according to the flight trajectory correlation comprises: the navigation reference value data is called; for each unreliable unmanned aerial vehicle, navigation reference value data corresponding to the unreliable unmanned aerial vehicle is collected from all reliable unmanned aerial vehicles; all navigation reference value data corresponding to the same unreliable unmanned aerial vehicle is arranged in descending order of value; a number of reference unmanned aerial vehicles to be selected for each unreliable unmanned aerial vehicle is preset, and reference unmanned aerial vehicles with a high ranking and a number meeting the preset are selected from the arranged navigation reference value data sequence; the selected reference unmanned aerial vehicles are determined as the reference unmanned aerial vehicles corresponding to the unreliable unmanned aerial vehicles.

9. The UAV swarm cooperative navigation system of claim 7, wherein, The fusion of navigation information of the reference unmanned aerial vehicles and the obtaining of estimated state information of the unreliable unmanned aerial vehicle comprise: navigation information of each reference unmanned aerial vehicle selected for the unreliable unmanned aerial vehicle is collected, and navigation reference value data of each reference unmanned aerial vehicle for the corresponding unreliable unmanned aerial vehicle is called; a weight of navigation information of each reference unmanned aerial vehicle corresponding to the reference unmanned aerial vehicle is set according to the navigation reference value data of the reference unmanned aerial vehicle; the navigation information of each reference unmanned aerial vehicle with the set weight is input into a preset filtering algorithm for data fusion calculation; estimated state information of the unreliable unmanned aerial vehicle is obtained through the data fusion calculation.

10. The UAV swarm cooperative navigation system of claim 1, wherein, The control of the unreliable unmanned aerial vehicle to adjust a flight state according to the estimated state information, thereby realizing the cooperative navigation of the unmanned aerial vehicle cluster, comprises: current actual flight state data of the unreliable unmanned aerial vehicle is obtained, and the estimated state information of the unreliable unmanned aerial vehicle is compared with the current actual flight state data to analyze a deviation therebetween; a flight state adjustment instruction for the unreliable unmanned aerial vehicle is generated according to a deviation analysis result and a preset flight requirement of the unmanned aerial vehicle cluster formation, and the adjustment instruction is transmitted to the corresponding unreliable unmanned aerial vehicle in real time.

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