Geographic surveying and mapping unmanned aerial vehicle flight control method and system

Abstract

The invention relates to the technical field of unmanned aerial vehicle attitude control, and provides a geographic surveying and mapping unmanned aerial vehicle flight control method and system, and the method comprises the steps: obtaining an attitude control state sequence; calculating an attitude control deviation characteristic value through a local attitude change characteristic difference between different attitude control state sequences, calculating an attitude information influence specific gravity coefficient of each attitude information data based on the attitude control deviation characteristic value, and obtaining an attitude information control weight of each attitude information data through the attitude information influence specific gravity coefficient; the attitude information of the unmanned aerial vehicle is obtained according to the attitude information control weights of the different attitude information data in combination with a Mahony filter, and the attitude of the unmanned aerial vehicle is accurately and rapidly controlled according to the obtained attitude information of the unmanned aerial vehicle. According to the method, the weight of each type of attitude information is determined by comparing the local dynamic feature differences of different unmanned aerial vehicle attitude information data, so that the accuracy of acquiring the attitude information by using the Mahony filter to control the unmanned aerial vehicle is improved.

Description

Technical Field

[0001] This application relates to the technical field of UAV attitude control, and specifically relates to a flight control method and system for a geographic mapping UAV. Background Art

[0002] With the rapid development of artificial intelligence technology, information processing technology, and automatic control technology, UAVs are increasingly widely used in the field of geographic mapping. When a UAV performs a mapping task, the flight tasks are diverse and complex. The randomness, ambiguity, and uncertainty of abnormal attitudes pose great challenges to the attitude control of the UAV during flight.

[0003] During the geographic mapping flight of a UAV, once an abnormal attitude is encountered, traditional ground control or remote control stability control methods often cannot achieve timely and effective attitude adjustment. In current technologies, although Mahony filters or Kalman filters are widely used in the estimation of UAV attitude information, the main sensors relied on by these filters, such as gyroscopes, accelerometers, and magnetometers, are vulnerable to factors such as temperature drift, cumulative error, airframe vibration, and magnetic field interference in a complex and changeable mapping environment, resulting in deviations in attitude information estimation and affecting the accuracy of UAV flight attitude control. Summary of the Invention

[0004] This application provides a flight control method and system for a geographic mapping UAV to solve the problem of low accuracy in UAV flight attitude control. The specific technical solutions adopted are as follows: An embodiment of this application provides a flight control method for a geographic mapping UAV. The method includes the following steps: Obtain the attitude control state sequence of each attitude information data during the flight of the UAV; Divide the attitude control state sequence of each attitude information data during the flight of the UAV and determine the time sequence interval, and divide the attitude control state sequence of other attitude information data according to the determined time sequence interval; Determine the attitude data difference degree coefficient of each subsequence after division according to the division result of the attitude control state sequence of each attitude information data, and determine the attitude control deviation eigenvalue of each subsequence; Construct the attitude deviation influence characteristic curve of each attitude control state sequence according to the change relationship of the attitude control deviation eigenvalues of the subsequences in each attitude control state sequence; Obtain the attitude information influence proportion coefficient of each attitude information data based on the relative difference of the attitude deviation influence characteristic curves of different attitude control state sequences; Complete the control of the UAV attitude through the attitude information influence proportion coefficients of each attitude information data during the flight of the UAV.

[0005] In some embodiments of the present application, the method for dividing the attitude control state sequence of each attitude information data during the flight of the unmanned aerial vehicle and determining the time sequence interval, and dividing the attitude control state sequence of other attitude information data according to the determined time sequence interval is as follows: For the attitude control state sequence of each attitude information data, the subsequences of the attitude control state sequence are obtained by using the sequence segmentation method, and the time sequence interval of each subsequence is determined according to the acquisition time range of the subsequence. The sequence composed of the data within the same time sequence interval in the attitude control state sequence of each other attitude information data is used as a subsequence of the attitude control state sequence of each other attitude information data.

[0006] In some embodiments of the present application, the method for obtaining the attitude control state sequence is as follows: The attitude information data during the flight of the unmanned aerial vehicle is obtained by using a sensor. The attitude information data includes angular velocity data, acceleration data, and magnetometer measurement data. The sequence composed of the normalization results of the combined quantities of the data corresponding to each acquisition moment in the three types of data is used as the attitude control state sequence.

[0007] In some embodiments of the present application, the determination method of the attitude data difference degree coefficient is as follows: For each subsequence in the attitude control state sequence of each attitude information data, the absolute value of the difference between the coefficient of variation of all elements in the subsequence and the coefficient of variation of the subsequence in the same time sequence interval in the attitude information control state sequence of other attitudes is used as the first difference. The mapping result of the first difference in the exponential function is calculated, and the trend statistic of the subsequence and the subsequence in the same time sequence interval in the attitude information control state sequence of other attitudes is determined by using the trend verification algorithm; The attitude data difference degree coefficient of the subsequence consists of the mapping result and the trend statistic. Among them, the attitude data difference degree coefficient is positively correlated with the mapping result, and the attitude data difference degree coefficient is negatively correlated with the trend statistic.

[0008] In some embodiments of the present application, the method for determining the attitude control deviation eigenvalue is as follows: For each subsequence in the attitude control state sequence of each attitude information data, the difference comparison result of the attitude data difference degree coefficient of the subsequence and the subsequence in the same time sequence interval in the attitude information control state sequence of other attitudes is obtained, and the comparison result of the change characteristics of all elements in the subsequence and the subsequence in the same time sequence interval in the attitude information control state sequence of other attitudes is obtained; The attitude control deviation eigenvalue of the subsequence consists of the difference comparison result and the comparison result. Among them, the attitude control deviation eigenvalue is positively correlated with the difference comparison result and the comparison result respectively.

[0009] In some embodiments of the present application, the process of constructing the attitude deviation influence characteristic curve includes: For the attitude control state sequence of each type of attitude information data, the sequence composed of the attitude control deviation eigenvalue of all subsequences in the attitude control state sequence is used as the attitude control deviation characteristic sequence. Probability statistics are performed on the attitude control deviation characteristic sequence, and a probability distribution curve is obtained. The probability distribution curve is used as the attitude deviation influence characteristic curve of the attitude control state sequence.

[0010] In some embodiments of the present application, the specific method for obtaining the attitude information influence proportion coefficient is: For each type of attitude information data, obtain the measurement result of the difference between the attitude deviation influence characteristic curve of the attitude control state sequence of the attitude information data and that of the attitude control state sequences of other attitude information data, and calculate the ratio of the mean value of all elements in the attitude control deviation data sequence corresponding to the attitude control state sequence of the attitude information data and that of other attitude information data; The attitude information influence proportion coefficient of the attitude information data consists of the two parts of the measurement result of the difference and the ratio. Among them, the attitude information influence proportion coefficient is positively correlated with the measurement result of the difference and the ratio respectively.

[0011] In some embodiments of the present application, the method for controlling the attitude of the unmanned aerial vehicle by using the attitude information influence proportion coefficient of each attitude information data during the flight of the unmanned aerial vehicle is: Obtain the attitude information control weight of each type of attitude information data through the relative relationship between the attitude information influence proportion coefficients of different attitude information data; Taking the collected attitude information data of each type and the corresponding attitude information control weight as inputs, use the Mahony filter to obtain the attitude value represented by quaternion during the flight of the unmanned aerial vehicle. Convert the attitude value into Euler angles as the input of the PI controller, and the output is the corrected angular velocity value. Adjust the motor speed of the unmanned aerial vehicle according to the corrected angular velocity value to control the attitude of the unmanned aerial vehicle.

[0012] In some embodiments of the present application, the method for obtaining the attitude information control weight is: Calculate the sum of the attitude information proportion coefficients of all types of attitude information data, and use the ratio of the attitude information proportion coefficient of each type of attitude information data to the sum as the attitude information control weight of each type of attitude information data.

[0013] The embodiment of the present application further provides a flight control system for a geographical mapping unmanned aerial vehicle, which includes a memory, a processor, and a computer program stored in the memory and running on the processor. When the processor executes the computer program, the steps of any one of the above methods are implemented.

[0014] As can be seen from the above embodiments, a flight control method for a geographical mapping unmanned aerial vehicle provided by the embodiment of the present application has at least the following beneficial effects: Considering the complex change characteristics of each attitude information data when the unmanned aerial vehicle responds to different flight environments during flight, which leads to a large deviation in the control of the flight attitude of the unmanned aerial vehicle. By collecting the relevant data of the flight attitude of the unmanned aerial vehicle in different flight environments and making the same division of other attitude information data based on the division result of one attitude information data, compared with the existing analysis methods for attitude information data in complex scenarios, it can accurately reflect the relative influence characteristics of environmental changes on the attitude line data; further, according to the analysis results of the relative influence characteristics of each attitude information data, the degree of change of each attitude information data affected by the flight environment in different environments can be determined. Based on the analysis results, the attitude information of the unmanned aerial vehicle in a complex flight environment can be accurately adjusted, thereby improving the control accuracy of the unmanned aerial vehicle's attitude when dealing with complex flight environments. Description of the Drawings

[0015] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the drawings in the following description are only some embodiments of the present application. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0016] Figure 1 It is a schematic flowchart of a flight control method for a geographical mapping unmanned aerial vehicle provided by an embodiment of the present application; Figure 2 It is a schematic diagram of the process of obtaining the control weight of each attitude information provided by an embodiment of the present application; Figure 3 It is a schematic diagram of the process of controlling the attitude of the unmanned aerial vehicle provided by an embodiment of the present application. Detailed Embodiments

[0017] In order to further elaborate on the technical means and effects adopted by this application to achieve the intended invention purpose, the following specifically describes, in conjunction with the accompanying drawings and preferred embodiments, a method and system for controlling the flight of a geographic mapping unmanned aerial vehicle (UAV) proposed according to this application, including its specific implementation manner, structure, features, and effects. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. In addition, specific features, structures, or characteristics in one or more embodiments can be combined in any suitable form.

[0018] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the technical field to which this application belongs.

[0019] The following specifically describes, in conjunction with the accompanying drawings, the specific solution of a method and system for controlling the flight of a geographic mapping UAV provided by this application.

[0020] Please refer to Figure 1 , which shows a flowchart of a method for controlling the flight of a geographic mapping UAV provided by an embodiment of this application. The method includes the following steps: S001, obtaining the attitude control state sequence obtained during the flight of the UAV.

[0021] Geographic mapping has extremely high requirements for data accuracy. The flight control of the UAV can ensure that the UAV flies precisely along a predetermined route, thereby obtaining high-precision and high-resolution mapping data, which is crucial for applications such as topographic map drawing and resource survey.

[0022] The purpose of the embodiment of this application is to improve the accuracy of attitude control during the flight of a geographic mapping UAV, so that the UAV can fly more stably.

[0023] The main sensors for obtaining the UAV attitude information data include an angular velocity meter, an accelerometer, and a magnetometer. Therefore, during the flight of the UAV, the angular velocity data, accelerometer data, and magnetometer measurement data of the UAV are collected through the angular velocity meter, accelerometer, and magnetometer.

[0024] Preferably, in an embodiment of this application, the device for collecting data is a nine-axis gyroscope, where the nine-axis gyroscope includes a three-axis angular velocity meter, a three-axis accelerometer, and a three-axis magnetometer. As other implementation manners, the implementer can select the specific model of the gyroscope according to the actual application scenario.

[0025] Furthermore, the collected UAV angular velocity data, accelerometer data, and magnetometer measurement data respectively include the angular velocity, accelerometer, and magnetometer measurement data rotating around the X-axis, Y-axis, and Z-axis in space. Therefore, each type of data collected is a vector composed of three-axis data. Specifically, for example, the angular velocity data collected at a certain moment is , where , and respectively represent the angular velocities of rotation about the X-axis, Y-axis, and Z-axis at that moment.

[0026] Furthermore, the angular velocity data, acceleration data, and magnetometer measurement data collected during the flight of the drone up to the current flight moment are respectively used as the angular velocity data sequence, acceleration data sequence, and magnetometer measurement data sequence in ascending order of time. Since each element in the above three sequences is a vector composed of three-axis data, the composite quantity of each element in the above three types of data is calculated respectively, that is, the modulus value of each element is taken as the composite quantity of each element. The sequence composed of the composite quantities of all elements in each of the above data sequences is used as an attitude feature sequence, and the normalized results of each attitude feature sequence obtained by using the Z-score normalization algorithm are respectively used as an attitude control state sequence; the Z-score normalization algorithm is a well-known technology and will not be elaborated here.

[0027] It should be understood that, as an embodiment of the present application, the Z-score normalization algorithm is used to normalize the collected data sequence. The above is only to provide a method for normalizing the attitude feature sequence. As other implementation manners, on the premise of achieving the purpose of normalizing the attitude feature sequence, other normalization algorithms in the prior art can be used to normalize the attitude feature sequence.

[0028] So far, the attitude control state sequences of the three attitude information data of the drone have been obtained.

[0029] S002. Determine the attitude control deviation eigenvalue of each attitude control state sequence through the local attitude change feature differences between different attitude control state sequences.

[0030] Generally, during the process of using the nine-axis gyroscope to collect the attitude information data during the flight of the drone, due to the different flight tasks and flight environments faced by the drone, the temperature drift in the nine-axis gyroscope, the degree of influence of the accelerometer by the vibration of the fuselage, and the degree of influence of the magnetometer by the environmental magnetic field are all different. When analyzing the attitude information of the drone by fusing the collected attitude information data, the characteristic influence change degrees of different data in different scenarios are different. Therefore, the influence change characteristics of different attitude information during the flight of the drone can be analyzed through the local change feature differences of different attitude information data collected during the flight of the drone.

[0031] Furthermore, during the flight of the UAV, the overall change characteristics of the attitude information of the entire fuselage are extremely small under the influence of the flight environment. The attitude differences of different attitude information of the UAV are mainly manifested in the local attitude characteristics that appear under the influence of the flight environment, that is, different attitude information is manifested as asynchronous or synchronous attitude information differences. Therefore, the local characteristics of different attitude information are analyzed by dividing time intervals.

[0032] Specifically, each attitude control state sequence is used as an input, and the Variable-length SubsequenceClustering algorithm is used to divide the attitude control state sequence to obtain subsequences of each attitude control state sequence. The minimum length of the divided subsequences is 50, and the maximum length is the ceiling result of one-third of the total number of elements in the attitude control state sequence. The specific division process of the Variable-length Subsequence Clustering algorithm is a well-known technology and will not be elaborated here. The time sequence interval of each subsequence is determined according to the maximum and minimum values of the acquisition times of all elements in each subsequence of the attitude control state sequence. For example, one of the subsequences is , and the sequence composed of the acquisition times corresponding to each element in the subsequence is [5, 6, 7, 8, 9], that is, the acquisition time corresponding to the data 10 in the subsequence is 5. Therefore, the time sequence interval of each subsequence is determined according to the maximum and minimum values of the acquisition times of all elements in the subsequence as [5, 9], that is, the time sequence interval is between 5 and 9 seconds. Since the time sequence interval is divided in the acquisition time range based on the division result of one attitude control state sequence, and all attitude control state sequences are collected synchronously, the acquisition time ranges of all attitude control state sequences are the same. When further analyzing one of the attitude control state sequences, the other two attitude control state sequences are divided according to the time sequence intervals of all subsequences in one attitude control state sequence. For example, the data within the same divided time sequence interval in each of the other attitude control state sequences is used as a subsequence of each of the other attitude control state sequences, so as to complete the division of each of the other attitude control state sequences according to the determined time sequence interval.

[0033] It should be understood that, as an embodiment of the present application, the Variable-length SubsequenceClustering algorithm is used to divide the collected data sequence. The above is only to provide a method for dividing the attitude control state sequence. As other implementation manners, on the premise of achieving the purpose of dividing the attitude control state sequence, other sequence division algorithms in the prior art can be used to divide the attitude feature sequence.

[0034] Under normal circumstances, when there are deviations in the drone flight attitude information data, it is manifested as the local relative change characteristics of different attitude information data. Therefore, the local attitude information relative change characteristics of different attitude control state sequences are analyzed through the time series interval determined by one of the attitude control state sequences, and the attitude data difference degree coefficient is calculated based on the analysis results. The specific process is as follows: For each subsequence in the attitude control state sequence of each attitude information data, the absolute value of the difference between the coefficient of variation of all elements in the subsequence and the coefficient of variation of the subsequence in the same time series interval in other attitude information control state sequences is used as the first difference, and the mapping result of the first difference in the exponential function is calculated. The trend statistic of the subsequence and the subsequence in the same time series interval in other attitude information control state sequences is determined using the trend verification algorithm; In an embodiment of the present application, the mean value of the accumulated result of the ratio of the mapping result to the trend statistic on the attitude control state sequence is used as the attitude data difference degree coefficient of the subsequence. It should be noted that when the trend statistic is 0, the sum of the trend statistic and any positive number less than 0.01 can be used as the denominator.

[0035] Preferably, the trend verification algorithm can be the Mann-Kendall algorithm, and the exponential function can be the exponential function with the natural constant as the base. As other embodiments, on the basis of achieving the purpose of sequence trend change analysis, implementers can use other methods in the prior art to obtain the sequence trend change analysis results, and this application does not make special restrictions.

[0036] Under normal circumstances, the differences in the attitude data difference degree coefficients of the subsequences in different attitude control state sequences can further reflect the local state characteristic deviations of the attitude information data. Therefore, the attitude control deviation characteristic value of each subsequence in the attitude control state sequence of each attitude information data can be calculated based on the attitude data difference degree coefficient of the local attitude information of different attitude control state sequences, and the local change differences of the attitude information data can be accurately reflected through the attitude control deviation characteristic value. The specific process for obtaining the attitude control deviation characteristic value includes: For each subsequence in the attitude control state sequence of each attitude information data, obtain the difference comparison result of the attitude data difference degree coefficients of the subsequence and the subsequence in the same time series interval in other attitude information control state sequences, and obtain the comparison result of the change characteristics of all elements in the subsequence and the subsequence in the same time series interval in other attitude information control state sequences; In an embodiment of the present application, the calculation method of the difference comparison result may be to calculate the absolute value of the difference between the difference degree coefficients of the attitude data of the subsequence and the subsequences in the same time sequence interval of other attitude information control state sequences; the calculation method of the comparison result may be to calculate the Manhattan distance between the subsequence and the subsequences in the same time sequence interval of other attitude information control state sequences; the mean value of the sum of the products of the difference comparison result and the comparison result on all attitude control state sequences is used as the attitude control deviation eigenvalue of the subsequence.

[0037] Thus, the attitude control deviation eigenvalue is obtained.

[0038] S003, obtain the attitude information influence proportion coefficient of each type of attitude information data through the attitude control deviation eigenvalue of each attitude control state sequence, and obtain the attitude information control weight of each type of attitude information data through the attitude information influence proportion coefficient.

[0039] The process of obtaining the attitude information control weight of each type is as Figure 2 shown, and the specific steps include: S1, obtain the attitude deviation influence characteristic curve of the attitude control state sequence of each type of attitude information data.

[0040] As an embodiment of the present application, the specific obtaining process of the attitude deviation influence characteristic curve includes: Taking the first attitude control state sequence as an example for analysis, the sequence formed by arranging the attitude control deviation eigenvalues of all subsequences in the th attitude control state sequence in ascending order is used as the attitude control deviation data sequence of the th attitude control state sequence. Perform probability statistics on all elements in the attitude control deviation data sequence to obtain the probability distribution curve of the th attitude control state sequence. The abscissa of the probability distribution curve is each element in the attitude control deviation data sequence, and the ordinate is the probability of each element appearing in the sequence; the probability distribution curve is used as the attitude deviation influence characteristic curve.

[0041] It should be noted that in an embodiment of the present application, in the same way as the obtaining method of the attitude deviation influence characteristic curve of the th attitude control state sequence, the attitude deviation influence characteristic curves of other each attitude control state sequence can be obtained. Among them, the time sequence interval determined by each attitude control state sequence is obtained from the local attitude data characteristics of the currently analyzed attitude control state sequence. For example, in the process of obtaining the attitude deviation influence characteristic curve of the th attitude control state sequence, taking the Determine the time sequence interval of the subsequences obtained by dividing a posture control state sequence, and obtain the subsequences of each other posture control state sequence according to the determined time sequence interval. Further, obtain the posture control deviation eigenvalue through the calculation method in S002, and obtain the posture deviation influence characteristic curve of the two posture control state sequences.

[0042] For an embodiment of the present application, the method for obtaining the probability distribution curve of the posture control deviation characteristic sequence may be: performing probability distribution statistics on the posture control deviation characteristic sequence, and constructing a two-dimensional rectangular coordinate system with the abscissa being each posture control deviation eigenvalue and the ordinate being the probability of the occurrence of each posture control deviation eigenvalue according to the statistical results, and taking the curve formed by all data points in the two-dimensional rectangular coordinate system as the probability distribution curve.

[0043] S2. Construct the posture information influence weight coefficient through the posture control deviation data sequence and the posture deviation influence characteristic curve of each posture information data.

[0044] Determine the posture information influence weight coefficient of different posture information data during the flight of the UAV according to the differences between the posture deviation influence characteristic curves of each posture control state sequence. The posture information influence weight coefficient reflects the change degree of the posture information data affected by the flight environment during the flight of the UAV. The specific construction process includes: For each posture information data, obtain the measurement result of the difference between the posture deviation influence characteristic curve of the posture control state sequence of the posture information data and the posture deviation influence characteristic curve of the posture control state sequence of other posture information data, and calculate the ratio of the mean value of all elements in the posture control deviation data sequence of the posture control state sequence of the posture information data and the posture control state sequence of other posture information data. In an embodiment of the present application, the mean value of the cumulative results of the product of the measurement result of the difference and the ratio on all posture information data is used as the posture information influence weight coefficient of the posture information data. The larger the posture information influence weight coefficient, the greater the degree of influence of the posture information data relative to other posture information data during the flight of the UAV.

[0045] Preferably, the measurement result of the difference between the posture deviation influence characteristic curve of the posture control state sequence of the posture information data and the posture deviation influence characteristic curve of the posture control state sequence of other posture information data is used as the third product factor. The calculation method of the measurement result of the difference may be the KL (Kullback-Leibler) divergence. As other embodiments, on the basis of achieving the purpose of measuring the difference between the two curves, the implementer may use other methods in the prior art to obtain the measurement result of the difference between the two curves, and the present application does not make special limitations.

[0046] S3. Obtain the attitude information control weight of each attitude information data according to the attitude information proportion coefficient of each attitude information data.

[0047] The specific obtaining process includes: calculating the sum of the attitude information proportion coefficients of all types of attitude information data, and taking the ratio of the attitude information proportion coefficient of each attitude information data to the sum as the attitude information control weight of each attitude information data; if the attitude information control weight is larger, it indicates that the relative change and influence degree of the corresponding attitude information data during the flight of the UAV are larger.

[0048] Thus far, the attitude information control weight has been obtained through the above calculations.

[0049] S004. Accurately and quickly control the UAV attitude according to the attitude information control weights of different attitude information data.

[0050] To accurately control the attitude of the UAV during flight, the attitude information control weights of each attitude information data are obtained through the calculations in S001 to S003 above, where the attitude information data includes angular velocity data, acceleration data, and magnetometer measurement data. Based on the obtained attitude information control weights of the attitude information data, the attitude information of the UAV is controlled and adjusted by combining the Mahony filter and the PI control strategy. The specific control process is as follows: Take the collected angular velocity data, acceleration data, magnetometer measurement data, and the attitude information control weight corresponding to each attitude information data as inputs, use the Mahony filter to obtain the attitude value represented by quaternion during the flight of the UAV, convert the attitude value into Euler angles as the input of the PI controller, and the output is the corrected angular velocity value. Adjust the motor speed of the UAV according to the corrected angular velocity value, where the proportional gain and integral gain in the PI controller are 1.5 and 0.005 respectively. The specific process of controlling the UAV attitude is as Figure 3 shown. The Mahony filter and the PI control strategy are well-known technologies to those skilled in the art, and the specific calculation process of the attitude value will not be elaborated here.

[0051] Based on the same inventive concept as the above method, an embodiment of the present application also provides a flight control system for a geodetic survey UAV, including a memory, a processor, and a computer program stored in the memory and running on the processor. When the processor executes the computer program, it implements the steps of any one of the above methods for a flight control method of a geodetic survey UAV.

[0052] It should be noted that: the above order of the embodiments of the present application is only for description and does not represent the superiority or inferiority of the embodiments. In addition, the specific embodiments of this specification have been described. Moreover, 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, multitasking and parallel processing are also possible or may be advantageous.

[0053] Each embodiment in this specification is described in a progressive manner. For the same or similar parts among the embodiments, reference can be made to each other. Each embodiment focuses on the differences from other embodiments. The above are only the preferred embodiments of the present application and are not used to limit the present application. Any modifications, equivalent replacements, improvements, etc. made within the principle of the present application shall be included in the protection scope of the present application.

Claims

1. A flight control method for a geographic mapping unmanned aerial vehicle, characterized in that, The method includes the following steps: Obtain the attitude control state sequence of each attitude information data during the flight of the unmanned aerial vehicle; Divide the attitude control state sequence of each attitude information data during the flight of the unmanned aerial vehicle and determine the time sequence interval, and divide the attitude control state sequence of other attitude information data according to the determined time sequence interval; Determine the attitude data difference degree coefficient of each subsequence after division according to the division result of the attitude control state sequence of each attitude information data, and determine the attitude control deviation eigenvalue of each subsequence; Construct the attitude deviation influence characteristic curve of each attitude control state sequence according to the change relationship of the attitude control deviation eigenvalues of the subsequences in each attitude control state sequence; Obtain the attitude information influence proportion coefficient of each attitude information data based on the relative difference of the attitude deviation influence characteristic curves of different attitude control state sequences; Complete the control of the attitude of the unmanned aerial vehicle through the attitude information influence proportion coefficient of each attitude information data during the flight of the unmanned aerial vehicle.

2. A flight control method for a geographic mapping unmanned aerial vehicle according to claim 1, characterized in that, The method for dividing the attitude control state sequence of each attitude information data during the flight of the unmanned aerial vehicle and determining the time sequence interval, and dividing the attitude control state sequence of other attitude information data according to the determined time sequence interval is as follows: For the attitude control state sequence of each attitude information data, use the sequence segmentation method to obtain the subsequences of the attitude control state sequence, and determine the time sequence interval of each subsequence according to the acquisition time range of the subsequences. The sequence composed of the data within the same time sequence interval in the attitude control state sequence of other each attitude information data is used as a subsequence of the attitude control state sequence of other each attitude information data.

3. A flight control method for a geographic mapping unmanned aerial vehicle according to claim 2, characterized in that, The method for obtaining the attitude control state sequence is as follows: Use sensors to obtain the attitude information data during the flight of the unmanned aerial vehicle. The attitude information data includes angular velocity data, acceleration data, and magnetometer measurement data. The sequence composed of the normalization results of the synthesized quantities of the data corresponding to each acquisition moment in the three types of data is used as the attitude control state sequence.

4. A flight control method for a geographical mapping unmanned aerial vehicle according to claim 1, characterized in that, The determination method of the attitude data difference degree coefficient is as follows: For each subsequence in the attitude control state sequence of each attitude information data, take the absolute value of the difference between the coefficient of variation of all elements in the subsequence and the coefficient of variation of the subsequence in the same time sequence interval in the attitude control state sequence of other attitude information as the first difference, calculate the mapping result of the first difference in the exponential function, and use the trend verification algorithm to determine the trend statistic of the subsequence and the subsequence in the same time sequence interval in the attitude control state sequence of other attitude information; The attitude data difference degree coefficient of the subsequence consists of the mapping result and the trend statistic. Among them, the attitude data difference degree coefficient is positively correlated with the mapping result, and the attitude data difference degree coefficient is negatively correlated with the trend statistic.

5. A flight control method for a geographic mapping unmanned aerial vehicle according to claim 1, characterized in that, The method for determining the attitude control deviation eigenvalue is: For each subsequence in the attitude control state sequence of each attitude information data, obtain the difference comparison result of the coefficient of the attitude data difference degree between the subsequence and the subsequence in the same time sequence interval of other attitude information control state sequences, and obtain the comparison result of the change characteristics of all elements in the subsequence and the subsequence in the same time sequence interval of other attitude information control state sequences; The attitude control deviation characteristic value of the subsequence consists of two parts: the difference comparison result and the comparison result, where the attitude control deviation characteristic value is positively correlated with the difference comparison result and the comparison result respectively.

6. A flight control method for a geographic mapping unmanned aerial vehicle according to claim 1, characterized in that, The construction process of the attitude deviation influence characteristic curve includes: For the attitude control state sequence of each attitude information data, use the sequence composed of the attitude control deviation characteristic values of all subsequences in the attitude control state sequence as the attitude control deviation characteristic sequence, perform probability statistics on the attitude control deviation characteristic sequence and obtain the probability distribution curve, and use the probability distribution curve as the attitude deviation influence characteristic curve of the attitude control state sequence.

7. A flight control method for a geographic mapping unmanned aerial vehicle according to claim 1, characterized in that, The specific method for obtaining the attitude information influence proportion coefficient is: For each attitude information data, obtain the measurement result of the difference between the attitude deviation influence characteristic curves of the attitude control state sequence of the attitude information data and the attitude control state sequences of other attitude information data, and calculate the ratio of the mean values of all elements in the attitude control deviation data sequence corresponding to the attitude control state sequence of the attitude information data and other attitude information data; The attitude information influence proportion coefficient of the attitude information data consists of two parts: the measurement result of the difference and the ratio, where the attitude information influence proportion coefficient is positively correlated with the measurement result of the difference and the ratio respectively.

8. A flight control method for a geographic mapping unmanned aerial vehicle according to claim 1, characterized in that, The method for controlling the attitude of the UAV by using the attitude information influence proportion coefficient of each attitude information data during the flight of the UAV is: Obtain the attitude information control weight of each attitude information data through the relative relationship between the attitude information influence proportion coefficients of different attitude information data; Take the collected attitude information data of each type and the corresponding attitude information control weight as inputs, use the Mahony filter to obtain the attitude value represented by quaternion during the flight of the UAV, convert the attitude value into Euler angles as the input of the PI controller, the output is the corrected angular velocity value, and adjust the motor speed of the UAV according to the corrected angular velocity value to control the attitude of the UAV.

9. A flight control method for a geographical mapping unmanned aerial vehicle according to claim 8, characterized in that, The method for obtaining the attitude information control weight is: Calculate the sum of the attitude information proportion coefficients of all types of attitude information data, and use the ratio of the attitude information proportion coefficient of each attitude information data to the sum as the attitude information control weight of each attitude information data.

10. A flight control system for a geographical mapping unmanned aerial vehicle, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that, When the processor executes the computer program, it implements the steps of a geographic mapping UAV flight control method as described in any one of claims 1-9.

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