CN117930664A - Unmanned aerial vehicle landing control optimizing system based on Beidou RTK differential positioning - Google Patents
Unmanned aerial vehicle landing control optimizing system based on Beidou RTK differential positioning Download PDFInfo
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Abstract
The invention discloses an unmanned aerial vehicle landing control optimization system based on Beidou RTK differential positioning, relates to the technical field of unmanned aerial vehicle landing control, and is used for solving the problem of unclear unmanned aerial vehicle landing process control; the landing process control system comprises a multi-source data acquisition module, a landing process determination module, a control process analysis module and an optimization control module; the method comprises the steps of determining target points and environment information, planning a flight path of the unmanned aerial vehicle, fusing data acquired by a sensor, performing filtering processing, sending the filtered data to a satellite end, receiving signal data sent by the satellite end, performing time domain analysis, identifying multipath effects of signals by using Doppler frequency shift, determining a compensation algorithm, analyzing a landing control process of the unmanned aerial vehicle, determining the accurate landing condition of the landing process of the unmanned aerial vehicle according to information generated in the landing control process, further selecting an optimal management strategy of the unmanned aerial vehicle, improving the control of the unmanned aerial vehicle during landing, and reducing the risk of accidents.
Description
Technical Field
The invention relates to the technical field of unmanned aerial vehicle landing control, in particular to an unmanned aerial vehicle landing control optimization system based on Beidou RTK differential positioning.
Background
RTK represents real-time dynamic differential positioning, which is a high-precision positioning technology commonly used in Global Navigation Satellite System (GNSS) receivers such as GPS, GLONASS, beidou and other systems. The RTK technology is used for realizing very high-precision positioning by arranging two or more GPS base stations on the ground and carrying out data exchange and processing between the RTK technology and a mobile receiver (such as an unmanned aerial vehicle), and compared with the common GPS positioning, the RTK technology can realize centimeter-level positioning precision and is suitable for application fields requiring high-precision positioning.
The prior art has the following defects:
In city or have high building environment, because reflection and refraction of signal can produce the multipath effect, lead to positioning error to increase, and the shelter from thing such as building, trees in the city can hinder satellite signal's receipt, influence reliability and the precision of location, and environmental factors such as wind speed, air current also influence unmanned aerial vehicle's flight orbit and landing gesture for there is great deviation in position estimation and actual position when unmanned aerial vehicle descends, influences landing precision and security.
Disclosure of Invention
In order to overcome the defects in the prior art, the embodiment of the invention provides an unmanned aerial vehicle landing control optimization system based on Beidou RTK differential positioning, which comprises the steps of determining target points and environment information, planning a flight path of an unmanned aerial vehicle, fusing data acquired by a sensor, performing filtering processing, transmitting the filtered data to a satellite end, receiving signal data transmitted by the satellite end, performing time domain analysis, identifying multipath effects of signals by using Doppler frequency shift, determining a compensation algorithm, analyzing the unmanned aerial vehicle landing control process, determining the landing accuracy of the unmanned aerial vehicle landing process according to information generated in the acquired landing control process, and further selecting an optimal management strategy of the unmanned aerial vehicle so as to solve the problems in the background art.
In order to achieve the above purpose, the present invention provides the following technical solutions:
The unmanned aerial vehicle landing control optimization system based on Beidou RTK differential positioning comprises a multi-source data acquisition module, a landing process determination module, a control process analysis module and an optimization control module, wherein the modules are connected through signals;
The multi-source data acquisition module is used for determining target points and environment information, planning the flight path of the unmanned aerial vehicle, calibrating the sensor, fusing the data acquired by the sensor and sending the fused data to the landing process determination module;
The landing process determining module receives the data sent by the multi-source data acquisition module, performs filtering processing on the fusion data by combining a Kalman filter and a moving average filter, sends the filtered data to a satellite end, receives signal data sent by the satellite end, performs domain analysis, recognizes the multipath effect of the signal by using a Doppler frequency shift signal processing technology, determines a compensation algorithm, and sends a processing result to the control process analyzing module;
The control process analysis module receives the data sent by the landing process determination module, is used for analyzing the unmanned aerial vehicle landing control process, determines the landing accuracy of the unmanned aerial vehicle landing process according to the acquired identification regulation information generated in the landing control process, and sends the determination result to the optimization control module;
The optimization control module receives the control process analysis module data and is used for analyzing the landing process of the unmanned aerial vehicle and selecting an optimal management strategy of the unmanned aerial vehicle.
In a preferred embodiment, the method is used for determining the target point and the environmental information, planning the flight path of the unmanned aerial vehicle, calibrating the sensors, fusing the data acquired by the sensors, namely, performing zero offset calibration and scale factor calibration on the data acquired by the sensors, performing error compensation on the sensors for inertial measurement by using integral drift and zero offset correction, and filtering and denoising the acquired raw data of each sensor.
In a preferred embodiment, the fused data is filtered using a combination of a kalman filter and a moving average filter, and the specific analysis steps include:
carrying out state prediction by using a Kalman filter, and obtaining a predicted state estimation value and a predicted covariance matrix according to a dynamic model and an observation equation of the system;
In the updating stage of the Kalman filter, calculating Kalman gain according to the prediction residual error and the prediction covariance matrix, and correcting the prediction state estimation value by using the Kalman gain to obtain a final state estimation value and an updated covariance matrix;
Updating the covariance matrix according to the Kalman gain and the prediction covariance matrix;
And taking the state estimation value obtained by the Kalman filter as input, performing filtering processing by a moving average filter, gradually updating the data in the sliding window from the initial moment, and calculating the data average value in the sliding window as the filtered position estimation value.
In a preferred embodiment, the filtered data is sent to a satellite terminal, the signal data sent by the satellite terminal is received and subjected to domain analysis, the multipath effect of the signal is identified by using a Doppler frequency shift signal processing technology, and a compensation algorithm is determined, wherein the specific process is as follows:
Performing time domain analysis on the signal data, including drawing a waveform diagram and an autocorrelation function diagram of the signal, and performing Fourier transform or power spectral density estimation on the signal data to obtain a spectrogram of the signal;
Identifying multipath effects existing in the signal by using Doppler shift signal processing technology;
according to the frequency components in the spectrogram, estimating the size and direction of Doppler frequency shift by using a proper frequency estimation algorithm, and then performing verification and determination by using a frequency estimation method of an autocorrelation function method;
and designing a compensation algorithm according to the obtained Doppler frequency shift information, and adjusting signals according to the size and the direction of the frequency offset by the compensation algorithm.
In a preferred embodiment, the method is used for analyzing the unmanned aerial vehicle landing control process and determining the landing accuracy of the unmanned aerial vehicle landing process according to the acquired identification regulation information generated in the landing control process, and comprises the following steps:
the identification regulation information comprises pose transformation information and background mark adaptation information, wherein the pose transformation information comprises pose regulation stability indexes, and the background mark adaptation information comprises region contour projection adaptation values;
The pose regulation and control stability indexes in the pose transformation information and the region contour projection adaptation values in the background mark adaptation information are combined to generate a stable landing control coefficient;
The pose regulation stability index and the regional contour projection adaptation value are in a proportional relation with the stable landing control coefficient;
And comparing the stable drop control coefficient with a management control threshold.
In a preferred embodiment, comparing the steady drop control coefficient to the management control threshold comprises:
if the stable landing control coefficient is greater than or equal to the management control threshold, generating a landing control stable signal;
If the stable landing control coefficient is smaller than the management control threshold value, generating a landing control abnormal signal and performing control adjustment.
The unmanned aerial vehicle landing control optimization system based on Beidou RTK differential positioning has the technical effects and advantages that:
According to the unmanned aerial vehicle landing control method, the target point and the environment information are determined, the flight path of the unmanned aerial vehicle is planned, data acquired by the sensor are fused and subjected to filtering processing, the filtered data are transmitted to the satellite end, the signal data transmitted by the satellite end are received and subjected to time domain analysis, the multipath effect of the signal is identified by using Doppler frequency shift, a compensation algorithm is determined, the unmanned aerial vehicle landing control process is analyzed, the landing accuracy condition of the unmanned aerial vehicle landing process is determined according to the information generated in the landing control process, the optimal management strategy of the unmanned aerial vehicle is selected, the control of the unmanned aerial vehicle during landing is improved, and the risk of accidents is reduced.
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Fig. 1 is a schematic structural diagram of an unmanned aerial vehicle landing control optimizing system based on Beidou RTK differential positioning.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
In order to achieve the above purpose, fig. 1 shows a schematic structural diagram of an unmanned aerial vehicle landing control optimizing system based on Beidou RTK differential positioning, which specifically comprises a multi-source data acquisition module, a landing process determining module, a control process analyzing module and an optimizing control module, wherein the modules are connected through signals;
The multi-source data acquisition module is used for determining target points and environment information, planning the flight path of the unmanned aerial vehicle, calibrating the sensor, fusing the data acquired by the sensor and sending the fused data to the landing process determination module;
The landing process determining module receives the data sent by the multi-source data acquisition module, performs filtering processing on the fusion data by combining a Kalman filter and a moving average filter, sends the filtered data to a satellite end, receives signal data sent by the satellite end, performs domain analysis, recognizes the multipath effect of the signal by using a Doppler frequency shift signal processing technology, determines a compensation algorithm, and sends a processing result to the control process analyzing module;
The control process analysis module receives the data sent by the landing process determination module, is used for analyzing the unmanned aerial vehicle landing control process, determines the landing accuracy of the unmanned aerial vehicle landing process according to the acquired identification regulation information generated in the landing control process, and sends the determination result to the optimization control module;
The optimization control module receives the control process analysis module data and is used for analyzing the landing process of the unmanned aerial vehicle and selecting an optimal management strategy of the unmanned aerial vehicle.
Correcting errors caused by factors such as atmosphere, clock error, multipath and the like through differential measurement between a base station receiver and a mobile station receiver at a known position so as to improve positioning accuracy, transmitting error information measured in real time to the mobile station in a wireless or wired mode after the base station receiver receives satellite signals, and correcting the received satellite signals by the mobile station by utilizing the error information so as to realize unmanned aerial vehicle landing control;
In the unmanned aerial vehicle landing control process, one or more base station receivers receive the accurate time of the satellite generated signals in real time, and satellite signals received by the unmanned aerial vehicle can be corrected more accurately through differential positioning, so that the positioning accuracy and the unmanned aerial vehicle landing stability are improved;
the basic strategy steps of the unmanned aerial vehicle landing process are as follows: determining a target area (preset landing point) of unmanned aerial vehicle landing, and planning a landing path;
The multi-source data acquisition module is used for acquiring data, namely sensing the environmental information of a landing area through sensors (such as cameras, laser radars, distance sensors and the like) carried on the unmanned aerial vehicle, including factors such as topography, obstacles, wind speeds and the like, selecting proper sensor models and specifications according to the positioning requirements of the unmanned aerial vehicle, determining inertial collection sensors needing to be integrated, including a Beidou RTK differential positioning system and sensors used for inertial measurement, wherein the sensors used for inertial measurement can comprise various inertial sensors used for measuring and recording the acceleration and the angular speed of an object, and the output data of the inertial measurement sensors can be transmitted to other equipment through a digital interface (such as a serial interface) or an analog interface (such as analog voltage output) for processing and analyzing, and the accelerometer is used for measuring the acceleration of the object and generally providing data in the form of three axes (x, y and z) to detect the linear motion of the object in space;
gyroscopes are used to measure the angular velocity of an object and also provide data in the form of three axes (x, y, z). The gyroscope can detect the rotary motion of an object and convert the angular velocity into angular acceleration according to the law of conservation of angular momentum;
magnetometers for measuring the intensity and direction of the earth's magnetic field, providing directional information of objects relative to the earth's magnetic field, magnetometers typically being used in combination with accelerometers and gyroscopes to achieve more accurate navigation and positioning;
according to the target point and the environmental information, the flight path of the unmanned aerial vehicle is planned, environmental factors such as avoiding obstacles, adapting to wind speed and the like are ensured, and the landing path is optimized to ensure safety and stability.
Calibration of each sensor, including zero offset calibration, scale factor calibration, etc., to ensure accuracy and consistency of sensor data, and error compensation of data collected by the sensor for inertial measurement, including correction of integral drift and zero offset, improves stability and reliability of data collected by the sensor for inertial measurement.
Designing a fusion algorithm, fusing data from different sensors to obtain more accurate and stable position estimation, preprocessing the original data from each sensor, including filtering, calibrating, denoising and the like, so as to ensure the quality and consistency of the data, selecting a proper filtering algorithm to carry out filtering processing according to the characteristics and working environment of the sensors, filtering the data by combining a Kalman filter and a moving average filter, reducing noise and uncertainty, and improving the filtering effect and stability, wherein the method comprises the following specific steps:
The prediction stage and the updating stage use Kalman filters, and perform state prediction according to a dynamic model and an observation equation of the system to obtain a prediction state estimation value and a prediction covariance matrix;
Dynamic models describe how the state of a system evolves over time, generally expressed as state transition equations: In the above, the ratio of/> Is a system state vector,/>Is a state transition matrix,/>To control the input matrix,/>For control input,/>For process noise, k represents time;
Covariance prediction equations describe how the uncertainty of the state estimate evolves over time, the equations being: In the above, the ratio of/> For predicting covariance matrix,/>A process noise covariance matrix;
the observation model describes how the system state maps to an observation, generally expressed as the observation equation: In the above, the ratio of/> For observations,/>For observing matrix,/>To observe noise.
In the updating stage of the Kalman filter, according to the predicted residual error and the predicted covariance matrix, calculating Kalman gain, and correcting the predicted state estimation value by using the Kalman gain to obtain a final state estimation value and an updated covariance matrix, wherein the Kalman gain is used for converting the predicted residual error (the difference between the predicted observed value and the actual observed value) into a correction amount of the state estimation value, and the calculation formula of the Kalman gain is as follows: In the above, the ratio of/> Representing the transpose of the observation matrix,/>The covariance matrix is observed;
the correction residual represents the difference between the predicted observed value and the actual observed value, namely the observed residual, and the calculation formula of the correction residual is as follows: In the above, the ratio of/> Multiplying the predicted state estimation value according to the Kalman gain and the correction residual error, and adding the predicted state estimation value for correction to obtain an updated state estimation value;
Updating the covariance matrix according to the Kalman gain and the prediction covariance matrix to reflect the uncertainty of the corrected state estimation, wherein the covariance update is calculated by the following formula: ；
Through the steps, the prediction residual error can be converted into the correction amount of the state estimation value by utilizing the Kalman gain, and the update of the prediction state estimation value is realized, so that the final state estimation value and the updated covariance matrix are obtained.
The state estimation value obtained by the Kalman filter is used as input, and the filter processing is carried out by the moving average filter, and the specific process is as follows: defining the size N of a sliding window, selecting a proper window size, initializing state variables and parameters of a sliding average filter according to the response speed of a system and the requirement on a filtering effect;
State estimation value sequence obtained for Kalman filter Performing a moving average filtering process, gradually updating the data in the sliding window from the initial time, and regarding the data at the kth timeCalculating a data average value in the sliding window as output;
whenever a new state estimate is received, it is added to the end of the sliding window, if the sliding window size N is full, the oldest data is removed from the sliding window to keep the window size unchanged, the output of the sliding average filter is the final position estimate, and the calculation expression is: In the above, the ratio of/> Representing the filtered position estimate at the kth time instant.
It should be noted that, in practical application, the specific form of the dynamic model and the observation equation may be selected and defined according to the physical characteristics of the system and the working principle of the sensor, for example, in unmanned aerial vehicle positioning landing control, the dynamic model may be modeled based on flight dynamics and control theory, the observation equation may be modeled based on the output of a GPS, an RTK or other sensors, and according to practical situations, these equations may be linear or nonlinear and correspond to different kalman filter variants (such as a standard kalman filter, an extended kalman filter, an unscented kalman filter, etc.); the time sequence problem needs to be considered in the landing process of the unmanned aerial vehicle, so that the fusion algorithm can process data from each sensor in time, and the instantaneity and the continuity of fusion results are maintained, so that positioning and correction can be performed in time when landing is performed.
The multipath effect is that the satellite signals are influenced by factors such as reflection, refraction, diffraction and scattering in the propagation process, so that a plurality of propagation paths exist when the signals reach a receiver, and the accuracy and stability of a positioning system are further influenced, particularly in an urban environment or an area with a high building, the positioning accuracy is reduced due to the multipath effect, the unmanned aerial vehicle can not accurately identify a target landing point, or the unmanned aerial vehicle deviates from a preset track in the landing process, and the landing accuracy is influenced;
In an actual environment, after receiving the filtered data, the unmanned aerial vehicle sends the filtered data to a satellite end or a base station, and then the unmanned aerial vehicle determines the position of the unmanned aerial vehicle by receiving signals from satellites, and the position of the unmanned aerial vehicle is used as signal data of the satellites for signal processing;
Performing a time domain analysis on the signal data, including plotting a waveform plot and an autocorrelation function plot of the signal, the time domain analysis may help to understand basic characteristics of the signal, such as delay, amplitude, periodicity, etc.;
carrying out Fourier transform or power spectral density estimation on the signal data to obtain a spectrogram of the signal, wherein frequency domain analysis can help to know the components and energy distribution conditions of the signal on different frequencies;
Identifying multipath effects existing in the signals by using a Doppler frequency shift signal processing technology, wherein the multipath effects are usually represented by phenomena such as signal delay, distortion or a plurality of peaks/valleys, and looking for Doppler frequency shift phenomena existing in a frequency spectrum by observing a signal spectrogram, wherein the Doppler frequency shift is caused by frequency shift of the signals transmitted through different paths, and is usually represented by frequency components in the spectrogram being changed or expanded;
According to the frequency components in the spectrogram, the size and the direction of Doppler frequency shift are estimated by using a proper frequency estimation algorithm, and are determined by using a frequency estimation method of an autocorrelation function method, wherein the autocorrelation function formula is as follows: In the above, the ratio of/> As an autocorrelation function, representing the autocorrelation of the time domain signal x (t) with it at time t+τ, E representing the expected value operator, τ representing the time delay; the Doppler shift, which represents the change in signal frequency in the receiver, is given by: /(I)V denotes the velocity, i.e. the velocity of the signal propagation medium relative to the receiver, and λ denotes the wavelength, i.e. the wavelength of the signal.
Establishing a mathematical model of multipath effect, describing reflection and refraction phenomena occurring in the signal propagation process, and based on a geometrical optics principle, wherein model input parameters comprise factors such as a signal propagation path, a reflector position, a signal arrival time difference and the like;
Modeling signal propagation in a city environment using geometric optics principles, defining propagation paths of signals, including a main path (directly from a satellite to a drone) and a plurality of secondary paths (propagated by reflection, refraction, etc. to the drone), according to building distribution and altitude in the city and relative positions between the drone, satellites, and buildings;
The arrival time difference is calculated, and the time difference of the signal reaching the unmanned aerial vehicle from different paths is calculated according to the length of the propagation path and the signal speed, and the propagation speed of the signal in different media is considered to be possibly different, so that the time difference is caused.
According to the established multipath effect model, a compensation algorithm is designed to counteract the influence of multipath effect on positioning accuracy, which may include the steps of adopting a filter, a correction algorithm and the like to compensate errors introduced by multipath effect;
according to the Doppler frequency shift information obtained by estimation, a corresponding compensation algorithm is designed to inhibit multipath effect, and the compensation algorithm adjusts signals according to the size and direction of frequency offset, so that the influence of the multipath effect on positioning accuracy is minimized;
Verifying a designed compensation algorithm, determining whether a predicted result in the unmanned aerial vehicle landing process accords with an actual situation or not, and generating effective error compensation in a landing process stage or not, so as to realize optimal control of the unmanned aerial vehicle landing process under a multipath effect;
The control process analysis module analyzes the unmanned aerial vehicle landing control process to obtain identification regulation information generated in the landing control process, wherein the identification regulation information comprises pose transformation information and background mark adaptation information;
the pose transformation information comprises a pose regulation and control stability index and is marked as WZT, and the background mark adaptation information comprises a region contour projection adaptation value and is marked as QYL;
The pose regulation and control stable index in the pose conversion information represents stability and response speed of the unmanned aerial vehicle pose control in the landing process, the pose regulation and control stable index can indicate the response speed of a control system to position and pose conversion and the stability of the system in the unmanned aerial vehicle landing control, the pose regulation and control stable index can be used as an important index for evaluating the performance of the landing control system, the landing process can be optimally controlled by adjusting the parameters and algorithms of a controller, the performance and stability of the control system are improved, more accurate and stable pose conversion is realized, and the pose regulation and control stable index can play a role in the following aspects:
Response speed: the improvement of the pose regulation stability index means that the response speed of the control system to the position and pose transformation is faster, and the unmanned aerial vehicle can be more quickly regulated to the target pose, so that the landing time is reduced;
Drop control stability: the high pose regulation stability index means that the control system is more stable, has better resistance to external interference and uncertainty, and is less susceptible to the unstable condition caused by factors such as wind, air flow and the like in the landing process of the unmanned aerial vehicle;
Energy consumption: the improvement of the stability of the control system and the acceleration of the response speed can bring more effective control, reduce the times of adjustment and correction and reduce the energy consumption in the landing process of the unmanned aerial vehicle.
The pose regulation and control stability index is obtained by the following steps:
obtaining landing preparation time and current position of unmanned aerial vehicle And postureAcquiring the position of a target landing areaAnd postureAcquiring position deviation delta P and attitude deviation delta R, and acquiring a starting time point of an output signal received by a control systemThe point in time when the output signal first exceeds the target valueCalculating response timeAcquiring a point in time at which the output signal stabilizes around the target valueCalculating the transition timeObtaining the maximum value of the output signal in the falling processAnd a target valueDetermining steady state output valuesCalculating to obtain overshoot: calculating a steady state error value: calculating a pose regulation stability index, wherein the calculation expression is as follows: ；
In aviation, the attitude is generally described by using an Euler angle, wherein the Euler angle comprises a roll angle (roll), a pitch angle (pitch) and a yaw angle (yaw), and the position deviation and the attitude deviation are obtained by subtracting the position and the attitude of the unmanned aerial vehicle in the current area from the position and the attitude of the unmanned aerial vehicle in the target landing area; the response time is calculated as the time difference between the time point when the control system first exceeds the target value and the start time of the input signal, and the transition time can be determined according to the time when the system reaches the steady state error range, generally, the time period when the control system output fluctuates around the target value and does not exceed a certain threshold value, and the threshold range when the output signal is stabilized around the target value is set according to actual needs so as to determine the end point of the transition time and determine the steady state output value.
The region contour projection adaptation value in the background mark adaptation information represents the adaptation relation between the background change and the landing background in the landing process in the local self-adaptation value separation image in the landing process, and the adaptation condition of the background contour in the landing process is analyzed, so that the landing accuracy is better identified, and the region contour projection adaptation value can influence the following aspects:
Background change identification: the region contour projection adaptation values can help identify background changes during landing. The change of the background can be detected by analyzing the projection adaptation value, and the control strategy in the landing process can be adjusted according to the change condition, so that the unmanned aerial vehicle can accurately identify the landing area;
And (3) adjusting a control strategy: according to the analysis result of the projection adaptation value of the regional profile, the control strategy in the landing process can be adjusted, if the projection adaptation value is lower, the background change is larger or the target regional profile is unstable, and the control strategy needs to be adjusted to cope with the changes, so that the landing accuracy and safety are ensured;
Profile stability: the region contour projection adaptation value reflects the adaptation relationship between the target region contour and the background. The higher projection adaptation value indicates that the contour of the target area is better adapted to the background, and the contour stability is higher; this helps ensure that the target area profile can be stably identified and tracked during descent, thereby improving accuracy of descent.
The acquisition mode of the area contour projection adaptation value is as follows:
Acquiring an image acquired in the landing process of the unmanned aerial vehicle, graying the acquired image to obtain a Gray image Gray (x, y), determining a Gaussian Gray value of the Gray image, and calculating the expression as follows: determining a contour value according to the Gaussian gray value, wherein the calculation expression is as follows: /(I) Where n is the number of pixels selected,/>Weight value representing Gaussian gray value, and determining background threshold:/>In the formula, G represents a constant for limiting a background threshold, the number SL of images with gray images exceeding the background threshold and the number YT of images with acquired images exceeding the background threshold are established, the area contour projection adaptation value is calculated, and the calculation expression is as follows:。
The weight value of the gaussian gray value is determined according to the actual requirement, for example, in the period of time when the landing is just started, the weight value may be set to be small further from the determined landing position, or in the captured image background, a reference object having no obvious landing position may be set to be small, so that the specific weight value is set according to the actual situation.
The pose transformation information and the background mark adaptation information are combined to generate a stable landing control coefficient;
the obtained pose regulation and control stability index WZT and the region contour projection adaptation value QYL are subjected to normalization analysis to generate a stable landing control coefficient, and the stable landing control coefficient is calibrated as follows The expression is: /(I)In the above, the ratio of/>To stabilize the landing control coefficient,/>Preset proportionality coefficients of the pose regulation and control stability index WZT and the region contour projection adaptation value QYL, and/>Are all greater than 0.
The particular method of generating stable drop control coefficients concurrently may involve a variety of algorithms and models, depending on the actual situation and application requirements. In this embodiment, a weighted summation mode is used to combine the pose regulation stability index and the region contour projection adaptation value to generate a comprehensive stable landing control coefficient. The stable landing control coefficient can be used as an input parameter of a control system and used for adjusting a landing control strategy of the unmanned aerial vehicle so as to ensure the stability and the accuracy of a landing process.
It should be noted that, the size of the preset scaling factor is a specific numerical value obtained by quantizing each parameter, and in order to facilitate the subsequent comparison, the size of the scaling factor depends on the number of sample data and the person skilled in the art to initially set a corresponding preset scaling factor for each group of sample data; and the method is not unique, and only the proportional relation between the parameter and the quantized numerical value is not influenced, for example, the proportional relation between the pose regulation stability index and the stable descent control coefficient is realized.
The pose transformation information and the background mark adaptation information are combined to generate a stable landing control coefficient so as to comprehensively consider the pose stability and the environmental adaptability in the landing process of the unmanned aerial vehicle, thereby determining stable landing control parameters. The greater the pose regulation and control stability index is, the stronger the pose regulation capability of the unmanned aerial vehicle in the landing process is, namely the more stable the pose conversion is; the larger the projection adaptation value of the region outline is, the higher the adaptation degree of the target region outline and the background is, which means that the environment recognition capability is stronger and the landing region is easier to be recognized and tracked. Therefore, the larger the expression value of the stable landing control coefficient is, the better the state of the stable landing control coefficient is in the unmanned plane landing control process, and the stable and reliable landing process is embodied.
Comparing the generated stable landing control coefficient with a management control threshold value to generate a landing control stable signal and a landing control abnormal signal;
after the stable landing control coefficient is obtained, comparing the stable landing control coefficient with a management control threshold;
If the stable landing control coefficient is greater than or equal to the management control threshold, generating a landing control stable signal, wherein the landing control stable signal indicates that landing control is in a stable state and landing operation can be continued;
If the stable landing control coefficient is smaller than the management control threshold, generating a landing control abnormal signal, indicating that the landing control has abnormal conditions, and taking corresponding measures for processing;
according to the generated landing control stable signal or abnormal signal, the system can perform corresponding signal processing and feedback control, and if the landing control stable signal is generated, the system can continuously execute landing tasks and continuously monitor the control state;
if a landing control exception signal is generated, the system can trigger a corresponding exception handling program, such as adjusting a control strategy, issuing an alarm or interrupting a landing task, etc.;
By comparing the stable landing control coefficient with the management control threshold value and generating a corresponding signal according to the comparison result, the system can timely identify the landing control state and take necessary measures to ensure the stability and safety of the landing process.
According to the abnormal situation, the landing control strategy is adjusted, and possible adjustment includes:
Replacing or reconfiguring the sensor: such as increasing or decreasing the sensors used to increase the sensing capabilities of the landing control system; re-planning the descent path: re-planning a landing path according to the abnormal condition, and selecting a safer and proper landing area; modifying a control algorithm: the parameters of the control algorithm are adjusted or different control strategies are adopted to adapt to the current environmental change or improve the control precision.
After generating the anomaly signal, the system may issue an alarm to alert an operator or other related personnel to the current anomaly, with possible alarm means including an audible alarm, a visual alarm, a vibratory alarm, etc.;
If the anomaly is severe or cannot be handled in time, the system may choose to interrupt the current landing task to avoid possible security risks, such as suspending the landing process, stopping the landing maneuver of the drone, and temporarily holding in the current position to await further processing.
In summary, when the landing control abnormal signal is generated, the system may adopt different adjustment strategies and processing methods according to specific situations, so as to cope with the current abnormal situation and ensure the safety and stability of the landing task.
It should be noted that, in this embodiment, the relevant threshold information is preset by a professional, and is not explained here too much, and some of the parameters in the embodiment have the same english letters, but are explained in different meanings when used, and are not explained here one by one.
According to the unmanned aerial vehicle landing control method, the target point and the environment information are determined, the flight path of the unmanned aerial vehicle is planned, the data acquired by the sensor are fused, the fused data are subjected to filtering processing, the filtered data are transmitted to the satellite end, the signal data transmitted by the satellite end are received and subjected to domain analysis, the multipath effect of the signal is identified by using the Doppler frequency shift signal processing technology, the compensation algorithm is determined, the landing control process of the unmanned aerial vehicle is analyzed, the accurate landing condition of the unmanned aerial vehicle in the landing control process is determined according to the identification regulation information generated in the landing control process, the optimal management strategy of the unmanned aerial vehicle is selected, the control of the unmanned aerial vehicle in landing is improved, and the risk of accidents is reduced.
The above formulas are all formulas with dimensions removed and numerical values calculated, the formulas are formulas with a large amount of data collected for software simulation to obtain the latest real situation, and preset parameters in the formulas are set by those skilled in the art according to the actual situation.
The above embodiments may be implemented in whole or in part by software, hardware, firmware, or any other combination. When implemented in software, the above-described embodiments may be implemented in whole or in part in the form of a computer program product.
Those of ordinary skill in the art will appreciate that the various illustrative modules and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application.
In addition, each functional module in each embodiment of the present application may be integrated into one processing module, or each module may exist alone physically, or two or more modules may be integrated into one module.
The foregoing is merely illustrative of the present application, and the present application is not limited thereto, and any person skilled in the art will readily recognize that variations or substitutions are within the scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.
Finally: the foregoing description of the preferred embodiments of the invention is not intended to limit the invention to the precise form disclosed, and any such modifications, equivalents, and alternatives falling within the spirit and principles of the invention are intended to be included within the scope of the invention.
Claims (6)
1. Unmanned aerial vehicle landing control optimizing system based on big dipper RTK difference location, its characterized in that: the landing process control system comprises a multi-source data acquisition module, a landing process determination module, a control process analysis module and an optimization control module, wherein the modules are connected through signals;
The multi-source data acquisition module is used for determining target points and environment information, planning the flight path of the unmanned aerial vehicle, calibrating the sensor, fusing the data acquired by the sensor and sending the fused data to the landing process determination module;
The landing process determining module receives the data sent by the multi-source data acquisition module, performs filtering processing on the fusion data by combining a Kalman filter and a moving average filter, sends the filtered data to a satellite end, receives signal data sent by the satellite end, performs domain analysis, recognizes the multipath effect of the signal by using a Doppler frequency shift signal processing technology, determines a compensation algorithm, and sends a processing result to the control process analyzing module;
The control process analysis module receives the data sent by the landing process determination module, is used for analyzing the unmanned aerial vehicle landing control process, determines the landing accuracy of the unmanned aerial vehicle landing process according to the acquired identification regulation information generated in the landing control process, and sends the determination result to the optimization control module;
The optimization control module receives the control process analysis module data and is used for analyzing the landing process of the unmanned aerial vehicle and selecting an optimal management strategy of the unmanned aerial vehicle.
2. The unmanned aerial vehicle landing control optimization system based on Beidou RTK differential positioning according to claim 1, wherein: the method is used for determining target points and environment information, planning a flight path of the unmanned aerial vehicle, calibrating sensors, fusing data acquired by the sensors, namely performing zero offset calibration and scale factor calibration on the data acquired by the sensors, performing error compensation on the sensors for inertial measurement by using integral drift and zero offset correction, and filtering and denoising the acquired raw data of each sensor.
3. The unmanned aerial vehicle landing control optimization system based on Beidou RTK differential positioning according to claim 2, wherein: the fusion data is filtered by using a Kalman filter and a moving average filter, and the specific analysis steps comprise:
carrying out state prediction by using a Kalman filter, and obtaining a predicted state estimation value and a predicted covariance matrix according to a dynamic model and an observation equation of the system;
In the updating stage of the Kalman filter, calculating Kalman gain according to the prediction residual error and the prediction covariance matrix, and correcting the prediction state estimation value by using the Kalman gain to obtain a final state estimation value and an updated covariance matrix;
Updating the covariance matrix according to the Kalman gain and the prediction covariance matrix;
And taking the state estimation value obtained by the Kalman filter as input, performing filtering processing by a moving average filter, gradually updating the data in the sliding window from the initial moment, and calculating the data average value in the sliding window as the filtered position estimation value.
4. The unmanned aerial vehicle landing control optimization system based on Beidou RTK differential positioning according to claim 3, wherein: the filtered data is sent to a satellite end, signal data sent by the satellite end is received and subjected to domain analysis, the multipath effect of the signal is identified by using a Doppler frequency shift signal processing technology, and a compensation algorithm is determined, wherein the specific process is as follows:
Performing time domain analysis on the signal data, including drawing a waveform diagram and an autocorrelation function diagram of the signal, and performing Fourier transform or power spectral density estimation on the signal data to obtain a spectrogram of the signal;
Identifying multipath effects existing in the signal by using Doppler shift signal processing technology;
according to the frequency components in the spectrogram, estimating the size and direction of Doppler frequency shift by using a proper frequency estimation algorithm, and then performing verification and determination by using a frequency estimation method of an autocorrelation function method;
and designing a compensation algorithm according to the obtained Doppler frequency shift information, and adjusting signals according to the size and the direction of the frequency offset by the compensation algorithm.
5. The unmanned aerial vehicle landing control optimization system based on Beidou RTK differential positioning of claim 4, wherein: the method is used for analyzing the unmanned aerial vehicle landing control process and determining the landing accuracy condition of the unmanned aerial vehicle landing process according to the acquired identification regulation and control information generated in the landing control process, and comprises the following steps:
the identification regulation information comprises pose transformation information and background mark adaptation information, wherein the pose transformation information comprises pose regulation stability indexes, and the background mark adaptation information comprises region contour projection adaptation values;
The pose regulation and control stability indexes in the pose transformation information and the region contour projection adaptation values in the background mark adaptation information are combined to generate a stable landing control coefficient;
The pose regulation stability index and the regional contour projection adaptation value are in a proportional relation with the stable landing control coefficient;
And comparing the stable drop control coefficient with a management control threshold.
6. The unmanned aerial vehicle landing control optimization system based on Beidou RTK differential positioning of claim 5, wherein: comparing the stable drop control coefficient with a management control threshold, comprising:
if the stable landing control coefficient is greater than or equal to the management control threshold, generating a landing control stable signal;
And if the stable landing control coefficient is smaller than the management control threshold value, generating a landing control abnormal signal.
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