An inertial navigation method and system for an unmanned aerial vehicle

Abstract

The present invention relates to the field of navigation technology, and particularly to an inertial navigation method and system for an unmanned aerial vehicle. The present invention determines the relative distance and offset angle of the unmanned aerial vehicle based on the preset track position coordinates and the current track running position coordinates, combines the track complexity of the current navigation section to determine the error influence coefficient, determines whether it is necessary to correct the running information of the unmanned aerial vehicle, determines the error accumulation variable, determines the error accumulation category of the unmanned aerial vehicle, determines the current track running position as the updated starting point position, combines with the navigation end position to reset the running information of the unmanned aerial vehicle; traces the error point, determines the yaw angle based on the navigation section corresponding to the unmanned aerial vehicle at the error point, corrects the yaw angle using Kalman filtering, determines the current track running position as the updated starting point position, combines with the corrected yaw angle and the navigation end position to reset the running information of the unmanned aerial vehicle, improving the flight accuracy and reliability of the unmanned aerial vehicle.

Description

Technical Field

[0001] The present invention relates to the technical field of navigation, and particularly to an inertial navigation method and system for an unmanned aerial vehicle (UAV). Background Art

[0002] The inertial navigation of a UAV is one of the core technologies for realizing the autonomous flight and precise navigation of the UAV, and is an autonomous navigation technology that does not rely on external signals. The core of the system is sensors, including an accelerometer and a gyroscope. It measures the linear acceleration and angular velocity of the UAV through the accelerometer and gyroscope on the UAV, and combines the initial position, attitude, and velocity information to calculate the current position, velocity, and attitude of the UAV. The advantage of inertial navigation is that it is completely autonomous and can continue to provide positioning information in the case of satellite signal loss or interference, playing a significant role.

[0003] Chinese Patent Publication No.: CN113447028A, discloses an inertial navigation system for a UAV and a UAV. The inertial navigation system of the UAV includes a flight control module, an inertial navigation module, a GPS module, and a visual navigation module; the GPS module is used to obtain GPS data during flight; the visual navigation module is used to obtain visual image data during flight; the flight control module is used to determine whether the signal strength of the GPS module is greater than a threshold, and when the signal strength of the GPS module is greater than the threshold, use the GPS data to correct the navigation data of the inertial navigation module; when the signal strength of the GPS module is less than or equal to the threshold, correct the navigation data of the inertial navigation module through the visual image data; the inertial navigation module is used to control the navigation flight of the UAV according to the corrected navigation data. Thus, the navigation of the UAV can be realized even when the signal is interfered.

[0004] Chinese Patent Publication No.: CN118565470A, discloses an inertial navigation system and navigation method based on a UAV. The invention uses multiple positioning methods for positioning operations, which is simple, reliable, and reduces computing power compared with similar technologies. When there is a GPS signal, accurate navigation information and position can be obtained through differential GPS. When the GPS signal is blocked, reliable positioning information can also be obtained through the inertial navigation positioning method and image comparison and verification. When the image comparison and verification fails, the navigation error is corrected through multi-source visual navigation assistance, and finally reliable positioning information can also be obtained, etc., which is suitable for large-scale promotion and application.

[0005] However, the following problems still exist in the prior art

[0006] In actual situations, although an inertial navigation system can provide high-frequency updates of navigation information, each update is based on the previous position and attitude information. Therefore, if the error of the unmanned aerial vehicle (UAV) is not judged and corrected, small errors will accumulate continuously during successive updates, resulting in a serious deviation of the UAV. Summary of the Invention

[0007] To this end, the present invention provides an inertial navigation method and system for a UAV to solve the problem that in actual situations, although an inertial navigation system can provide high-frequency updates of navigation information, each update is based on the previous position and attitude information. Therefore, if the error of the UAV is not judged and corrected, small errors will accumulate continuously during successive updates, resulting in a serious deviation of the UAV.

[0008] To achieve the above object, the present invention provides an inertial navigation method for a UAV, which includes:

[0009] Obtain the preset orbit position coordinates and the current orbit running position coordinates of the UAV, and determine the preset attitude and preset angular velocity of the UAV;

[0010] Based on the preset orbit position coordinates and the current orbit running position coordinates, determine the relative distance and offset angle of the UAV, and combine the trajectory complexity of the current navigation section to determine the error influence coefficient of the UAV, and judge whether it is necessary to correct the running information of the UAV;

[0011] In response to the result that the UAV needs to be corrected, determine the error accumulation variable of the UAV in the current navigation section, and determine the error accumulation category of the UAV to correct the running information of the UAV, where

[0012] In response to the initial error accumulation category, determine the current orbit running position as the update starting point position, and combine the navigation end position to reset the running information of the UAV;

[0013] In response to the running error accumulation category, trace the error point, determine the yaw angle based on the navigation section corresponding to the UAV at the error point, correct the yaw angle using Kalman filtering, determine the current orbit running position as the update starting point position, and combine the corrected yaw angle and the navigation end position to reset the running information of the UAV;

[0014] Determine that the UAV continues to travel along the preset orbit or the corrected orbit, and perform periodic monitoring on the UAV to timely correct the running information of the UAV.

[0015] Further, the process of determining the relative distance and offset angle of the UAV includes

[0016] Determine the coordinate distances between the preset orbital position coordinate points corresponding to each moment and the current orbital running position coordinate points;

[0017] Solve the average value of the coordinate distances corresponding to each moment to obtain the relative distance;

[0018] Determine the preset navigation section based on the preset orbital position coordinates, and determine the current navigation section based on the current orbital running position coordinates;

[0019] Determine the tangents of the preset orbital position coordinate points corresponding to each moment relative to the preset navigation section and the tangents of the current orbital running position coordinate points relative to the current navigation section, so as to solve the tangent angle;

[0020] Solve the average value of the tangent angles corresponding to each moment to obtain the offset angle.

[0021] Further, the process of determining the trajectory complexity of the current navigation section includes,

[0022] Determine the number of times the direction of the drone changes in the current navigation section;

[0023] Determine the ratio of the number of direction changes to the preset change number threshold as the trajectory complexity.

[0024] Further, the process of determining the error influence coefficient of the drone includes,

[0025] Determine the ratio of the relative distance to the reference relative distance as the first error influence factor;

[0026] Determine the ratio of the offset angle to the reference offset angle as the second error influence factor;

[0027] Determine the ratio of the trajectory complexity to the reference trajectory complexity as the third error influence factor;

[0028] Determine the weighted sum value of the first error influence factor, the second error influence factor and the third error influence factor as the error influence coefficient of the drone.

[0029] Further, determine whether it is necessary to correct the operation information of the drone, wherein,

[0030] If the error influence coefficient is greater than the preset error influence coefficient, it is necessary to correct the operation information of the drone;

[0031] If the error influence coefficient is less than or equal to the preset error influence coefficient, it is not necessary to correct the operation information of the drone, and it is determined that the drone continues to travel along the preset orbit.

[0032] Further, the process of determining the error accumulation variable of the drone in the current navigation section includes,

[0033] Divide the preset navigation segment into a plurality of preset navigation sub - segments;

[0034] Divide the current navigation segment into a plurality of current navigation sub - segments;

[0035] Determine whether the preset navigation sub - segment is offset relative to the current navigation sub - segment;

[0036] Determine the ratio of the number of currently offset navigation sub - segments to the total number of current navigation sub - segments as the error accumulation variable;

[0037] Wherein, if the current navigation sub - segment meets the angular velocity offset condition and / or the attitude offset condition, it is determined that the navigation sub - segment is offset.

[0038] Further, the process of determining the error accumulation category of the drone to correct the drone operation information, wherein,

[0039] If the error accumulation variable is greater than the preset error accumulation variable, determine that the error accumulation category of the drone is the initial error accumulation category;

[0040] If the error accumulation variable is less than or equal to the preset error accumulation variable, determine that the error accumulation category of the drone is the operation error accumulation category.

[0041] Further, the process of tracing the error point includes,

[0042] Determine the first offset navigation sub - segment that has an offset;

[0043] Determine the coordinate point corresponding to the start end of the offset navigation sub - segment as the error point.

[0044] Further, the process of determining the yaw angle based on the navigation segment corresponding to the error point of the drone includes,

[0045] Determine the first tangent line of the error point on the preset navigation segment;

[0046] Determine the second tangent line of the error point on the current navigation segment;

[0047] Determine the included angle between the first tangent line and the second tangent line as the yaw angle.

[0048] Further, there is also provided an inertial navigation system for a drone, characterized in that it includes:

[0049] An information acquisition module for acquiring the preset orbit position coordinates and the current orbit operation position coordinates of the drone, and determining the preset attitude and the preset angular velocity of the drone;

[0050] An error determination module, connected to the information acquisition module, is configured to determine the relative distance and offset angle of the drone based on the preset track position coordinates and the current track running position coordinates, determine the error influence coefficient of the drone in combination with the trajectory complexity of the current navigation segment, and determine whether it is necessary to correct the running information of the drone;

[0051] An error correction module, connected to the error determination module, is configured to, in response to the result that the drone needs to be corrected, determine the error accumulation variable of the drone in the current navigation segment, determine the error accumulation category of the drone, so as to correct the running information of the drone, where

[0052] In response to the initial error accumulation category, determine the current track running position as the update starting position, and combine with the navigation end position to reset the running information of the drone;

[0053] In response to the running error accumulation category, trace back to the error point, determine the yaw angle based on the navigation segment corresponding to the drone at the error point, correct the yaw angle using the Kalman filter, determine the current track running position as the update starting position, and combine with the corrected yaw angle and the navigation end position to reset the running information of the drone;

[0054] A continuous monitoring module, connected to the error correction module, is configured to determine that the drone continues to travel along the preset track or the corrected track, and perform periodic monitoring on the drone to timely correct the running information of the drone.

[0055] Compared with the prior art, the present invention determines the relative distance and offset angle of the drone based on the preset track position coordinates and the current track running position coordinates, determines the error influence coefficient in combination with the trajectory complexity of the current navigation segment, determines whether it is necessary to correct the running information of the drone, determines the error accumulation variable, determines the error accumulation category of the drone, so as to correct the running information of the drone, determines the current track running position as the update starting position, and combines with the navigation end position to reset the running information of the drone; traces back to the error point, determines the yaw angle based on the navigation segment corresponding to the drone at the error point, corrects the yaw angle using the Kalman filter, determines the current track running position as the update starting position, and combines with the corrected yaw angle and the navigation end position to reset the running information of the drone.

[0056] In particular, the error influence coefficient of the UAV is determined according to the relative distance, offset angle and trajectory complexity of the UAV, providing a data basis for subsequent judgment on whether to correct the operation information of the UAV. During the actual flight of the UAV, the inertial navigation system updates the current state by using the previous position and attitude information. Once a deviation occurs, these small errors will accumulate continuously. If the UAV state is not determined and intervened in time, it will lead to a greater deviation, thus affecting the flight accuracy and safety. Based on this, the present invention calculates the error influence coefficient of the UAV, and determines the UAVs that need to correct the operation information based on the error influence coefficient, so as to improve the flight accuracy and reliability of the UAV.

[0057] In particular, the error accumulation category of the UAV is determined through the error accumulation variable of the UAV, so as to specifically correct the operation information of the UAV. In actual situations, during the flight of the UAV, the offset error mainly stems from the misconfiguration of the initial operation information and the dynamic interference during the operation process. For different offset types, if the same correction method is adopted, it may lead to inaccurate correction of the operation information. Based on this, the present invention divides the error accumulation category of the UAV into two types. For the initial error accumulation category, it is corrected by methods such as readjusting parameter settings; for the operation error accumulation category, technologies such as updating the starting position and the Kalman filter algorithm are adopted for correction, improving the flight accuracy and reliability of the UAV. Description of the Drawings

[0058] Figure 1 Schematic diagram of the steps of the inertial navigation method for UAVs according to the embodiments of the invention;

[0059] Figure 2 Logic block diagram for judging whether to correct the operation information of the UAV according to the embodiments of the invention;

[0060] Figure 3 Logic block diagram for determining the error accumulation category of the UAV to correct the operation information of the UAV;

[0061] Figure 4 Schematic diagram of the structure of the inertial navigation system for UAVs according to the embodiments of the invention. Detailed Description of the Invention

[0062] In order to make the purpose and advantages of the present invention clearer and more understandable, the present invention will be further described below in conjunction with embodiments; it should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0063] The preferred embodiments of the present invention will be described below with reference to the accompanying drawings. Those skilled in the art should understand that these embodiments are only used to explain the technical principle of the present invention and do not limit the protection scope of the present invention.

[0064] Please refer to Figure 1 , Figure 1 which is a schematic diagram of the steps of an inertial navigation method for an unmanned aerial vehicle according to an embodiment of the invention. The inertial navigation method for an unmanned aerial vehicle of the present invention includes:

[0065] Step S1, obtaining the preset orbit position coordinates and the current orbit running position coordinates of the unmanned aerial vehicle, and determining the preset attitude and the preset angular velocity of the unmanned aerial vehicle. It can be understood that the preset attitude includes a preset pitch angle, a preset roll angle, and a preset yaw angle;

[0066] Step S2, determining the relative distance and the offset angle of the unmanned aerial vehicle based on the preset orbit position coordinates and the current orbit running position coordinates, combining the trajectory complexity of the current navigation segment to determine the error influence coefficient of the unmanned aerial vehicle, and determining whether it is necessary to correct the running information of the unmanned aerial vehicle;

[0067] Step S3, in response to the result that the unmanned aerial vehicle needs to be corrected, determining the error accumulation variable of the unmanned aerial vehicle in the current navigation segment, and determining the error accumulation category of the unmanned aerial vehicle to correct the running information of the unmanned aerial vehicle, where

[0068] in response to the initial error accumulation category, determining the current orbit running position as the updated starting point position, and combining the navigation end position to reset the running information of the unmanned aerial vehicle;

[0069] in response to the running error accumulation category, tracing the error point, determining the yaw angle based on the navigation segment corresponding to the unmanned aerial vehicle at the error point, correcting the yaw angle by using the Kalman filter, determining the current orbit running position as the updated starting point position, and combining the corrected yaw angle and the navigation end position to reset the running information of the unmanned aerial vehicle;

[0070] Step S4, determining that the unmanned aerial vehicle continues to travel along the preset orbit or the corrected orbit, and performing periodic monitoring on the unmanned aerial vehicle to timely correct the running information of the unmanned aerial vehicle.

[0071] Specifically, the current orbit running position coordinates are the coordinates of several points on the current orbit, and the preset orbit position coordinates are the coordinates of several points on the preset orbit.

[0072] Specifically, there are no restrictions on the acquisition methods of the current orbital position coordinates and the preset orbital position coordinates. For example, the current orbital position coordinates can be obtained based on the positioning system of the UAV, and the preset orbital position coordinates are obtained based on the flight control system of the UAV to determine the preset orbit that the UAV needs to fly, and then further determine the coordinates of each point on the orbit. Of course, other methods can also be adopted, which will not be elaborated here.

[0073] Specifically, in implementation, the navigation segment is a time period considered from the time domain dimension, which will not be elaborated here.

[0074] Specifically, there are no restrictions on the specific implementation method of correcting the yaw angle using the Kalman filter algorithm. For example, the Kalman filter can be used to correct the yaw angle. The Kalman filter initializes the UAV state vector and covariance matrix according to the initial measurement value, predicts the yaw angle at the next moment using the angular velocity measured by the gyroscope, combines the measurement values of other sensors such as the magnetometer, and corrects the predicted yaw angle using the Kalman gain. Of course, those skilled in the art can also adopt other methods to correct the yaw angle, which will not be elaborated here.

[0075] Specifically, the UAV operation information includes the preset orbit, operating speed, and operating attitude of the UAV, which will not be elaborated here.

[0076] Specifically, for the case where there are updated starting point positions and navigation end point positions, the UAV can use the RRT algorithm to plan the path. By randomly sampling in the environmental space and gradually constructing a tree to explore the environment to find a feasible path from the starting point to the end point. It can be understood that the UAV autonomous flight system can convert the planned path into instructions executable by the UAV. By converting the continuous path curve or broken line into a series of discrete waypoints, each waypoint contains the position information, speed information, and heading information of the UAV. According to the position information of the waypoints, position instructions can be generated; according to the heading information of the UAV, operating attitude instructions can be generated.

[0077] Specifically, there are no restrictions on the predetermined period of periodic monitoring. In implementation, the period can be set to 5s to 10s to ensure the timeliness of orbit monitoring. Of course, those skilled in the art can adjust the set period according to the actual situation, which will not be elaborated here.

[0078] Specifically, the process of determining the relative distance and offset angle of the UAV includes

[0079] Determining the coordinate distances between the preset orbital position coordinate points and the current orbital operation position coordinate points corresponding to each moment;

[0080] Solving the mean value of the coordinate distances corresponding to each moment to obtain the relative distance;

[0081] Determine a preset navigation segment based on preset orbital position coordinates, and determine the current navigation segment based on the current orbital operation position coordinates;

[0082] Determine the tangents of the preset orbital position coordinate points corresponding to each moment relative to the preset navigation segment and the tangents of the current orbital operation position coordinate points relative to the current navigation segment to solve for the tangent angle;

[0083] Solve the average value of the tangent angles corresponding to each moment to obtain the offset angle.

[0084] It can be understood that the current navigation segment and the preset navigation segment correspond in the time domain relationship, which will not be elaborated here.

[0085] It can be understood that the preset orbital position coordinate points and the current orbital operation position coordinate points are placed in the same coordinate system, and then the coordinate distance is calculated, which will not be elaborated here.

[0086] It can be understood that the preset navigation segment and the current navigation segment correspond in the time domain dimension, that is, in the case where the drone does not deviate, the coordinates corresponding to the preset navigation segment and the current navigation segment at each moment should be the same.

[0087] Specifically, the process of determining the trajectory complexity of the current navigation segment includes,

[0088] Determine the number of direction changes of the drone in the current navigation segment;

[0089] Determine the ratio of the number of direction changes to the preset change number threshold as the trajectory complexity.

[0090] It can be understood that the higher the complexity of the orbit, the easier it is to exacerbate the deviation phenomenon of the drone when the drone deviates.

[0091] Specifically, during the flight process of the drone corresponding to the current navigation segment, when the angle of the flight direction changes relative to the previous moment is greater than the preset threshold, it is counted as a direction change. In practice, the preset threshold is set to 5 degrees, and the interval of the moments participating in the comparison is set to 0.5 s.

[0092] Specifically, the preset change number threshold represents the maximum number of times the direction of the drone can change. Therefore, the historical number of direction changes of the drone in several navigation segments is obtained in advance, and 1.5 times the average value of the historical number of direction changes is determined as the preset change number threshold.

[0093] Specifically, the process of determining the error influence coefficient of the drone includes,

[0094] Determine the ratio of the relative distance to the reference relative distance as the first error influence factor;

[0095] Determine the ratio of the offset angle to the reference offset angle as the second error influence factor;

[0096] Determine the ratio of the trajectory complexity to the reference trajectory complexity as the third error influence factor;

[0097] Determine the weighted sum value of the first error influence factor, the second error influence factor, and the third error influence factor as the error influence coefficient of the UAV.

[0098] Specifically, the reference relative distance is calculated in advance. Obtain the historical relative distances of the corresponding navigation segments during several flights of the UAV in advance; determine the average value of the historical relative distances as the reference relative distance.

[0099] Specifically, the reference offset angle is calculated in advance. Obtain the historical offset angles of the corresponding navigation segments during several flights of the UAV in advance; determine the average value of the historical offset angles as the reference offset angle.

[0100] Specifically, the reference trajectory complexity is calculated in advance. Obtain the historical trajectory complexities of the corresponding navigation segments during several flights of the UAV in advance; determine the average value of the historical trajectory complexities as the reference trajectory complexity.

[0101] Specifically, the sum of the weight coefficients of the first error influence factor, the second error influence factor, and the third error influence factor is 1. The weight coefficient of the first error influence factor is 0.32, the weight coefficient of the second error influence factor is 0.31, and the weight coefficient of the third error influence factor is 0.37.

[0102] Please refer to Figure 2 , Figure 2 which is the logic block diagram for judging whether it is necessary to correct the operation information of the UAV in the invention embodiment. Specifically, judge whether it is necessary to correct the operation information of the UAV, where

[0103] if the error influence coefficient is greater than the preset error influence coefficient, it is necessary to correct the operation information of the UAV;

[0104] if the error influence coefficient is less than or equal to the preset error influence coefficient, there is no need to correct the operation information of the UAV, and it is determined that the UAV continues to travel along the preset orbit.

[0105] Specifically, the preset error influence coefficient characterizes whether the UAV maintains operation on the preset orbit. Therefore, the preset error influence coefficient is set to be selected within the interval [0.81, 0.96].

[0106] Specifically, the error influence coefficient of the drone is determined according to the relative distance, offset angle and trajectory complexity of the drone, providing a data basis for subsequent judgment on whether to correct the operation information of the drone. During the actual flight of the drone, the inertial navigation system updates the current state by using the previous position and attitude information. Once a deviation occurs, these small errors will continuously accumulate. If the drone state is not determined and intervened in a timely manner, it will lead to a greater deviation, thereby affecting the flight accuracy and safety. Based on this, the present invention calculates the error influence coefficient of the drone and determines the drones that need to correct the operation information based on the error influence coefficient, so as to improve the flight accuracy and reliability of the drone.

[0107] Specifically, the process of determining the error accumulation variable of the drone in the current navigation segment includes

[0108] Dividing the preset navigation segment into a plurality of preset navigation sub-segments;

[0109] Dividing the current navigation segment into a plurality of current navigation sub-segments;

[0110] Determining whether the preset navigation sub-segment is offset relative to the current navigation sub-segment;

[0111] Determining the ratio of the number of current offset navigation sub-segments to the total number of current navigation sub-segments as the error accumulation variable;

[0112] Wherein, if the current navigation sub-segment satisfies the angular velocity offset condition and / or the attitude offset condition, it is determined that the navigation sub-segment is offset.

[0113] Specifically, setting the angular velocity offset condition as the difference ratio between the current angular velocity corresponding to any moment in the current navigation sub-segment and the preset angular velocity corresponding to the corresponding preset navigation sub-segment is greater than a predetermined angular velocity difference ratio threshold;

[0114] Setting the attitude offset condition as the angle change amount of any one of the current pitch angle, current roll angle and current yaw angle corresponding to any moment in the current navigation sub-segment exceeds a predetermined angle change amount threshold.

[0115] Specifically, the predetermined angular velocity difference ratio threshold is set to 0.3 times of the preset angular velocity; the predetermined angle change amount threshold is set to 0.2 times of the sum average of the preset pitch angle, preset roll angle and preset yaw angle.

[0116] Please refer to Figure 3 , Figure 3 For the logic block diagram of determining the error accumulation category of the drone in the embodiment of the invention to correct the operation information of the drone. Specifically, determining the error accumulation category of the drone to correct the operation information of the drone, wherein,

[0117] If the error accumulation variable is greater than a preset error accumulation variable, determine that the error accumulation category of the UAV is the initial error accumulation category;

[0118] If the error accumulation variable is less than or equal to the preset error accumulation variable, determine that the error accumulation category of the UAV is the running error accumulation category.

[0119] Specifically, the preset error accumulation variable characterizes the initial time when the UAV has an error, so the preset error accumulation variable is set to be selected within the interval [0.87, 0.99].

[0120] Specifically, the error accumulation category of the UAV is determined through the error accumulation variable of the UAV, so as to specifically correct the operation information of the UAV. In actual situations, during the flight of the UAV, the deviation error mainly stems from the initial operation information setting error and the dynamic interference during the operation process. For different deviation types, if the same correction method is used, it may lead to inaccurate correction of the operation information. Based on this, the present invention divides the error accumulation category of the UAV into two types. For the initial error accumulation category, it is corrected by methods such as readjusting parameter settings; for the running error accumulation category, technologies such as updating the starting point position and the Kalman filtering algorithm are used for correction to improve the flight accuracy and reliability of the UAV.

[0121] Specifically, the process of tracing the error point includes

[0122] Determine the offset navigation sub-segment where the first offset occurs;

[0123] Determine the coordinate point corresponding to the head end of the offset navigation sub-segment as the error point.

[0124] Specifically, the process of determining the yaw angle based on the navigation segment corresponding to the error point of the UAV includes

[0125] Determine the first tangent line of the error point on the preset navigation segment;

[0126] Determine the second tangent line of the error point on the current navigation segment;

[0127] Determine the included angle between the first tangent line and the second tangent line as the yaw angle.

[0128] Please refer to Figure 4 , Figure 4 is a schematic structural diagram of an inertial navigation system for a UAV according to an embodiment of the invention. The system for an inertial navigation method for a UAV is characterized by including:

[0129] An information acquisition module, configured to acquire the preset orbital position coordinates and the current orbital operation position coordinates of the drone, and determine the preset attitude and preset angular velocity of the drone;

[0130] An error determination module, connected to the information acquisition module, configured to determine the relative distance and offset angle of the drone based on the preset orbital position coordinates and the current orbital operation position coordinates, combine the trajectory complexity of the current navigation segment to determine the error influence coefficient of the drone, and determine whether it is necessary to correct the operation information of the drone;

[0131] An error correction module, connected to the error determination module, configured to, in response to the result that the drone needs to be corrected, determine the error accumulation variable of the drone in the current navigation segment, determine the error accumulation category of the drone, so as to correct the operation information of the drone, wherein,

[0132] In response to the initial error accumulation category, determine the current orbital operation position as the updated starting point position, and combine the navigation end position to reset the operation information of the drone;

[0133] In response to the operation error accumulation category, trace back to the error point, determine the yaw angle based on the navigation segment corresponding to the drone at the error point, correct the yaw angle using Kalman filtering, determine the current orbital operation position as the updated starting point position, and combine the corrected yaw angle and the navigation end position to reset the operation information of the drone;

[0134] A continuous monitoring module, connected to the error correction module, configured to determine that the drone continues to travel along the preset orbit or the corrected orbit, and perform periodic monitoring on the drone to timely correct the operation information of the drone.

[0135] So far, the technical solution of the present invention has been described in conjunction with the preferred embodiments shown in the accompanying drawings. However, it is easy for those skilled in the art to understand that the protection scope of the present invention is obviously not limited to these specific embodiments. Without departing from the principle of the present invention, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will all fall within the protection scope of the present invention.

[0136] The above are only the preferred embodiments of the present invention and are not used to limit the present invention; for those skilled in the art, the present invention can have various changes and modifications. Any modification, equivalent substitution, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. An inertial navigation method for an unmanned aerial vehicle, characterized in that, Including: Obtain the preset orbital position coordinates and the current orbital position coordinates of the drone, and determine the preset attitude and preset angular velocity of the drone; Based on the preset orbital position coordinates and the current orbital position coordinates, determine the relative distance and offset angle of the drone, and combine the trajectory complexity of the current navigation section to determine the error influence coefficient of the drone, and judge whether it is necessary to correct the operation information of the drone; In response to the result that the drone needs to be corrected, determine the error accumulation variable of the drone in the current navigation section, and determine the error accumulation category of the drone, so as to correct the operation information of the drone, where In response to the initial error accumulation category, determine the current orbital position as the updated starting position, and combine the navigation end position to reset the operation information of the drone; In response to the operation error accumulation category, trace back the error point, determine the yaw angle based on the navigation section corresponding to the drone at the error point, use the Kalman filter to correct the yaw angle, determine the current orbital position as the updated starting position, and combine the corrected yaw angle and the navigation end position to reset the operation information of the drone; Determine that the drone continues to travel along the preset orbit or the corrected orbit, and perform periodic monitoring on the drone to timely correct the operation information of the drone; The process of determining the error influence coefficient of the drone includes Determine the ratio of the relative distance to the reference relative distance as the first error influence factor; Determine the ratio of the offset angle to the reference offset angle as the second error influence factor; Determine the ratio of the trajectory complexity to the reference trajectory complexity as the third error influence factor; Determine the weighted sum value of the first error influence factor, the second error influence factor and the third error influence factor as the error influence coefficient of the drone.

2. The inertial navigation method for an unmanned aerial vehicle according to claim 1, characterized in that The process of determining the relative distance and offset angle of the drone includes Determine the coordinate distances of the preset orbital position coordinate points and the current orbital position coordinate points corresponding to each moment; Solve the average value of the coordinate distances corresponding to each moment to obtain the relative distance; Determine the preset navigation section based on the preset orbital position coordinates, and determine the current navigation section based on the current orbital position coordinates; Determine the tangents of the preset orbital position coordinate points relative to the preset navigation section and the tangents of the current orbital position coordinate points relative to the current navigation section corresponding to each moment, so as to solve the tangent angle; Solve the average value of the tangent angles corresponding to each moment to obtain the offset angle.

3. The inertial navigation method for an unmanned aerial vehicle according to claim 1, characterized in that, The process of determining the trajectory complexity of the current navigation section includes Determine the number of direction changes of the drone in the current navigation section; Determine the ratio of the number of direction changes to the preset change number threshold as the trajectory complexity.

4. The inertial navigation method for an unmanned aerial vehicle according to claim 1, characterized in that, Judging whether it is necessary to correct the operation information of the drone, where If the error influence coefficient is greater than the preset error influence coefficient, it is necessary to correct the operation information of the drone; If the error influence coefficient is less than or equal to the preset error influence coefficient, it is not necessary to correct the operation information of the drone, and it is determined that the drone continues to travel along the preset orbit.

5. The inertial navigation method for an unmanned aerial vehicle according to claim 2, characterized in that, The process of determining the error accumulation variable of the drone in the current navigation section includes Divide the preset navigation segment into a number of preset navigation sub - segments; Divide the current navigation segment into a number of current navigation sub - segments; Determine whether the preset navigation sub - segment is offset relative to the current navigation sub - segment; Determine the ratio of the number of currently offset navigation sub - segments to the total number of current navigation sub - segments as the error accumulation variable; Wherein, if the current navigation sub - segment meets the angular velocity offset condition and / or the attitude offset condition, it is determined that the navigation sub - segment is offset.

6. The inertial navigation method for an unmanned aerial vehicle according to claim 1, characterized in that, Determine the error accumulation category of the UAV to correct the UAV operation information, wherein, If the error accumulation variable is greater than the preset error accumulation variable, determine that the error accumulation category of the UAV is the initial error accumulation category; If the error accumulation variable is less than or equal to the preset error accumulation variable, determine that the error accumulation category of the UAV is the operation error accumulation category.

7. The inertial navigation method for an unmanned aerial vehicle according to claim 5, characterized in that The process of tracing the error point includes, Determine the first offset navigation sub - segment that occurs; Determine the coordinate point corresponding to the head end of the offset navigation sub - segment as the error point.

8. The inertial navigation method for an unmanned aerial vehicle according to claim 5, wherein, The process of determining the yaw angle based on the navigation segment corresponding to the UAV at the error point includes, Determine the first tangent line of the error point on the preset navigation segment; Determine the second tangent line of the error point on the current navigation segment; Determine the included angle between the first tangent line and the second tangent line as the yaw angle.

9. A system applying the inertial navigation method for an unmanned aerial vehicle according to any one of claims 1-8, characterized in that, Includes: An information acquisition module for acquiring the preset orbit position coordinates and the current orbit operation position coordinates of the UAV, and determining the preset attitude and preset angular velocity of the UAV; An error determination module connected to the information acquisition module for determining the relative distance and offset angle of the UAV based on the preset orbit position coordinates and the current orbit operation position coordinates, combining the trajectory complexity of the current navigation segment to determine the error influence coefficient of the UAV, and judging whether it is necessary to correct the operation information of the UAV; An error correction module connected to the error determination module for responding to the result that the UAV needs to be corrected, determining the error accumulation variable of the UAV in the current navigation segment, determining the error accumulation category of the UAV to correct the UAV operation information, wherein, In response to the initial error accumulation category, determine the current orbit operation position as the updated starting position, and combine with the navigation end position to reset the UAV operation information; In response to the operation error accumulation category, trace the error point, determine the yaw angle based on the navigation segment corresponding to the UAV at the error point, correct the yaw angle using the Kalman filter, determine the current orbit operation position as the updated starting position, and combine with the corrected yaw angle and the navigation end position to reset the operation information of the UAV; A continuous monitoring module connected to the error correction module for determining that the UAV continues to travel along the preset orbit or the corrected orbit, and performing periodic monitoring on the UAV to timely correct the UAV operation information.

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