Inertial navigation method and system for unmanned aerial vehicle

Abstract

The invention relates to the technical field of navigation, in particular to an inertial navigation method and system for an unmanned aerial vehicle, and the method comprises the steps: determining a relative distance and a deviation included angle of the unmanned aerial vehicle based on a preset orbit position coordinate and a current orbit operation position coordinate, and determining an error influence coefficient in combination with the trajectory complexity of a current navigation segment; judging whether the operation information of the unmanned aerial vehicle needs to be corrected or not, determining an error accumulation variable, determining an error accumulation category of the unmanned aerial vehicle, determining a current orbit operation position as an update starting point position, and resetting the operation information of the unmanned aerial vehicle in combination with a navigation terminal point position; tracing an error point, determining a yaw angle based on the unmanned aerial vehicle in a navigation segment corresponding to the error point, correcting the yaw angle by using Kalman filtering, determining a current orbit operation position as an update starting point position, and resetting operation information of the unmanned aerial vehicle in combination with the corrected yaw angle and a navigation terminal point position. And the flight precision and reliability of the unmanned aerial vehicle are improved.

Description

Technical Field

[0001] The present invention relates to the field of navigation technology, and in particular to an inertial navigation method and system for unmanned aerial vehicles. Background Art

[0002] The inertial navigation of UAVs is one of the core technologies for achieving autonomous flight and precise navigation of UAVs. It is an autonomous navigation technology that does not rely on external signals. The core of the system is the sensor, including accelerometers and gyroscopes. It measures the linear acceleration and angular velocity of the UAV through the accelerometers and gyroscopes on the UAV, and combines the initial position, attitude and speed information to calculate the current position, speed 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 event of satellite signal loss or interference, playing a significant role.

[0003] Chinese patent publication number: CN113447028A, discloses an inertial navigation system of a UAV and a UAV, wherein 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 in flight; the visual navigation module is used to obtain visual image data in flight; the flight control module is used to determine whether the signal strength of the GPS module is greater than a threshold, and use the GPS data to correct the navigation data of the inertial navigation module when the signal strength of the GPS module is greater than the threshold; the navigation data of the inertial navigation module is corrected by the visual image data when the signal strength of the GPS module is less than or equal to the threshold; the inertial navigation module is used to control the navigation flight of the UAV according to the corrected navigation data. In this way, navigation of the UAV can be achieved even when the signal is interfered.

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

[0005] However, there are still the following problems in the prior art: In actual situations, although the inertial navigation system can provide high-frequency navigation information updates, each update is based on the previous position and attitude information. Therefore, if the drone is not judged and corrected for errors, small errors will be accumulated in continuous updates, causing the drone to deviate seriously. Summary of the invention

[0006] To this end, the present invention provides an inertial navigation method and system for a UAV, so as to solve the problem that in actual situations, although the inertial navigation system can provide high-frequency navigation information updates, each update is based on the previous position and attitude information. Therefore, if the UAV is not subjected to error judgment and correction, small errors will be continuously accumulated in continuous updates, causing serious deviation of the UAV.

[0007] To achieve the above object, the present invention provides an inertial navigation method for a drone, comprising: Obtain the preset orbit position coordinates and current orbit position coordinates of the drone, and determine the preset attitude and preset angular velocity of the drone; Determine the relative distance and offset angle of the UAV based on the preset orbit position coordinates and the current orbit operation position coordinates, determine the error influence coefficient of the UAV in combination with the trajectory complexity of the current navigation segment, and determine whether the operation information of the UAV needs to be corrected; In response to the result that the drone needs to be corrected, the error accumulation variable of the drone in the current navigation segment is determined, and the error accumulation category of the drone is determined to correct the drone operation information, wherein: In response to the initial error accumulation category, determining the current orbital operation position as the update starting position, combined with the navigation end position, to reset the UAV operation information; In response to the operation error accumulation category, the error point is traced back, the yaw angle is determined based on the navigation segment corresponding to the error point of the UAV, the yaw angle is corrected by using Kalman filtering, the current orbit operation position is determined as the update starting position, and the operation information of the UAV is reset in combination with the corrected yaw angle and the navigation end position; Ensure that the UAV continues to travel on the preset or corrected track, and maintain periodic monitoring of the UAV to timely correct the UAV operation information.

[0008] Furthermore, the process of determining the relative distance and offset angle of the UAV includes: Determine the coordinate distance between the preset orbital position coordinate point corresponding to each moment and the current orbital running position coordinate point; Solve the mean value of the coordinate distance corresponding to each moment to obtain the relative distance; Determine a preset navigation segment based on the preset orbit position coordinates, and determine a current navigation segment based on the current orbit running position coordinates; Determine the tangent of the preset orbit position coordinate point corresponding to each moment relative to the preset navigation segment and the tangent of the current orbit running position coordinate point relative to the current navigation segment to solve the tangent angle; The mean value of the tangent angle corresponding to each moment is solved to obtain the offset angle.

[0009] Furthermore, the process of determining the trajectory complexity of the current navigation segment includes: Determine the number of direction changes of the drone in the current navigation segment; The ratio of the number of direction changes to a preset change number threshold is determined as the trajectory complexity.

[0010] Furthermore, the process of determining the error influence coefficient of the drone includes: Determine a ratio of the relative distance to a reference relative distance as a first error influencing factor; Determine a ratio of the offset angle to a reference offset angle as a second error influencing factor; Determining a ratio of the trajectory complexity to the reference trajectory complexity as a third error influencing factor; Determine a weighted sum 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.

[0011] Furthermore, the determination of whether the operation information of the UAV needs to be corrected includes: If the error influence coefficient is greater than the preset error influence coefficient, the operation information of the UAV needs to be corrected; 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 the UAV is determined to maintain the preset track and continue to travel.

[0012] Furthermore, the process of determining the error accumulation variable of the UAV in the current navigation segment includes: Dividing the preset navigation segment into a plurality of preset navigation sub-segments; Dividing the current navigation segment into a plurality 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 current offset navigation sub-segment number to the current navigation sub-segment total number as an error accumulation variable; 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.

[0013] Furthermore, the error accumulation category of the drone is determined to correct the drone operation information, wherein: If the error accumulation variable is greater than a preset error accumulation variable, determining that the error accumulation category of the UAV is an initial error accumulation category; If the error accumulation variable is less than or equal to a preset error accumulation variable, the error accumulation category of the UAV is determined to be an operating error accumulation category.

[0014] Furthermore, the process of tracing the error point includes: Determine the first offset navigation subsegment where the offset occurs; The coordinate point corresponding to the head end of the offset navigation sub-segment is determined as the error point.

[0015] Furthermore, the process of determining the yaw angle based on the drone at the navigation segment corresponding to the error point includes: Determining a first tangent line of the error point on a preset navigation segment; Determine a second tangent line of the error point on the current navigation segment; An angle between the first tangent line and the second tangent line is determined as a yaw angle.

[0016] Furthermore, an inertial navigation system for a drone is provided, characterized in that it comprises: An information acquisition module is used to obtain the preset orbit position coordinates and current orbit position coordinates of the drone, and determine the preset attitude and preset angular velocity of the drone; An error determination module, which is connected to the information acquisition module, is used to determine the relative distance and offset angle of the UAV based on the preset orbit position coordinates and the current orbit operation position coordinates, determine the error influence coefficient of the UAV in combination with the trajectory complexity of the current navigation segment, and determine whether the operation information of the UAV needs to be corrected; An error correction module is connected to the error determination module, and is used to determine the error accumulation variable of the drone in the current navigation segment and the error accumulation category of the drone in response to the result that the drone needs to be corrected, so as to correct the drone operation information, wherein: In response to the initial error accumulation category, determining the current orbital operation position as the update starting position, combined with the navigation end position, to reset the UAV operation information; In response to the operation error accumulation category, the error point is traced back, the yaw angle is determined based on the navigation segment corresponding to the error point of the UAV, the yaw angle is corrected by using Kalman filtering, the current orbit operation position is determined as the update starting position, and the operation information of the UAV is reset in combination with the corrected yaw angle and the navigation end position; The continuous monitoring module is connected to the error correction module to ensure that the UAV continues to travel on a preset track or a corrected track, and to periodically monitor the UAV and timely correct the UAV operation information.

[0017] Compared with the prior art, the present invention determines the relative distance and offset angle of the UAV based on the preset orbital position coordinates and the current orbital operation 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 operation information of the UAV, determines the error accumulation variable, determines the error accumulation category of the UAV to correct the operation information of the UAV, determines the current orbital operation position as the update starting position, and resets the UAV operation information in combination with the navigation end position; traces the error point, determines the yaw angle based on the navigation segment corresponding to the UAV at the error point, uses Kalman filtering to correct the yaw angle, determines the current orbital operation position as the update starting position, and resets the UAV operation information in combination with the corrected yaw angle and the navigation end position.

[0018] 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 the operation information of the UAV needs to be corrected. In the actual flight of the UAV, the inertial navigation system updates the current state by using the previous position and attitude information. Once deviations begin to occur, these small errors will continue to accumulate. If the UAV state is not determined and intervened in time, larger deviations will result, thereby affecting the accuracy and safety of the flight. Based on this, the present invention calculates the error influence coefficient of the UAV, and determines the UAV whose operation information needs to be corrected based on the error influence coefficient, so as to improve the flight accuracy and reliability of the UAV.

[0019] In particular, the error accumulation category of the UAV is determined through the error accumulation variable of the UAV, so as to correct the operation information of the UAV in a targeted manner. In actual situations, the offset error of the UAV during flight is mainly caused by errors in the initial operation information setting and dynamic interference during operation. If the same correction method is used for different offset types, inaccurate correction of the operation information may result. Based on this, the present invention divides the error accumulation category of the UAV into two categories. For the initial error accumulation category, correction is performed by re-adjusting parameter settings and the like; for the operation error accumulation category, correction is performed by using technologies such as updating the starting point position and the Kalman filter algorithm, so as to improve the flight accuracy and reliability of the UAV. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1 A schematic diagram of the steps of an inertial navigation method for a drone according to an embodiment of the invention; Figure 2 A logic block diagram for determining whether it is necessary to modify the operation information of the drone according to an embodiment of the invention; Figure 3 A logic block diagram for determining the error accumulation category of the drone to correct the drone operation information according to an embodiment of the invention; Figure 4 The figure is a schematic diagram of the structure of an inertial navigation system for a drone according to an embodiment of the invention. DETAILED DESCRIPTION

[0021] In order to make the objects and advantages of the present invention more clearly understood, the present invention is 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.

[0022] The preferred embodiments of the present invention are described below with reference to the accompanying drawings. It should be understood by those skilled in the art that these embodiments are only used to explain the technical principles of the present invention and are not intended to limit the protection scope of the present invention.

[0023] See also Figure 1 , Figure 1 The inertial navigation method for a drone of the present invention is a schematic diagram of the steps of the inertial navigation method for a drone of the present invention. The inertial navigation method for a drone of the present invention comprises: Step S1, obtaining the preset orbit position coordinates and the current orbit running position coordinates of the drone, and determining the preset attitude and preset angular velocity of the drone. It can be understood that the preset attitude includes a preset pitch angle, a preset roll angle, and a preset yaw angle; Step S2, determining the relative distance and offset angle of the UAV based on the preset orbit position coordinates and the current orbit operation position coordinates, determining the error influence coefficient of the UAV in combination with the trajectory complexity of the current navigation segment, and judging whether it is necessary to correct the operation information of the UAV; Step S3, in response to the result that the drone needs to be corrected, determining the error accumulation variable of the drone in the current navigation segment, determining the error accumulation category of the drone, so as to correct the drone operation information, wherein: In response to the initial error accumulation category, determining the current orbital operation position as the update starting position, combined with the navigation end position, to reset the UAV operation information; In response to the operation error accumulation category, the error point is traced back, the yaw angle is determined based on the navigation segment corresponding to the error point of the UAV, the yaw angle is corrected by using Kalman filtering, the current orbit operation position is determined as the update starting position, and the operation information of the UAV is reset in combination with the corrected yaw angle and the navigation end position; Step S4, determining whether the UAV continues to travel on the preset track or the corrected track, and maintaining periodic monitoring of the UAV to timely correct the UAV operation information.

[0024] 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.

[0025] Specifically, there is no limitation on the method of obtaining 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 can be 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 repeated here.

[0026] Specifically, in implementation, the navigation segment is a time period considered from the time domain dimension, which will not be elaborated here.

[0027] Specifically, there is no limitation on the specific implementation method of correcting the yaw angle using the Kalman filter algorithm. For example, the yaw angle can be corrected by using a Kalman filter. The Kalman filter initializes the drone state vector and covariance matrix according to the initial measurement value, and uses the angular velocity measured by the gyroscope to predict the yaw angle at the next moment. The Kalman gain is used to correct the predicted yaw angle in combination with the measurement values ​​of other sensors such as a magnetometer. Of course, those skilled in the art may also use other methods to correct the yaw angle, which will not be repeated here.

[0028] Specifically, the drone operation information includes the drone's preset trajectory, operating speed, and operating posture, which will not be elaborated here.

[0029] Specifically, in the case of updating the starting point position and the navigation end point position, the UAV can use the RRT algorithm to plan the path, randomly sample in the environment space, and gradually build 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 executable instructions for the UAV by converting continuous path curves or broken lines into a series of discrete waypoints, each waypoint contains the UAV's position information, speed information and heading information. According to the position information of the waypoint, the position instruction can be generated; according to the heading information of the UAV, the running attitude instruction can be generated.

[0030] Specifically, there is no limit on the predetermined period of periodic monitoring. In implementation, the period can be set to 5s to 10s to ensure the timeliness of track monitoring. Of course, those skilled in the art can adjust the set period according to actual conditions, which will not be repeated here.

[0031] Specifically, the process of determining the relative distance and offset angle of the drone includes: Determine the coordinate distance between the preset orbital position coordinate point corresponding to each moment and the current orbital running position coordinate point; Solve the mean value of the coordinate distance corresponding to each moment to obtain the relative distance; Determine a preset navigation segment based on the preset orbit position coordinates, and determine a current navigation segment based on the current orbit running position coordinates; Determine the tangent of the preset orbit position coordinate point corresponding to each moment relative to the preset navigation segment and the tangent of the current orbit running position coordinate point relative to the current navigation segment to solve the tangent angle; The mean value of the tangent angle corresponding to each moment is solved to obtain the offset angle.

[0032] It can be understood that the current navigation segment and the preset navigation segment correspond to each other in the time domain, which will not be elaborated here.

[0033] It is understandable that the preset track position coordinate point and the current track running position coordinate point are placed in the same coordinate system to calculate the coordinate distance, which will not be repeated here.

[0034] It can be understood that the preset navigation segment and the current navigation segment correspond to each other in the time domain dimension, that is, when 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.

[0035] Specifically, the process of determining the trajectory complexity of the current navigation segment includes: Determine the number of direction changes of the drone in the current navigation segment; The ratio of the number of direction changes to a preset change number threshold is determined as the trajectory complexity.

[0036] It is understandable that the more complex the track is, the more likely it is that the drone will deviate when it does.

[0037] Specifically, during the flight process corresponding to the current navigation segment, when the angle of change in the flight direction relative to the direction at the previous moment exceeds a preset threshold, it is counted as a direction change. In implementation, the preset threshold is set to 5 degrees, and the interval between the moments involved in the comparison is set to 0.5s.

[0038] Specifically, the preset change count threshold represents the maximum number of times the drone's direction can change, so 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 direction change times is determined as the preset change count threshold.

[0039] Specifically, the process of determining the error influence coefficient of the UAV includes: Determine a ratio of the relative distance to a reference relative distance as a first error influencing factor; Determine a ratio of the offset angle to a reference offset angle as a second error influencing factor; Determining a ratio of the trajectory complexity to the reference trajectory complexity as a third error influencing factor; Determine a weighted sum 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.

[0040] Specifically, the reference relative distance is calculated in advance, and the historical relative distances of the corresponding navigation segments during several flights of the UAV are obtained in advance; the average value of the historical relative distances is determined as the reference relative distance.

[0041] Specifically, the reference offset angle is calculated in advance, and the historical offset angles of the corresponding navigation segments during several flights of the UAV are obtained in advance; the average value of the historical offset angles is determined as the reference offset angle.

[0042] Specifically, the baseline trajectory complexity is calculated in advance, and the historical trajectory complexity of the corresponding navigation segment during several flights of the UAV is obtained in advance; the average value of the historical trajectory complexity is determined as the baseline trajectory complexity.

[0043] 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.

[0044] See also Figure 2 , Figure 2 The following is a logic block diagram of an embodiment of the invention for determining whether the operation information of a drone needs to be corrected. Specifically, the following is a logic block diagram of an embodiment of the invention for determining whether the operation information of a drone needs to be corrected. If the error influence coefficient is greater than the preset error influence coefficient, the operation information of the UAV needs to be corrected; 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 the UAV is determined to maintain the preset track and continue to travel.

[0045] Specifically, the preset error influence coefficient represents whether the UAV keeps running on the preset track, so the preset error influence coefficient is set to be selected within the interval [0.81, 0.96].

[0046] Specifically, the error influence coefficient of the UAV is determined according to the relative distance, offset angle and trajectory complexity of the UAV, so as to provide a data basis for the subsequent judgment on whether the operation information of the UAV needs to be corrected. In the actual flight of the UAV, the inertial navigation system updates the current state by using the previous position and attitude information. Once deviations begin to occur, these small errors will continue to accumulate. If the UAV state is not determined and intervened in time, larger deviations will result, thereby affecting the accuracy and safety of the flight. Based on this, the present invention calculates the error influence coefficient of the UAV, and determines the UAV whose operation information needs to be corrected based on the error influence coefficient, so as to improve the flight accuracy and reliability of the UAV.

[0047] Specifically, the process of determining the error accumulation variable of the UAV in the current navigation segment includes: Dividing the preset navigation segment into a plurality of preset navigation sub-segments; Dividing the current navigation segment into a plurality 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 current offset navigation sub-segment number to the current navigation sub-segment total number as an error accumulation variable; 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.

[0048] Specifically, the angular velocity offset condition is set as follows: a difference ratio between a current angular velocity corresponding to any moment in the current navigation subsegment and a preset angular velocity corresponding to a corresponding preset navigation subsegment is greater than a predetermined angular velocity difference ratio threshold; The attitude deviation condition is set as follows: the angle change of any one of the current pitch angle, the current roll angle and the current yaw angle corresponding to any time in the current navigation subsegment exceeds a predetermined angle change threshold.

[0049] Specifically, the predetermined angular velocity difference ratio threshold is set to 0.3 times the preset angular velocity; the predetermined angle change threshold is set to 0.2 times the sum of the average value of the preset pitch angle, the preset roll angle and the preset yaw angle.

[0050] See also Figure 3 , Figure 3 The present invention is a logic block diagram of determining the error accumulation category of the drone to correct the drone operation information according to an embodiment of the invention. Specifically, the error accumulation category of the drone is determined to correct the drone operation information, wherein: If the error accumulation variable is greater than a preset error accumulation variable, determining that the error accumulation category of the UAV is an initial error accumulation category; If the error accumulation variable is less than or equal to a preset error accumulation variable, the error accumulation category of the UAV is determined to be an operating error accumulation category.

[0051] Specifically, the preset error accumulation variable represents the initial time when the UAV error occurs, so the preset error accumulation variable is set to be selected within the interval [0.87, 0.99].

[0052] Specifically, the error accumulation category of the UAV is determined through the error accumulation variable of the UAV, so as to correct the operation information of the UAV in a targeted manner. In actual situations, the offset error of the UAV during flight is mainly caused by errors in the initial operation information setting and dynamic interference during operation. For different offset 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 categories. For the initial error accumulation category, correction is made by re-adjusting parameter settings and other methods; for the operation error accumulation category, correction is made by using technologies such as updating the starting point position and the Kalman filter algorithm to improve the flight accuracy and reliability of the UAV.

[0053] Specifically, the process of tracing the error point includes: Determine the first offset navigation subsegment where the offset occurs; The coordinate point corresponding to the head end of the offset navigation sub-segment is determined as the error point.

[0054] Specifically, the process of determining the yaw angle based on the navigation segment corresponding to the error point of the UAV includes: Determining a first tangent line of the error point on a preset navigation segment; Determine a second tangent line of the error point on the current navigation segment; An angle between the first tangent line and the second tangent line is determined as a yaw angle.

[0055] See also Figure 4 , Figure 4 The structure diagram of the inertial navigation system for unmanned aerial vehicles according to the embodiment of the invention is shown in FIG. The system for the inertial navigation method for unmanned aerial vehicles is characterized by comprising: An information acquisition module is used to obtain the preset orbit position coordinates and current orbit position coordinates of the drone, and determine the preset attitude and preset angular velocity of the drone; An error determination module, which is connected to the information acquisition module, is used to determine the relative distance and offset angle of the UAV based on the preset orbit position coordinates and the current orbit operation position coordinates, determine the error influence coefficient of the UAV in combination with the trajectory complexity of the current navigation segment, and determine whether the operation information of the UAV needs to be corrected; An error correction module is connected to the error determination module, and is used to determine the error accumulation variable of the drone in the current navigation segment and the error accumulation category of the drone in response to the result that the drone needs to be corrected, so as to correct the drone operation information, wherein: In response to the initial error accumulation category, determining the current orbital operation position as the update starting position, combined with the navigation end position, to reset the UAV operation information; In response to the operation error accumulation category, the error point is traced back, the yaw angle is determined based on the navigation segment corresponding to the error point of the UAV, the yaw angle is corrected by using Kalman filtering, the current orbit operation position is determined as the update starting position, and the operation information of the UAV is reset in combination with the corrected yaw angle and the navigation end position; The continuous monitoring module is connected to the error correction module to ensure that the UAV continues to travel on a preset track or a corrected track, and to periodically monitor the UAV and timely correct the UAV operation information.

[0056] So far, the technical solutions of the present invention have 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 fall within the protection scope of the present invention.

[0057] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, 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: include: Obtain the preset orbit position coordinates and current orbit position coordinates of the drone, and determine the preset attitude and preset angular velocity of the drone; Determine the relative distance and offset angle of the UAV based on the preset orbit position coordinates and the current orbit operation position coordinates, determine the error influence coefficient of the UAV in combination with the trajectory complexity of the current navigation segment, and determine whether the operation information of the UAV needs to be corrected; In response to the result that the drone needs to be corrected, the error accumulation variable of the drone in the current navigation segment is determined, and the error accumulation category of the drone is determined to correct the drone operation information, wherein: In response to the initial error accumulation category, determining the current orbital operation position as the update starting position, combined with the navigation end position, to reset the UAV operation information; In response to the operation error accumulation category, the error point is traced back, the yaw angle is determined based on the navigation segment corresponding to the error point of the UAV, the yaw angle is corrected by using Kalman filtering, the current orbit operation position is determined as the update starting position, and the operation information of the UAV is reset in combination with the corrected yaw angle and the navigation end position; Ensure that the UAV continues to travel on the preset or corrected track, and maintain periodic monitoring of the UAV to timely correct the UAV operation information.

2. The inertial navigation method for a drone according to claim 1, characterized in that: The process of determining the relative distance and offset angle of the UAV includes: Determine the coordinate distance between the preset orbital position coordinate point corresponding to each moment and the current orbital running position coordinate point; Solve the mean value of the coordinate distance corresponding to each moment to obtain the relative distance; Determine a preset navigation segment based on the preset orbit position coordinates, and determine a current navigation segment based on the current orbit running position coordinates; Determine the tangent of the preset orbit position coordinate point corresponding to each moment relative to the preset navigation segment and the tangent of the current orbit running position coordinate point relative to the current navigation segment to solve the tangent angle; The mean value of the tangent angle corresponding to each moment is solved to obtain the offset angle.

3. The inertial navigation method for a drone according to claim 1, characterized in that: The process of determining the trajectory complexity of the current navigation segment includes: Determine the number of direction changes of the drone in the current navigation segment; The ratio of the number of direction changes to a preset change number threshold is determined as the trajectory complexity.

4. The inertial navigation method for a drone according to claim 1, characterized in that: The process of determining the error influence coefficient of the UAV includes: Determine a ratio of the relative distance to a reference relative distance as a first error influencing factor; Determine a ratio of the offset angle to a reference offset angle as a second error influencing factor; Determining a ratio of the trajectory complexity to the reference trajectory complexity as a third error influencing factor; Determine a weighted sum 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.

5. The inertial navigation method for a drone according to claim 1, characterized in that: The step of determining whether the operation information of the drone needs to be corrected comprises: If the error influence coefficient is greater than the preset error influence coefficient, the operation information of the UAV needs to be corrected; 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 the UAV is determined to maintain the preset track and continue to travel.

6. The inertial navigation method for a drone according to claim 2, characterized in that: The process of determining the error accumulation variable of the UAV in the current navigation segment includes: Dividing the preset navigation segment into a plurality of preset navigation sub-segments; Dividing the current navigation segment into a plurality 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 current offset navigation sub-segment number to the current navigation sub-segment total number as an error accumulation variable; 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.

7. The inertial navigation method for a drone according to claim 1, characterized in that: The error accumulation category of the UAV is determined to correct the UAV operation information, wherein: If the error accumulation variable is greater than a preset error accumulation variable, determining that the error accumulation category of the UAV is an initial error accumulation category; If the error accumulation variable is less than or equal to a preset error accumulation variable, the error accumulation category of the UAV is determined to be an operating error accumulation category.

8. The inertial navigation method for a drone according to claim 6, characterized in that: The process of tracing the error point includes: Determine the first offset navigation subsegment where the offset occurs; The coordinate point corresponding to the head end of the offset navigation sub-segment is determined as the error point.

9. The inertial navigation method for a drone according to claim 6, characterized in that: The process of determining the yaw angle based on the navigation segment corresponding to the error point of the UAV includes: Determining a first tangent line of the error point on a preset navigation segment; Determine a second tangent line of the error point on the current navigation segment; An angle between the first tangent line and the second tangent line is determined as a yaw angle.

10. A system using the inertial navigation method for a drone according to any one of claims 1 to 9, characterized in that: include: An information acquisition module is used to obtain the preset orbit position coordinates and current orbit position coordinates of the drone, and determine the preset attitude and preset angular velocity of the drone; An error determination module, which is connected to the information acquisition module, is used to determine the relative distance and offset angle of the UAV based on the preset orbit position coordinates and the current orbit operation position coordinates, determine the error influence coefficient of the UAV in combination with the trajectory complexity of the current navigation segment, and determine whether the operation information of the UAV needs to be corrected; An error correction module is connected to the error determination module, and is used to determine the error accumulation variable of the drone in the current navigation segment and the error accumulation category of the drone in response to the result that the drone needs to be corrected, so as to correct the drone operation information, wherein: In response to the initial error accumulation category, determining the current orbital operation position as the update starting position, combined with the navigation end position, to reset the UAV operation information; In response to the operation error accumulation category, the error point is traced back, the yaw angle is determined based on the navigation segment corresponding to the error point of the UAV, the yaw angle is corrected by using Kalman filtering, the current orbit operation position is determined as the update starting position, and the operation information of the UAV is reset in combination with the corrected yaw angle and the navigation end position; The continuous monitoring module is connected to the error correction module to ensure that the UAV continues to travel on a preset track or a corrected track, and to periodically monitor the UAV and timely correct the UAV operation information.

Images