Control system for unmanned aerial vehicle low-altitude inspection of power transmission line

Abstract

The invention discloses a control system for unmanned aerial vehicle low-altitude inspection of a power transmission line, and relates to the technical field of unmanned aerial vehicle inspection, and the control system comprises an attitude control system which utilizes a power system to change the flight attitude of an unmanned aerial vehicle; the image acquisition system acquires surrounding environment images in the flight process of the unmanned aerial vehicle and generates auxiliary images; measurement of the height of a tower pole, the length of a cable and the distance between the unmanned aerial vehicle and a feature point is realized through multi-sensor fusion; according to the positions, calculated by the image processing system, of the distance feature points of the unmanned aerial vehicle, flight attitude changes of the unmanned aerial vehicle at different positions are determined, and the flight attitude adjustment data of the unmanned aerial vehicle are changed by combining the relation between the multiple feature points and the distance of the unmanned aerial vehicle. According to the invention, the position of the unmanned aerial vehicle is further corrected by using the environmental characteristics, and it is ensured that the unmanned aerial vehicle can complete accurate inspection in an electromagnetic interference area.

Description

Technical Field

[0001] The present invention relates to the technical field of unmanned aerial vehicle inspection, and in particular to a control system for unmanned aerial vehicle low-altitude inspection of power transmission lines. Background Art

[0002] Drones play an important role in low-altitude inspections of power transmission lines, significantly improving inspection efficiency and safety. Drones can perform automated inspections according to preset routes and inspection points, reducing errors and risks of manual operations; by carrying infrared cameras and laser radars and other equipment, drones can collect detailed data and promptly detect equipment anomalies and potential faults; drone inspections are fast and can cover large areas of power transmission lines; drone inspections avoid the risks of manual tower climbing and high-altitude operations, and reduce the risk of falling and electric shock for operators.

[0003] Announcement No. CN105790155B discloses a transmission line UAV autonomous inspection system based on differential GPS, including a UAV and a ground station control system; the flight control system of the UAV receives waypoint information from the pole tower autonomous inspection waypoint generation system of the ground station control system, and controls the flight system to fly according to the waypoint information, and the waypoint information includes the UAV flight latitude and longitude, altitude, nose direction and angular direction of the gimbal. This application uses geographic information technology to accurately identify the transmission line pole towers, uses differential GPS positioning technology to accurately locate the UAV platform in flight, uses transmission line pole tower inspection technology to solidify the inspection points, and uses control technology to accurately control the angular displacement of the gimbal, so as to realize the autonomous inspection UAV system and inspection method of the transmission line.

[0004] However, the basis for applying differential GPS is that in the same area, factors that affect the accuracy of GPS real-time single-point positioning, such as atmospheric ionospheric delay error, satellite ephemeris error, satellite clock error, etc., have the same or similar impact on the base station and its neighboring users. Therefore, the distance between the receiver and the base station affects the accuracy of the correction. The farther away, the worse the accuracy of the correction result, especially when the receiver and the base station lack a common satellite reference. In addition, drones are also faced with electromagnetic interference during inspections, which can cause GPS signal loss or compass failure, affecting the stability and navigation accuracy of drones.

[0005] When the GPS signal of a drone is lost or the compass fails, its flight attitude changes, causing the images collected by the drone to be offset, affecting the integrity of the transmission line. Therefore, when facing electromagnetic interference, it is particularly important to ensure that the drone can ensure the accuracy of the drone's inspection path when the GPS signal is lost or the compass fails. To this end, the present application proposes a control system that ensures the accuracy of the drone's inspection path when the drone is inspecting transmission lines at low altitudes in the presence of electromagnetic interference. Summary of the invention

[0006] One of the purposes of the present invention is to provide a control system for low-altitude inspection of power transmission lines by unmanned aerial vehicles, which ensures the position accuracy of the unmanned aerial vehicle during the inspection path when electromagnetic interference causes GPS signal loss or compass failure.

[0007] To achieve the above objectives, the present invention is implemented through the following technical solutions: a control system for low-altitude inspection of transmission lines by unmanned aerial vehicles, the control system is used to control the unmanned aerial vehicles to inspect the transmission lines, and includes an attitude control system for controlling the flight attitude of the unmanned aerial vehicle, the attitude control system is connected to the power system of the unmanned aerial vehicle, and the power system is used to change the flight attitude of the unmanned aerial vehicle;

[0008] An image acquisition system, including an image acquisition unit, which acquires an image of a power transmission line corresponding to an inspection path during an inspection by a UAV;

[0009] The image acquisition system also includes an image auxiliary unit, which uses a movable camera device to acquire images of the surrounding environment of the drone during flight and generates an auxiliary image;

[0010] Also includes:

[0011] A measurement system, which measures the tower height, cable length, and distance between the UAV and the feature point through multi-sensor fusion. The measurement system also includes at least one measurement device for measuring factors affecting the flight attitude of the UAV, and the measurement device obtains environmental data affecting the flight attitude of the UAV;

[0012] An image processing system acquires auxiliary images, determines environmental features in the auxiliary images for positioning the UAV, constructs a relationship graph between the UAV and feature points in the environmental features, calculates the distance between the UAV and the feature points and determines the position of the UAV;

[0013] The cable sag model is constructed through tower data and cable length to determine the image acquisition range during the drone inspection process. The drone inspection path is determined based on the cable sag model, and the flight posture of the drone in the transmission line corresponding to the cable sag model is set according to the inspection path.

[0014] The attitude correction model obtains the UAV flight attitude and makes synchronous adjustments based on the UAV flight attitude, determines the impact of environmental data on the UAV environment, and determines the flight attitude changes of UAVs at different positions based on the positions of the UAV distance feature points calculated by the image processing system, and combines the relationship between multiple feature points and the UAV distance to change the UAV flight attitude adjustment data.

[0015] In one or more embodiments of the present invention, the multi-sensor fusion includes an altitude sensor for determining the tower altitude and the tower height, a laser ranging unit for measuring the cable length, and an acoustic ranging sensor for obtaining the distance between the drone and the feature point;

[0016] Determine the height relationship between the two towers and the supporting point data of the tower supporting cables, and determine the height difference between the supporting points;

[0017] The acoustic wave ranging sensor combined with the auxiliary image determines the distance between the drone and the feature point. Based on the acoustic wave ranging sensor combined with the auxiliary image, three-dimensional data of the feature point position can be constructed.

[0018] In one or more embodiments of the present invention, the measuring device is an airflow detection sensor, which obtains the airflow velocity, airflow direction and turbulence degree around the fuselage of the drone during the inspection process, and determines the airflow state and the relationship between the airflow and the drone through the airflow data.

[0019] In one or more embodiments of the present invention, the method of constructing a relationship graph between the drone and the feature point, calculating the distance between the drone and the feature point and determining the position of the drone is as follows:

[0020] Acquire an auxiliary image, extract multiple environmental feature ranges from the auxiliary image by an image processing algorithm, and determine at least one feature point in each environmental feature;

[0021] The acoustic ranging sensor is transmitted by the drone to the environmental features to determine the three-dimensional data within the range of the environmental features;

[0022] Continuously collecting auxiliary images, matching feature points in a plurality of auxiliary images, and determining at least one high-precision feature point;

[0023] Determine the distance between the high-precision feature points detected by the acoustic ranging sensor and the drone;

[0024] The drone position is determined based on the distance between the drone and high-precision feature points in multiple auxiliary images.

[0025] In one or more embodiments of the present invention, the cable sag model is established as follows:

[0026] Get the height h of two adjacent towers of the line to be inspected A and h B , Cable support point data h Ax and h Bx and the cable length L;

[0027] Calculate the weight w of the cable per unit length according to the cable model;

[0028] Calculate the horizontal distance D between the two towers based on the tower height and cable support point data;

[0029] Determine the catenary parameters in the catenary equation and calculate the cable sag S.

[0030] In one or more embodiments of the present invention, the horizontal distance D is calculated as follows:

[0031]

[0032] Among them, h A is the height of tower A, h B is the height of tower B, h Ax is the cable support point data of tower A, h Bx The cable support point data of tower B;

[0033] The catenary equation is:

[0034]

[0035] Among them, a is the catenary parameter, x is the horizontal distance, and y is the vertical distance;

[0036] The catenary parameter a is calculated by iteration so that a satisfies the following conditions:

[0037]

[0038] The calculation formula of cable sag S is as follows:

[0039]

[0040] The cable arc is drawn through the cable sag S.

[0041] In one or more embodiments of the present invention, the inspection path of the drone is determined based on the cable sag and the optimal inspection distance, the inspection path corresponds to the entire cable length of the sag, and the inspection path is adjusted in combination with the change in the cable arc;

[0042] Divide the cables into multiple different areas and determine the best inspection path based on the obstacles in different areas;

[0043] According to the height and position of the optimal inspection path, the flight posture of the drone corresponding to different areas is set.

[0044] In one or more embodiments of the present invention, the attitude correction model obtains the control data of the attitude control system on the UAV, and adjusts the UAV state in the attitude correction model according to the control data of the UAV. The attitude correction model obtains the environmental data obtained by the measuring equipment, and further adjusts the UAV state according to the attitude of the UAV controlled by the attitude control system combined with the environmental data.

[0045] In one or more embodiments of the present invention, the method for determining the attitude offset position of the drone through airflow data is as follows:

[0046] The airflow detection sensor obtains airflow data and performs filtering on the airflow data to remove noise;

[0047] Establish an airflow model based on airflow data and analyze the impact of airflow on the flight posture of the UAV;

[0048] Determine the flight attitude data of the UAV in the attitude control system and calculate the attitude deviation;

[0049] After determining the attitude deviation, the UAV's motion deviation position per unit time is adjusted to the standard position per unit time set by the attitude control system according to the UAV deviation position.

[0050] In one or more embodiments of the present invention, the flight attitude change of the drone at different positions is determined according to the position of the drone distance feature point calculated by the image processing system, and the method for determining the drone flight attitude adjustment data is as follows:

[0051] Determine the positional relationship between the feature points and the drone, and calculate the distance d between the drone and each feature point PA and angle θ PA ;

[0052]

[0053] Among them, the coordinates of the feature point are (x1, y1), and the initial position of the drone is (x0, y0, z0);

[0054] Determine the set flight attitude of the drone at its current location Calculate the standard position P'(x',y',z') within unit time t;

[0055]

[0056] After a unit time, the deviation position P" (x", y", z") of the UAV is determined by the feature points, and the flight attitude deviation ΔP between the deviation position and the standard position is determined;

[0057]

[0058] The UAV flight attitude adjustment data is determined according to the flight attitude deviation.

[0059] Through the above technical solution, the present invention has the following beneficial effects:

[0060] 1. This application is used to control drones. When the drone is subject to electromagnetic interference during inspection, causing GPS loss and compass failure, and the drone's attitude cannot be stably controlled, the environmental characteristics are used to further correct the drone's position to ensure that the drone can complete accurate inspections in electromagnetic interference areas.

[0061] 2. During the drone inspection process, establish a cable sag model between the two towers, determine the cable arc according to the cable sag, set the optimal distance for drone inspection, determine the drone inspection path, ensure the effectiveness and efficiency of drone inspection in the inspection path, and set the drone's flight posture at different positions accordingly.

[0062] 3. Obtain the airflow data around the drone during operation, determine the impact of the airflow data on the drone's flight attitude, and further determine the change in the drone's flight attitude in combination with the drone's offset position. After the drone has a position deviation, the drone's position can be corrected according to the change in the drone's flight attitude.

[0063] 4. Determine the low-altitude feature points, determine the three-dimensional data of the feature points, determine the high-precision feature points, avoid the problem of insufficient accuracy of the feature point position caused by visual difference, and further determine the stable feature points in the environmental feature range to calculate the drone position after the feature point position deviation occurs, so as to ensure the accuracy of the drone position.

[0064] Other features and advantages of the present invention will be described in the following description, and part of them will become obvious from the description, or will be understood by practicing the present invention. The purpose and other advantages of the present invention can be realized and obtained by the structures particularly pointed out in the written description and the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0065] Figure 1 It is a schematic diagram of the system of the present invention;

[0066] Figure 2 Schematic diagram of the positional relationship between the drone and the feature points of the present invention. DETAILED DESCRIPTION

[0067] The following will disclose multiple embodiments of the present invention with the accompanying drawings. For the purpose of clear description, many practical details will be described together in the following description. However, it should be understood that these practical details should not be used to limit the present invention. In other words, in some embodiments of the present invention, these practical details are not necessary. And if it is possible to implement, the features of different embodiments can be applied interchangeably.

[0068] Unless otherwise defined, all words (including technical and scientific terms) used herein have their usual meanings, which can be understood by those familiar with this field. Furthermore, the definitions of the above words in commonly used dictionaries should be interpreted as the meanings consistent with the relevant fields of the present invention in the content of this specification. Unless otherwise clearly defined, these words will not be interpreted as idealized or overly formal meanings.

[0069] like Figure 1 As shown, the present invention provides a control system for low-altitude inspection of transmission lines by UAVs, which is used to improve the inspection path accuracy of UAVs during transmission line inspections, and to prevent the UAVs from deviating from the preset inspection path after being subjected to electromagnetic interference, thereby affecting the comprehensiveness of the inspection data.

[0070] In one embodiment, the control system is used to control a UAV to perform transmission line inspection, and includes an attitude control system for controlling the flight attitude of the UAV, the attitude control system is connected to the power system of the UAV, and the flight attitude of the UAV is changed by using the power system;

[0071] An image acquisition system, including an image acquisition unit, which acquires an image of a power transmission line corresponding to an inspection path during an inspection by a UAV;

[0072] The image acquisition system also includes an image auxiliary unit, which uses a movable camera device to acquire images of the surrounding environment of the drone during flight and generates an auxiliary image;

[0073] Also includes:

[0074] A measurement system, which measures the tower height, cable length, and distance between the UAV and the feature point through multi-sensor fusion. The measurement system also includes at least one measurement device for measuring factors affecting the flight attitude of the UAV, and the measurement device obtains environmental data affecting the flight attitude of the UAV;

[0075] An image processing system acquires auxiliary images, determines environmental features in the auxiliary images for positioning the UAV, constructs a relationship graph between the UAV and feature points in the environmental features, calculates the distance between the UAV and the feature points and determines the position of the UAV;

[0076] The cable sag model is constructed through tower data and cable length to determine the image acquisition range during the drone inspection process. The drone inspection path is determined based on the cable sag model, and the flight posture of the drone in the transmission line corresponding to the cable sag model is set according to the inspection path.

[0077] The attitude correction model obtains the UAV flight attitude and makes synchronous adjustments based on the UAV flight attitude, determines the impact of environmental data on the UAV environment, and determines the flight attitude changes of UAVs at different positions based on the positions of the UAV distance feature points calculated by the image processing system, and combines the relationship between multiple feature points and the UAV distance to change the UAV flight attitude adjustment data.

[0078] In one implementable manner, the image acquisition system is an acquisition system used by a drone to obtain image data. The image acquisition unit and the image auxiliary unit are both part of the image acquisition system. Since the image acquisition unit needs to inspect the transmission lines, the acquisition of the surrounding environment image is achieved through an additional image auxiliary unit, and the auxiliary image is used to assist in the positioning of the drone.

[0079] Among them, due to the existence of electromagnetic interference, when the GPS signal of the drone is lost or the compass fails, the attitude of the drone will change under the influence of the external environment, and the corresponding collection range of the drone will also change after the attitude change. The positioning of the auxiliary image can further ensure the stability of the drone's flight attitude when it is subject to electromagnetic interference.

[0080] During the flight of the UAV, the flight attitude corresponds to the position that the UAV can reach after a period of time. When determining the cable sag at different positions, the UAV flight attitude is set, and the feature points are continuously used during the flight of the UAV to determine the change in the UAV's flight attitude. By combining the flight attitude with the change in the distance between the feature points and the UAV, the UAV's flight attitude can be accurately corrected.

[0081] In one embodiment, the multi-sensor fusion includes an altitude sensor for determining the tower altitude and the tower height, a laser ranging unit for measuring the cable length, and an acoustic ranging sensor for obtaining the distance between the drone and the feature point;

[0082] Determine the height relationship between the two towers and the supporting point data of the tower supporting cables, and determine the height difference between the supporting points;

[0083] The acoustic wave ranging sensor combined with the auxiliary image determines the distance between the drone and the feature point. Based on the acoustic wave ranging sensor combined with the auxiliary image, three-dimensional data of the feature point position can be constructed.

[0084] In one feasible method, by determining the distance data between the tower, cable, and drone and the feature points, a cable sag model is constructed and the drone is positioned, so that when the drone is conducting low-altitude inspections, the flight attitude of the drone can be determined by relying on multiple feature points at low altitudes.

[0085] Since the environmental features in the auxiliary image will cause visual differences, based on the above problem, the distance detection of the environmental features is realized through the acoustic ranging sensor, and the three-dimensional data of the environmental feature position is determined. According to the three-dimensional data, a three-dimensional model of the environmental feature position can be constructed, or it can be used to analyze the position and occlusion of the feature points, so as to further improve the accuracy of the feature points.

[0086] In one embodiment, the measuring device is an airflow detection sensor, which obtains the airflow speed, airflow direction and turbulence degree around the fuselage of the drone during the inspection process, and determines the airflow state and the relationship between the airflow and the drone through the airflow data.

[0087] In one feasible method, after determining the inspection path of the UAV, the flight attitude change of the UAV is set. Without the influence of the external environment, the flight attitude of the UAV will not change significantly within a certain controllable range, and the change of airflow will directly affect the flight attitude of the UAV. Therefore, the measuring equipment is set as an airflow detection sensor. The corresponding attitude change of the UAV can be determined through the airflow data, and the attitude can be adjusted in time during the UAV's continued inspection.

[0088] In one embodiment, a method for constructing a relationship graph between a drone and a feature point, calculating the distance between the drone and the feature point, and determining the position of the drone is as follows:

[0089] Acquire an auxiliary image, extract multiple environmental feature ranges from the auxiliary image by an image processing algorithm, and determine at least one feature point in each environmental feature;

[0090] The acoustic ranging sensor is transmitted by the drone to the environmental features to determine the three-dimensional data within the range of the environmental features;

[0091] Continuously collecting auxiliary images, matching feature points in a plurality of auxiliary images, and determining at least one high-precision feature point;

[0092] Determine the distance between the high-precision feature points detected by the acoustic ranging sensor and the drone;

[0093] The drone position is determined based on the distance between the drone and high-precision feature points in multiple auxiliary images.

[0094] The drone determines its position through image feature points for visual positioning. However, since the auxiliary image has a large amount of data and contains multiple environmental feature ranges and multiple high-precision feature points, the amount of calculation required is also large.

[0095] In one feasible method, the three-dimensional data of the range of environmental features in the auxiliary image is determined by an acoustic wave ranging sensor. It is only necessary to determine the environmental features through the auxiliary image, and the feature points in the environmental features are determined by the three-dimensional data. This reduces the amount of calculation for calculating feature points in multiple auxiliary images and improves the distance accuracy between the drone and the feature points.

[0096] In order to avoid changes in feature points affected by the external environment, multiple feature points are determined within the range of environmental features. Three-dimensional data can reflect the changes in the appearance of environmental features, and feature points with no or little changes are determined as high-precision feature points to ensure the accuracy of the drone's position.

[0097] In one embodiment, the cable sag model is established as follows:

[0098] Get the height h of two adjacent towers of the line to be inspected A and h B , Cable support point data h Ax and h Bx and the cable length L;

[0099] Calculate the weight w of the cable per unit length according to the cable model;

[0100] Calculate the horizontal distance D between the two towers based on the tower height and cable support point data;

[0101] Determine the catenary parameters in the catenary equation and calculate the cable sag S.

[0102] In one feasible method, the cable sag is calculated by using tower data and cable data, which can reflect the changing state of the cable between the two towers. The inspection path of the drone is determined based on the cable sag and the optimal inspection distance range of the drone. Since the optimal inspection distance is a certain range value, the drone inspection path ultimately determined is a range.

[0103] In one embodiment, the horizontal distance D is calculated as follows:

[0104]

[0105] Among them, h A is the height of tower A, h B is the height of tower B, h Ax is the cable support point data of tower A, h Bx The cable support point data of tower B;

[0106] The catenary equation is:

[0107]

[0108] Among them, a is the catenary parameter, x is the horizontal distance, and y is the vertical distance;

[0109] The catenary parameter a is calculated by iteration so that a satisfies the following conditions:

[0110]

[0111] The calculation formula of cable sag S is as follows:

[0112]

[0113] The cable arc is drawn through the cable sag S.

[0114] In one feasible method, the catenary equation is used to calculate and determine the sag state and obtain data between the two towers. The drone inspection is a daily inspection. Therefore, it is only necessary to obtain tower data and cable data at different positions during the first drone inspection. In subsequent inspections, the sag can be quickly calculated using the previous data. If there are multiple cables extending to the same or different positions on the tower, the sag of the cable between the two towers can be quickly determined based on the corresponding tower data and the length state of the cable during the installation process.

[0115] For example, the total length of the cable L = 350, the weight per unit length of the cable w = 3, and the height of the tower A h A =158, Tower B height h B =350, the height of the cable support point on the tower A h Ax =100, the height of the cable support point on the tower B h Bx =320, the horizontal distance between the two towers D = 292;

[0116] Select an initial value a0=100, gradually adjust the value of a through iteration, and after determining the appropriate value of a, calculate the sag;

[0117] By iteration, we get a=150 and calculate the cosh value in the catenary equation:

[0118]

[0119] Calculate the sag S≈78.6;

[0120] Draw a cable arc with X values ​​from 0-292 and calculate the corresponding Y values:

[0121]

[0122] like Figure 2As shown, in one embodiment, the inspection path of the drone is determined according to the cable sag and the optimal inspection distance, the inspection path corresponds to the entire cable length of the sag, and the inspection path is adjusted in combination with the cable arc change;

[0123] Divide the cables into multiple different areas and determine the best inspection path based on the obstacles in different areas;

[0124] According to the height and position of the optimal inspection path, the flight posture of the drone corresponding to different areas is set.

[0125] The flight attitude of a drone includes pitch angle, roll angle and yaw angle.

[0126] In one feasible method, obstacles around the cable are determined based on the situation of the cable during the laying process, and the best inspection path for the drone is determined based on the obstacles. The cable has sag, so the cable is divided into multiple areas, and different flight postures are set in different areas, so that the drone can better inspect the transmission line in this flight posture.

[0127] In one embodiment, the attitude correction model obtains control data of the UAV from the attitude control system, and adjusts the UAV state in the attitude correction model according to the control data of the UAV. The attitude correction model obtains environmental data obtained by the measuring equipment, and further adjusts the UAV state according to the attitude of the UAV controlled by the attitude control system combined with the environmental data.

[0128] In one feasible method, the attitude of the UAV corresponds to the position that the UAV can reach per unit time. When the UAV moves in a specific attitude, it is affected by the external environmental data, causing the attitude of the UAV to change. The external changes caused by the external environment cannot be determined by the attitude control system. The position offset of the UAV in flying in a fixed attitude is determined by determining the environmental data.

[0129] In one embodiment, the method for determining the attitude offset position of the drone through airflow data is as follows:

[0130] The airflow detection sensor obtains airflow data and performs filtering on the airflow data to remove noise;

[0131] Establish an airflow model based on airflow data and analyze the impact of airflow on the flight posture of the UAV;

[0132] Determine the flight attitude data of the UAV in the attitude control system and calculate the attitude deviation;

[0133] After determining the attitude deviation, the UAV's motion deviation position per unit time is adjusted to the standard position per unit time set by the attitude control system according to the UAV deviation position.

[0134] In one feasible method, when adjusting the drone to the standard position, the calculated drone movement time is the sum of the time it takes for the drone to move to the deviation position and the time it takes for the drone to move from the deviation position to the standard position. The drone's catching-up state after moving to the deviation position is used to adjust the drone's inspection efficiency, thereby avoiding the situation where the inspection time changes due to deviations in the drone, and further ensuring the inspection efficiency.

[0135] The flight attitude data of the drone includes pitch angle, roll angle, and yaw angle.

[0136] In one embodiment, the flight attitude change of the drone at different positions is determined according to the position of the drone distance feature point calculated by the image processing system, and the method for determining the drone flight attitude adjustment data is as follows:

[0137] Determine the positional relationship between the feature points and the drone, and calculate the distance d between the drone and each feature point PA and angle θ PA ;

[0138]

[0139] Among them, the coordinates of the feature point are (x1, y1), and the initial position of the drone is (x0, y0, z0);

[0140] Determine the set flight attitude of the drone at its current location Calculate the standard position P'(x',y',z') within unit time t;

[0141]

[0142] After a unit time, the deviation position P" (x", y", z") of the UAV is determined by the feature points, and the flight attitude deviation ΔP between the deviation position and the standard position is determined;

[0143]

[0144] The UAV flight attitude adjustment data is determined according to the flight attitude deviation.

[0145] In one feasible method, after the GPS signal is lost or the compass fails, the impact of airflow on the drone causes the drone's flight attitude to deviate, which is not a deviation of the drone controlled by the attitude control system. In order to calculate the drone's attitude deviation more accurately, multiple feature points are combined to determine the drone's standard flight position and deviation position, and the difference between the two positions is used to calculate the deviation of the flight attitude. The drone's attitude is adjusted according to the flight attitude deviation to restore its attitude and position.

[0146] Although the present invention is disclosed in conjunction with the above embodiments, it is not intended to limit the present invention. Anyone familiar with this technology can make various changes and modifications without departing from the spirit and scope of the present invention. Therefore, the scope of protection of the present invention should be based on the definition of the attached claims.

Claims

1. A control system for low-altitude inspection of transmission lines by unmanned aerial vehicles, the control system is used to control unmanned aerial vehicles to inspect transmission lines, and includes an attitude control system for controlling the flight attitude of the unmanned aerial vehicle, the attitude control system is connected to the power system of the unmanned aerial vehicle, and the power system is used to change the flight attitude of the unmanned aerial vehicle; The image acquisition system includes an image auxiliary unit, which uses a movable camera device to acquire images of the surrounding environment of the UAV during flight and generates auxiliary images; It is characterized in that Also includes: A measurement system, which measures the tower height, cable length, and distance between the UAV and the feature point through multi-sensor fusion. The measurement system also includes at least one measurement device for measuring factors affecting the flight attitude of the UAV, and the measurement device obtains environmental data affecting the flight attitude of the UAV; An image processing system acquires auxiliary images, determines environmental features in the auxiliary images for positioning the UAV, constructs a relationship graph between the UAV and feature points in the environmental features, calculates the distance between the UAV and the feature points and determines the position of the UAV; The cable sag model is constructed through tower data and cable length to determine the image acquisition range during the drone inspection process. The drone inspection path is determined based on the cable sag model, and the flight posture of the drone in the transmission line corresponding to the cable sag model is set according to the inspection path. The attitude correction model obtains the UAV flight attitude and makes synchronous adjustments based on the UAV flight attitude, determines the impact of environmental data on the UAV environment, and determines the flight attitude changes of UAVs at different positions based on the positions of the UAV distance feature points calculated by the image processing system, and combines the relationship between multiple feature points and the UAV distance to change the UAV flight attitude adjustment data.

2. A control system for low-altitude inspection of power transmission lines by unmanned aerial vehicles according to claim 1, characterized in that: The multi-sensor fusion includes an altitude sensor to determine the tower altitude and tower height, a laser ranging unit to measure the cable length, and an acoustic ranging sensor to obtain the distance between the drone and the feature point; Determine the height relationship between the two towers and the supporting point data of the tower supporting cables, and determine the height difference between the supporting points; The acoustic wave ranging sensor combined with the auxiliary image determines the distance between the drone and the feature point. Based on the acoustic wave ranging sensor combined with the auxiliary image, three-dimensional data of the feature point position can be constructed.

3. A control system for low-altitude inspection of power transmission lines by drones according to claim 1, characterized in that: The measuring device is an airflow detection sensor, which obtains the airflow speed, direction and turbulence degree around the drone during the inspection process, and determines the airflow state and the relationship between the airflow and the drone through the airflow data.

4. A control system for low-altitude inspection of power transmission lines by drones according to claim 1, characterized in that: The method of constructing the relationship graph between the drone and the feature points, calculating the distance between the drone and the feature points and determining the drone's position is as follows: Acquire an auxiliary image, extract multiple environmental feature ranges from the auxiliary image by an image processing algorithm, and determine at least one feature point in each environmental feature; The acoustic ranging sensor is transmitted by the drone to the environmental features to determine the three-dimensional data within the range of the environmental features; Continuously collecting auxiliary images, matching feature points in a plurality of auxiliary images, and determining at least one high-precision feature point; Determine the distance between the high-precision feature points detected by the acoustic ranging sensor and the drone; The drone position is determined based on the distance between the drone and high-precision feature points in multiple auxiliary images.

5. A control system for low-altitude inspection of power transmission lines by drones according to claim 1, characterized in that: The cable sag model is established as follows: Get the height h of two adjacent towers of the line to be inspected A and h B , Cable support point data h Ax and h Bx and the cable length L; Calculate the weight w of the cable per unit length according to the cable model; Calculate the horizontal distance D between the two towers based on the tower height and cable support point data; Determine the catenary parameters in the catenary equation and calculate the cable sag S.

6. A control system for low-altitude inspection of power transmission lines by drones according to claim 1, characterized in that: The calculation formula of horizontal distance D is as follows: Among them, h A is the height of tower A, h B is the height of tower B, h Ax is the cable support point data of tower A, h Bx The cable support point data of tower B; The catenary equation is: Among them, a is the catenary parameter, x is the horizontal distance, and y is the vertical distance; The catenary parameter a is calculated by iteration so that a satisfies the following conditions: The calculation formula of cable sag S is as follows: The cable arc is drawn through the cable sag S.

7. A control system for low-altitude inspection of power transmission lines by drones according to claim 1, characterized in that: The inspection path of the drone is determined based on the cable sag and the optimal inspection distance. The inspection path corresponds to the entire cable length of the sag, and the inspection path is adjusted in combination with the change in the cable arc. Divide the cables into multiple different areas and determine the best inspection path based on the obstacles in different areas; According to the height and position of the optimal inspection path, the flight posture of the drone corresponding to different areas is set.

8. The control system for low-altitude inspection of power transmission lines by unmanned aerial vehicles according to claim 1 is characterized in that: The attitude correction model obtains the control data of the UAV from the attitude control system, and adjusts the UAV state in the attitude correction model according to the control data of the UAV. The attitude correction model obtains the environmental data obtained by the measuring equipment, and further adjusts the UAV state according to the attitude of the UAV controlled by the attitude control system combined with the environmental data.

9. A control system for low-altitude inspection of power transmission lines by drones according to claim 1, characterized in that: The method for determining the attitude offset position of the drone through airflow data is as follows: The airflow detection sensor obtains airflow data and performs filtering on the airflow data to remove noise; Establish an airflow model based on airflow data and analyze the impact of airflow on the flight posture of the UAV; Determine the flight attitude data of the UAV in the attitude control system and calculate the attitude deviation; After determining the attitude deviation, the UAV's motion deviation position per unit time is adjusted to the standard position per unit time set by the attitude control system according to the UAV deviation position.

10. A control system for low-altitude inspection of power transmission lines by drones according to claim 1, characterized in that: The flight attitude change of the drone at different positions is determined according to the position of the drone distance feature point calculated by the image processing system. The method for determining the drone flight attitude adjustment data is as follows: Determine the positional relationship between the feature points and the drone, and calculate the distance d between the drone and each feature point PA and angle θ PA ; Among them, the coordinates of the feature point are (x1, y1), and the initial position of the drone is (x0, y0, z0); Determine the set flight attitude of the drone at its current location Calculate the standard position P'(x',y',z') within unit time t; After a unit time, the deviation position P" (x", y", z") of the UAV is determined by the feature points, and the flight attitude deviation ΔP between the deviation position and the standard position is determined; The UAV flight attitude adjustment data is determined according to the flight attitude deviation.

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