Unmanned aerial vehicle inspection path automatic planning method for power transmission line

Abstract

The invention provides an unmanned aerial vehicle routing inspection path automatic planning method for a power transmission line, and belongs to the technical field of unmanned aerial vehicle routing inspection. The problems that when an unmanned aerial vehicle conducts power transmission line inspection at present, picture shooting is not clear, and precise hovering cannot be achieved are solved. Comprising the following steps: acquiring position information of unmanned aerial vehicle inspection mark points of a power transmission line, and calculating coordinates; according to the PID parameters, dynamic models of the height and the attitude of the unmanned aerial vehicle are constructed; designing a path planning tracking controller based on a PID algorithm; according to the central position of the unmanned aerial vehicle and the distance between the unmanned aerial vehicle and the obstacle, the safety distance of unmanned aerial vehicle inspection flight in any direction is obtained, and inspection path planning is completed; calculating the current height and attitude of the unmanned aerial vehicle, and measuring and calculating the position and angle deviation between the current unmanned aerial vehicle and the corresponding task point; setting a deviation threshold value, measuring relative position deviation and angle deviation after the unmanned aerial vehicle arrives at the task point, and adjusting the height and posture of the unmanned aerial vehicle according to the deviation threshold value; the method is applied to power transmission line inspection.

Description

Technical Field

[0001] The invention provides a method for automatically planning an unmanned aerial vehicle inspection path for a power transmission line, and belongs to the technical field of power transmission line inspection. Background Art

[0002] With the maturity of drone technology, drones have broad application prospects in the process of power inspection. At present, ground staff mainly control the flight and shooting of drones during the inspection process based on the images and GPS information sent back by the visual camera on the drone. However, this inspection method is still highly dependent on operators, with high work intensity and low efficiency. It can no longer meet the needs of power maintenance and power supervision in my country. Therefore, the development of autonomous drone inspection systems has become a new trend.

[0003] In the existing transmission line drone automatic inspection technology, the production of waypoints in the route planning process mostly requires manual input, and the process is cumbersome. In particular, the latitude and longitude coordinates and altitude of each drone waypoint need to be collected point by point in advance. Once an error occurs, it will seriously affect flight safety. At the same time, when fixed-wing drones conduct transmission line inspections, their work path planning is usually constrained by multiple conditions such as inspection tasks and drone performance parameters, making it extremely difficult to find a safe, suitable, and effective transmission line drone automatic inspection path. In addition, when drones inspect transmission lines, they need to accurately focus on the target so that the drone can accurately identify the working conditions of each part of the transmission line, so as to effectively inspect the transmission line. This is a necessary condition to ensure the correctness and efficiency of drone inspections. Therefore, in order to achieve the purpose of taking accurate and clear pictures of transmission lines, it is necessary for the inspection drone to hover accurately at the positioning point to take images. Summary of the invention

[0004] In order to solve the problems of unclear picture taking and inability to hover accurately when drones are currently used for power transmission line inspections, the present invention proposes an automatic planning method for drone inspection paths for power transmission lines.

[0005] In order to solve the above technical problems, the technical solution adopted by the present invention is: a method for automatically planning a UAV inspection path for a power transmission line, comprising the following steps:

[0006] Step 1: Obtain the location information of the inspection mark point of the transmission line by drone, and use the data transmission link to transmit the acquired information to the receiver of the mobile receiving station set at the corresponding inspection mark point. The mobile receiving station calculates the coordinates of the inspection mark point relative to the reference station based on the observation value by differential positioning method, and calculates the coordinates of the inspection mark point based on the coordinates of the reference station;

[0007] Step 2: Construct the dynamic model of the altitude and attitude of the drone based on the PID parameters;

[0008] Step 3: Design a path planning and tracking controller based on the PID algorithm, and use the inner and outer loop control method to obtain the PID tracking controller block diagram of the UAV;

[0009] Step 4: The safe distance for drone inspection flight in any direction is obtained by the center position of the drone and its distance from obstacles, and then the coordinate transformation matrix is ​​used to complete the transmission line drone inspection path planning;

[0010] Step 5: Calculate the current altitude and attitude of the drone, and measure the position and angle deviation between the current drone and the corresponding mission point;

[0011] Step 6: Set the deviation threshold. After the UAV reaches the mission point, it measures the relative position deviation and angle deviation, and adjusts the UAV height and posture according to the deviation threshold. If it is less than the deviation threshold, the UAV hovers to the mission point to perform the mission; otherwise, the UAV continues to adjust the speed and change the posture until the relative deviation is less than the deviation threshold, and then hovers precisely at the fixed position to perform the mission, and then iterates steps 5 and 6 to complete the automatic inspection task.

[0012] The calculation process of the inspection mark point coordinates in step 1 is as follows: the carrier integer ambiguity of the drone inspection mark point is obtained through the linear combination model, the coefficient is calculated through the least squares adjustment method, and finally the carrier side phase pseudorange observation is realized. Then, the coordinate positioning of the inspection mark point is completed through the three-sided positioning method.

[0013] The UAV altitude dynamics model constructed in step 2 is the UAV altitude control model, and its expression is as follows:

[0014]

[0015] In the above formula: Z(s) represents the actual hovering height of the drone after Laplace transform processing, K1 is a constant, K represents the gain, T s represents the time constant;

[0016] The expression of the PID transfer function L(s) of the UAV altitude control model is as follows:

[0017] L(s)=ρ T γ(s);

[0018] In the above formula: T =[K p , K i , K d ], where K p , K i , K d These are PID algorithm parameters.

[0019] The UAV attitude dynamics model constructed in step 2 is the UAV attitude control model, which includes the transfer functions of the three attitude angles of the UAV yaw angle, pitch angle, and roll angle, and its expression is:

[0020]

[0021] In the above formula: θ(s) is the pitch angle transfer function, φ(s) is the roll angle transfer function, is the yaw angle transfer function, U2(s) is the pitch torque transfer function of the UAV, U3(s) is the roll torque transfer function of the UAV, U4(s) is the yaw torque transfer function of the UAV, L is the distance between the center of mass of the UAV and the center of the rotor, I xx ,I yy ,I zz Respectively represent the moment of inertia of the x, y, and z axes;

[0022] The error calculation process of the PID algorithm to control each attitude angle is shown in the following formula:

[0023]

[0024] In the above formula: θ ref , θ ref Respectively represent the ideal value of each attitude angle control, θ, θ represents the input value of each attitude angle, and the expression of the closed-loop transfer function of each attitude angle is as follows:

[0025]

[0026] In the above formula: T s Represents the time constant.

[0027] The expression of the PID tracking controller in step 3 is as follows:

[0028]

[0029] In the above formula: the input and output of the PID tracking controller are represented by e and u respectively, and k P , k I , k D Represent the proportional, integral and differential coefficients respectively, T I , T D represent the integral and differential time constants respectively.

[0030] The specific implementation process of step 4 is as follows:

[0031] Step 4-1: Divide the distance between the starting point and the target point of the drone into several segments. Use the coordinate transformation matrix shown in the following formula to rotate the coordinate system to the specified angle with the y-axis as the rotation center, and rotate the x-axis to the direction of the connection line to obtain the transformed coordinate system x'y'z'. Calculate the step length of each segment according to the specified transmission line drone inspection path planning accuracy standard and drone performance constraints;

[0032]

[0033] In the above formula: α represents the X-axis and The angle between in Indicates the distance between the starting point and the target point of the drone. The initial coordinate system coordinates are represented by (x, y, z), and the transformed coordinate system coordinates are (x', y', z');

[0034] Step 4-2: The starting point and target point of the drone are represented by Ps and Pt respectively. Divide, the number of parts is represented by n, each segment and The straight line perpendicular and parallel to the Z' axis is denoted by l k It means that through path smoothing and path pruning, l is obtained. k The best path point p on k (x k , z k ), connect the best path points of each section, that is, the expression of the planned transmission line UAV inspection path P is as follows:

[0035] P={P s ,p1(x1,z1),p2(x1,z1),...,p n-1 (x n-1 z n-1 ), P t}.

[0036] The specific implementation process of step 6 is as follows:

[0037] (1) When the drone takes off, the image to be taken is uploaded to the drone's flight controller. The inspection drone is equipped with a camera and uses machine vision technology to identify the towers, pole number plates, insulators and assembly hardware on the transmission line and obtain their relative position information of the drone, that is, the relative position information of the drone to the shooting target is obtained;

[0038] (2) The acquired position information is compared with the image data uploaded to the controller. If the deviation is large, the deviation is sent to the flight controller of the hovering UAV, and then the flight controller outputs it to the actuator. This process is repeated to ensure that the inspection UAV reaches the optimal hovering position for shooting;

[0039] (3) The variable parameter PID control algorithm is used to control the UAV to hover precisely at the optimal shooting position.

[0040] The inspection iteration process means that after the UAV completes the relevant operations of the current task point along the automatic inspection route, it updates this task to the current point of the UAV, obtains the next task point as the expected point, compares it with the set deviation threshold, adjusts the height and posture of the UAV, and iterates steps 5 and 6 until all task points are inspected and tasks are completed. The UAV returns and the inspection task is successfully completed.

[0041] The precise hovering control of the inspection UAV adopts a piecewise linear variable parameter PID algorithm. By fitting the platform response curve under different speeds / accelerations, a control parameter iteration model is established to achieve the adaptability of the algorithm.

[0042] The expression of the safe distance L of the drone inspection flight in any direction in step 4 is as follows:

[0043]

[0044] In the above formula: η, 2σ represent the freedom of the UAV, λ represents the maximum coefficient of the safe distance between the UAV and obstacles, l OA represents the center position of the drone, l OD Indicates the distance between the center position of the drone and the obstacle.

[0045] The beneficial effects of the present invention compared to the prior art are as follows: based on the PID algorithm, the present invention plans an inspection path with a shorter total flight range, has excellent obstacle avoidance capabilities, and comprehensively improves the efficiency of the UAV in completing inspection tasks. In addition, by introducing the PID algorithm, the present invention can effectively ensure the flight stability of the UAV while controlling the UAV to smoothly reach the target point according to the planned path to complete all inspection tasks, without being subject to various constraints such as inspection tasks and UAV performance parameters, in order to provide guarantees for the safe operation of the UAV and the smooth completion of the established tasks. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] The present invention will be further described below in conjunction with the accompanying drawings:

[0047] Figure 1 is a flow chart of the method of the present invention;

[0048] Figure 2 It is the block diagram of PID tracking controller;

[0049] Figure 3 For precise hover control block diagram;

[0050] Figure 4 This is the block diagram of variable parameter PID control. DETAILED DESCRIPTION

[0051] like Figures 1 to 4 As shown, the present invention provides a method for automatic planning of inspection paths of UAVs for power transmission lines. Based on the PID algorithm, a reasonable and short inspection path with excellent obstacle avoidance and precise hovering control is planned. The specific implementation steps are as follows: In the following steps of the intelligent automatic planning method for the inspection path of UAVs for power transmission lines, step 1 is the positioning of the landmark points, steps 2 to 4 are the intelligent planning of the inspection path, and steps 5 and 6 are the inspection process.

[0052] Step 1: Arrange drone inspection points along the transmission line, and install a mobile receiving station at each inspection point. The mobile receiving station is used to obtain the location information of the inspection points and cooperate with the base station. Then obtain the location information of the drone inspection points of the transmission line, and use the data transmission link to transmit the acquired information to the receiver of the mobile receiving station. The mobile receiving station calculates the coordinates of the corresponding inspection points relative to the base station based on the observation values ​​through the differential positioning method, and calculates the coordinates of the inspection points based on the coordinates of the base station.

[0053] The calculation process of the coordinates of the inspection mark points is as follows: the carrier integer ambiguity of the UAV inspection mark points is obtained through the linear combination model, the coefficients are calculated through the least squares adjustment method, and finally the carrier side phase pseudorange observation is realized. Then, the coordinate positioning of the inspection mark points is completed through the three-sided positioning method.

[0054] Specifically, the linear combination model represented by formula (1) is used to calculate the carrier integer ambiguity of all inspection mark points set on the transmission line:

[0055]

[0056] In the above formula: N n-1，n is the carrier ambiguity after linear combination, ΔX n-1，n is the real-time position of the inspection mark point, ΔY n-1，n is the deviation between the inspection mark point and the reference station, and their coefficients are represented by a and b respectively. By calculating the two coefficients by the least squares adjustment method, the carrier integer ambiguity of the inspection mark point can be obtained, which is substituted into formula (2) to obtain the carrier side phase pseudorange.

[0057] λφ i (t) = ρ i (t)-I+e i (t)+T i (t)+C[dt i (t)-dt i-1 (t)]-λN i +M i (t)+ε i (t) (2);

[0058] In the above formula: λ represents the phase wavelength, φ i (t) represents the fuzzy position of the phase observation landmark, ρ i (t) represents the geometric distance between the marker points, I represents the ionospheric error, e i (t) represents the fuzzy error, T i (t) represents the tropospheric error, C represents the speed of light, dt i (t) represents the clock error of the mobile receiver, N i Indicates the integer ambiguity on the carrier side, M i (t) represents the multipath effect error, ε i (t) represents the noise of the mobile receiver.

[0059] The above formula is used to obtain the fuzzy data of the positioning of the transmission line UAV inspection mark point. Based on the fuzzy data, the coordinate positioning of the mark point is completed by the three-side positioning method shown in formula (3):

[0060]

[0061] In the above formula: (x, y) represents the position of node A, node A is the unknown inspection mark point position coordinate of the drone, (x1, y1), (x2, y2) and (x3, y3) represent the position coordinates of nodes B, C and D respectively, the distances between node A and nodes B, C and D are λ1, λ2, λ3 respectively, where nodes B, C, D represent any three known inspection mark points around node A respectively.

[0062] Convert equation (3) into the linear matrix equation shown in equation (4), and solve it. The result is the location information of the transmission line UAV inspection mark point.

[0063]

[0064] Step 2: Based on the PID parameters, the dynamic model of the UAV height and attitude is constructed to clearly reflect the UAV motion state. The motion law of the UAV on the transmission line is obtained through the Newton-Euler theory as shown in formula (5).

[0065]

[0066] In the above formula: θ and φ represent the three attitude angles of the drone, namely the yaw angle, pitch angle and roll angle. Respectively represent the angular acceleration of the UAV in the yaw, pitch and roll directions, I xx ,I yy ,I zz represent the moments of inertia about the x, y, and z axes, respectively. Respectively represent the acceleration of the x, y, and z axes, b represents the lift coefficient, d represents the drag coefficient, U1 represents the upward pull, U2, U3, and U4 represent the moments of the pitch, roll, and yaw motions of the drone, g represents the acceleration of gravity, m represents the mass of the drone, ω1, ω2, ω3, and ω4 represent the angular velocities of the four rotors of the drone, and the rotor transfer function G(s) is shown in formula (6):

[0067] G(s)=K / (T s +1) (6);

[0068] In the above formula: K represents the gain, which is obtained by testing the propeller lift of the drone, T s Represents the time constant. When the speed step response reaches 0.63 times the steady-state value, the corresponding time is T s , then the transfer function of the UAV motor rotor is obtained by subtracting the obtained T s Substitute , K into formula (6) to obtain the result.

[0069] The dynamic model of the drone altitude is the drone altitude control model, and its construction steps are as follows:

[0070] Let the x and y axes remain unchanged and only consider the change of the z axis. According to formula (5), the z axis error value e can be obtained as shown in the following formula: Z Calculation process:

[0071] e Z =Z ref -Z>0 (7);

[0072] In the above formula: Z ref - represents the ideal height of the drone, and Z represents the actual height of the drone. When the drone is hovering at a specified height, the constant K1 can be used to replace By Laplace transform processing formula (5) The expression of can obtain the result of formula (8):

[0073]

[0074] At the same time, the PID transfer function shown in formula (9) is obtained:

[0075] L(s)=ρ T γ(s) (9);

[0076] In the above formula: T =[K p , K i , K d ], where K p , K i , K d These are PID algorithm parameters.

[0077]

[0078] The dynamic model of the UAV attitude is the UAV attitude control model. The yaw angle, pitch angle and roll angle have a great influence on it. According to formula (5), the transfer function of each attitude angle described by the following formula can be obtained by using Laplace transform:

[0079]

[0080] In the above formula: L represents the distance between the center of mass of the drone and the center of the rotor.

[0081] The error calculation process of the PID algorithm to control each attitude angle is shown in formula (11):

[0082]

[0083] In the above formula: θ ref , θ ref Respectively represent the ideal value of each attitude angle control, θ, θ represents the input value of each attitude angle, and the closed-loop transfer function of each attitude angle is shown in formula (12):

[0084]

[0085] Step 3: Design a path planning and tracking controller based on the PID algorithm, and use the inner and outer loop control method to obtain the PID tracking controller block diagram of the drone. The PID algorithm can effectively suppress the impact of external interference on the drone by adjusting the proportional, integral and differential coefficients, so that the transmission line drone inspection motion tracking control can achieve better stability. According to the transmission line drone dynamics model created in step 2, the inner and outer loop control scheme is adopted to obtain the following Figure 2 The block diagram of the UAV PID tracking controller is shown.

[0086] Figure 2 In, Z ref represents the ideal height of the input drone, Ω c,i represents the desired propeller speed, σ c,i Indicates the throttle command, P, d υ represents the three-dimensional coordinates and speed measured by the drone, Θ, b ω Represents the attitude angle and angular velocity measured by the drone, b T c represents the desired tension, τ c Indicates the desired three-axis torque. In the PID tracking controller, the inner loop is used to control the drone attitude, and the outer loop is used to control the drone position. The following formula is used to describe the PID tracking controller structure:

[0087]

[0088] In the above formula: the input and output of the PID tracking controller are represented by e and u respectively, and k P , k I , k D Represent the proportional, integral and differential coefficients respectively, T I , T D represent the integral and differential time constants respectively.

[0089] Step 4: Assume the center position of the drone and its distance from the obstacle, respectively using l OA , l OD If , the following formula is used to describe the safe distance S of the drone inspection flight in any direction:

[0090]

[0091] In the above formula: η, 2σ represent the freedom of the UAV, λ represents the maximum coefficient of the safe distance between the UAV and obstacles, l OA represents the center position of the drone, l OD Represents the distance between the center position of the drone and the obstacle. OA , l OD Finally, the following process is used to complete the transmission line UAV inspection path planning.

[0092] Step 4-1: When planning the transmission line UAV inspection path, the acceleration and speed of the UAV cannot exceed the maximum capacity of the UAV. Divide the distance between the starting point and the target point of the UAV into several segments. Use the coordinate transformation matrix shown in formula (15) to rotate the coordinate system to the specified angle with the y-axis as the rotation center, and rotate the x-axis to the direction of the connection line to obtain the transformed coordinate system x'y'z'. Calculate the step length of each segment according to the specified transmission line UAV inspection path planning accuracy standard and UAV performance constraints.

[0093]

[0094] In the above formula: α represents the X-axis and The angle between in Indicates the distance between the starting point and the target point of the drone. The initial coordinate system coordinates are represented by (x, y, z), and the transformed coordinate system coordinates are (x', y', z').

[0095] Step 4-2: The starting point and target point of the drone are represented by P s , P t Said, yes Divide, the number of parts is represented by n, each segment and The straight line perpendicular and parallel to the Z' axis is denoted by l k Represents. Through path smoothing and path pruning, we can obtain l k The best path point p on k (x k , z k ), connecting the best path points of each section, that is, the planned transmission line UAV inspection path, which is specifically described by formula (16):

[0096] P={P s ,p1(x1,z1),p2(x1,z1),...,p n-1 (x n-1 , z n-1 ), P t} (16).

[0097] Step 5: Get the current altitude and posture information of the drone. Determine the current position and posture parameters of the drone through IMU, relative position, quaternion and other information. Upload the expected position of the first mission point of the drone, and calculate the position and angle deviation between the current drone and the first mission point.

[0098] Specifically, the current position and attitude parameters of the drone are determined by subscribing to GPS, IMU, relative position, quaternion, Euler angle and other information through the ROS node. Fly to the first mission point. Upload the expected height and attitude information of the first mission point, and calculate the position and angle deviation between the current drone and the first mission point. The drone sets the flight speed according to the measured relative position deviation and angle deviation, and flies to the first mission point.

[0099] Step 6: Set the deviation threshold. After the drone reaches the mission point, it will measure the relative position deviation and angle deviation, and adjust the drone's height and posture according to the deviation threshold. If it is less than the deviation threshold, the drone will hover at the mission point to perform tasks such as taking photos. Otherwise, the drone will continue to adjust its speed and change its posture until the relative deviation is less than the threshold, and then it will hover accurately at a fixed position to perform the task, and then iterate steps 5 and 6 to complete the automatic inspection task. The specific implementation steps are as follows:

[0100] Step 6-1: After the inspection route of the drone is automatically planned, the operation task is carried out and the drone is precisely hovered and controlled by visual servo. Specifically, it includes:

[0101] (1) When the drone takes off, the image to be taken is uploaded to the controller. The inspection drone is equipped with a camera and uses machine vision technology to identify the towers, pole number plates, insulators and assembly hardware on the transmission line and obtain their relative position information of the drone, that is, the relative position information of the drone is obtained.

[0102] (2) The acquired position information is compared with the image data uploaded to the controller. If the deviation is large, the deviation is sent to the flight controller of the hovering drone, and then the controller outputs it to the actuator. This process is repeated to ensure that the inspection drone reaches the optimal hovering position for shooting.

[0103] (3) The variable parameter PID control algorithm is used to control the drone to hover precisely at the optimal shooting position. The implementation process is as follows: Figure 3 shown.

[0104] For the precise hovering control of the inspection drone, a piecewise linear variable parameter PID algorithm is used to solve the contradiction between global nonlinearity and local linearity. Figure 4 As shown in the figure. By fitting the platform response curves under different speeds / accelerations, the control parameter iteration model is established to achieve the adaptability of the algorithm. This fundamentally improves the dynamic characteristics of the control system, increases the system response frequency, and achieves precise hovering of the inspection drone.

[0105] Step 6-2: The inspection iteration process means that after the UAV completes the relevant operations of the current task point along the automatic inspection route, it updates this task to the current point of the UAV, obtains the next task point as the expected point, compares it with the set deviation threshold, adjusts the height and posture of the UAV, and iterates steps 5 and 6 until all task points are inspected and tasks are completed. The UAV returns and the inspection task is successfully completed.

[0106] The refined inspection of transmission line by UAV mainly targets the panoramic view of line towers, tower heads, pole number plates, foundations, insulators and assembly hardware, ground wires, large and small side channels of optical cables, and other components for shooting and inspection. The camera parameters should be set reasonably during shooting to ensure accurate focus, reasonable exposure, no blur, and the shooting target should be in the middle of the picture. Another precise hovering control proposed by the present invention can meet the standard of accurate and clear shooting of transmission line pictures, hover accurately at a fixed position, and ensure that the inspection UAV can effectively inspect the transmission line. The automatic path planning method and fixed-point precise hovering control are applicable to all transmission line path planning.

[0107] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein with equivalents. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for automatic planning of a UAV inspection path for a power transmission line, characterized in that: The following steps are involved: Step 1: Obtain the location information of the inspection mark point of the transmission line by drone, and use the data transmission link to transmit the acquired information to the receiver of the mobile receiving station set at the corresponding inspection mark point. The mobile receiving station calculates the coordinates of the inspection mark point relative to the reference station based on the observation value by differential positioning method, and calculates the coordinates of the inspection mark point based on the coordinates of the reference station; Step 2: Construct the dynamic model of the altitude and attitude of the drone based on the PID parameters; Step 3: Design a path planning and tracking controller based on the PID algorithm, and use the inner and outer loop control method to obtain the PID tracking controller block diagram of the UAV; Step 4: The safe distance for drone inspection flight in any direction is obtained by the center position of the drone and its distance from obstacles, and then the coordinate transformation matrix is ​​used to complete the transmission line drone inspection path planning; Step 5: Calculate the current altitude and attitude of the drone, and measure the position and angle deviation between the current drone and the corresponding mission point; Step 6: Set the deviation threshold. After the UAV reaches the mission point, it measures the relative position deviation and angle deviation, and adjusts the UAV height and posture according to the deviation threshold. If it is less than the deviation threshold, the UAV hovers to the mission point to perform the mission; otherwise, the UAV continues to adjust the speed and change the posture until the relative deviation is less than the deviation threshold, and then hovers precisely at the fixed position to perform the mission, and then iterates steps 5 and 6 to complete the automatic inspection task.

2. The method for automatic planning of a UAV inspection path for a power transmission line according to claim 1, characterized in that: The calculation process of the inspection mark point coordinates in step 1 is as follows: the carrier integer ambiguity of the drone inspection mark point is obtained through the linear combination model, the coefficient is calculated through the least squares adjustment method, and finally the carrier side phase pseudorange observation is realized. Then, the coordinate positioning of the inspection mark point is completed through the three-sided positioning method.

3. The method for automatic planning of a UAV inspection path for a power transmission line according to claim 1 is characterized in that: The UAV altitude dynamics model constructed in step 2 is the UAV altitude control model, and its expression is as follows: In the above formula: Z(s) represents the actual hovering height of the drone after Laplace transform processing, K1 is a constant, K represents the gain, T s represents the time constant; The expression of the PID transfer function L(s) of the UAV altitude control model is as follows: L(s)=ρ T c(s); In the above formula: T =[K p , K i , K d ], where K p , K i , K d These are PID algorithm parameters.

4. The method for automatic planning of a UAV inspection path for a power transmission line according to claim 1, characterized in that: The UAV attitude dynamics model constructed in step 2 is the UAV attitude control model, which includes the transfer functions of the three attitude angles of the UAV yaw angle, pitch angle, and roll angle, and its expression is: In the above formula: θ(s) is the pitch angle transfer function, φ(s) is the roll angle transfer function, is the yaw angle transfer function, U2(s) is the pitch torque transfer function of the UAV, U3(s) is the roll torque transfer function of the UAV, U4(s) is the yaw torque transfer function of the UAV, L is the distance between the center of mass of the UAV and the center of the rotor, I xx ,I yy , Izz represent the moment of inertia about the x, y, and z axes respectively; The error calculation process of the PID algorithm to control each attitude angle is shown in the following formula: In the above formula: θ ref , θ ref Respectively represent the ideal value of each attitude angle control, θ, θ represents the input value of each attitude angle, and the expression of the closed-loop transfer function of each attitude angle is as follows: In the above formula: T s Represents the time constant.

5. The method for automatic planning of a UAV inspection path for a power transmission line according to claim 1, characterized in that: The expression of the PID tracking controller in step 3 is as follows: In the above formula: the input and output of the PID tracking controller are represented by e and u respectively, and k P , k I , k D Represent the proportional, integral and differential coefficients respectively, T I 、T D represent the integral and differential time constants respectively.

6. The method for automatic planning of a UAV inspection path for a power transmission line according to claim 1, characterized in that: The specific implementation process of step 4 is as follows: Step 4-1: Divide the distance between the starting point and the target point of the drone into several segments. Use the coordinate transformation matrix shown in the following formula to rotate the coordinate system to the specified angle with the y-axis as the rotation center, and rotate the x-axis to the direction of the connection line to obtain the transformed coordinate system x'y'z'. Calculate the step length of each segment according to the specified transmission line drone inspection path planning accuracy standard and drone performance constraints; In the above formula: α represents the X-axis and The angle between in Indicates the distance between the starting point and the target point of the drone. The initial coordinate system coordinates are represented by (x, y, z), and the transformed coordinate system coordinates are (x', y', z'); Step 4-2: The starting point and target point of the drone are represented by P s , P t Said, yes Divide, the number of parts is represented by n, and each segment is The straight line perpendicular and parallel to the Z' axis is denoted by l k It means that the best path point p on lk is obtained through path smoothing and path pruning. k (x k , z k ), connect the best path points of each section, that is, the expression of the planned transmission line UAV inspection path P is as follows: P={P s ,p1(x1,z1),p2(x1,z1),...,p n-1 (x) n-1 ,z n-1 ),P t }。 7. The method for automatic planning of a UAV inspection path for a power transmission line according to claim 1, characterized in that: The specific implementation process of step 6 is as follows: (1) When the drone takes off, the image to be taken is uploaded to the drone's flight controller. The inspection drone is equipped with a camera and uses machine vision technology to identify the towers, pole number plates, insulators and assembly hardware on the transmission line and obtain their relative position information of the drone, that is, the relative position information of the drone to the shooting target is obtained; (2) The acquired position information is compared with the image data uploaded to the controller. If the deviation is large, the deviation is sent to the flight controller of the hovering UAV, and then the flight controller outputs it to the actuator. This process is repeated to ensure that the inspection UAV reaches the optimal hovering position for shooting; (3) The variable parameter PID control algorithm is used to control the UAV to hover precisely at the optimal shooting position.

8. The method for automatic planning of a UAV inspection path for a power transmission line according to claim 7, characterized in that: The inspection iteration process means that after the UAV completes the relevant operations of the current task point along the automatic inspection route, it updates this task to the current point of the UAV, obtains the next task point as the expected point, compares it with the set deviation threshold, adjusts the height and posture of the UAV, and iterates steps 5 and 6 until all task points are inspected and tasks are completed. The UAV returns and the inspection task is successfully completed.

9. The method for automatic planning of a UAV inspection path for a power transmission line according to claim 7, characterized in that: The precise hovering control of the inspection UAV adopts a piecewise linear variable parameter PID algorithm. By fitting the platform response curve under different speeds / accelerations, a control parameter iteration model is established to achieve the adaptability of the algorithm.

10. The method for automatic planning of a UAV inspection path for a power transmission line according to claim 1, characterized in that: The expression of the safe distance L of the drone inspection flight in any direction in step 4 is as follows: In the above formula: η, 2σ represent the freedom of the UAV, λ represents the maximum coefficient of the safe distance between the UAV and obstacles, l OA represents the center position of the drone, l OD Indicates the distance between the center position of the drone and the obstacle.

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