CN118034341A - Unmanned aerial vehicle path planning algorithm for detecting electric power tower insulator - Google Patents
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Abstract
The invention discloses an unmanned aerial vehicle path planning algorithm for detecting an electric power tower insulator, belonging to the technical field of fault detection; the method comprises the following steps: 1. and carrying out point cloud modeling on the power tower. 2. Unmanned aerial vehicle laser point cloud positioning. 3. And identifying and classifying the barriers, and classifying the barriers into three types of power lines, insulators and towers. 4. The expansion radius is set for different types of obstacles according to the dangerous level and the detection level. 5. A linear function is established for each insulator. 6. Establishing a path planning objective function based on multi-objective optimization, adding a plurality of constraints in the objective function, wherein the constraints comprise anti-collision constraints, parallel constraints with insulators, insulator detection angle maximization constraints and insulator detection number ratio maximization constraints. 7. And solving an objective function and executing path planning. The invention solves the problems of low efficiency, high risk, missed detection and short insulator detection angle existing in the existing insulator detection method.
Description
Technical Field
The invention belongs to the technical field of fault detection, and particularly relates to an unmanned aerial vehicle path planning algorithm for detecting an insulator of an electric power tower.
Background
In an electrical power system, an insulator is a key electrical device, which plays an important role in transmission lines and substations. The insulator is used for isolating wires and supporting structures between the high-voltage transmission line and the power equipment, effectively preventing current leakage and arc discharge, and ensuring safe and stable operation of the power system.
In order to ensure the reliability and performance of the insulator, various detection modes are currently adopted to evaluate and monitor the insulator. Currently, common insulator detection methods include, but are not limited to, the following, the first is manual climbing detection, and a detector directly performs visual inspection on an insulator through climbing a power line tower or an insulator string. This method can provide detailed visual information including cracks, contamination, damage, and the like. However, this approach has the problems of high risk and low efficiency. The second type is unmanned aerial vehicle photographing detection, and the remote control unmanned aerial vehicle is used for carrying high-resolution camera equipment to carry out aerial photographing detection on the insulator. Through the maneuverability and the overhead angle of the unmanned aerial vehicle, the image information of the surface of the insulator can be obtained, so that quick evaluation is realized. However, the unmanned aerial vehicle is higher in control technical requirement by the mode, insulators cannot be comprehensively detected, the insulators in the electric power tower are large in number, the insulators are generally cylindrical, and detection of the insulators is easy to occur under the conditions of missed detection and insufficient detection angle.
Disclosure of Invention
The invention aims to provide an unmanned aerial vehicle path planning algorithm for detecting an insulator of a power tower, which aims to solve the technical problems of low efficiency, high risk, missed detection and short insulator detection angle existing in the existing insulator detection method.
In order to achieve the above purpose, the specific technical scheme of the unmanned aerial vehicle path planning algorithm for detecting the power tower insulator is as follows:
An unmanned aerial vehicle path planning algorithm for detecting an electric power tower insulator, comprising the steps of:
s1: point cloud modeling of power towers
Before the first detection, the unmanned aerial vehicle carrying the laser radar is controlled to slowly fly around the power tower, and the flight track of the unmanned aerial vehicle is in a spiral ascending shape. According to the point cloud data obtained when the unmanned aerial vehicle flies, some laser SLAM (simultaneous localization AND MAPPING) methods (such as LOAM (Lidar Odometry AND MAPPING), FAST-LIO (FAST-LIO is an open source algorithm, which is a known technology published in the paper FAST-LIO: A FAST, robust LiDAR-inertial Odometry Package by Tightly-Coupled ITERATED KALMAN FILTER) are used for carrying out point cloud modeling on a power tower, and a power tower coordinate system is established.
S2: unmanned aerial vehicle laser point cloud positioning
And in the step, an insulator detection step is started, laser radar is used for obtaining single-frame point cloud data in the unmanned aerial vehicle detection process, matching is carried out according to the single-frame point cloud obtained in real time and the power tower point cloud, and a positioning algorithm (such as ICP (Iterative Closest Point), NDT (Normal Distribution Transform) and the like) based on a point cloud map is used for obtaining the coordinates of the unmanned aerial vehicle relative to a power tower coordinate system.
S3: obstacle identification and classification
Unmanned aerial vehicle has the demand of avoiding the barrier in the route planning process, and the dangerous coefficient of different barriers is different in the electric power pylon, detects the demand also different, consequently need discern the classification to different barriers. The obstacle point clouds in the power tower are classified into three categories by using a target detection algorithm, namely: power line, insulator, body of the tower.
S4: setting expansion radius for different types of obstacle according to dangerous grade and detection grade
In the last step, the obstacles in the power tower are classified into three types, and the power line is high-voltage equipment with high risk coefficient, so that the expansion radius R1 of the power line needs to be set to be the maximum; the risk coefficient of the insulator is general, but the insulator is taken as a detection target, and the unmanned aerial vehicle is required to carry a detection camera to detect at a shorter distance, so that the expansion radius R2 of the insulator needs to be set to be minimum; the risk coefficient of the tower body is low, and is not a detection target, and the expansion radius R3 of the tower body is set to be moderate. Finally, R1> R3> R2.
S5: establishing a linear function for each insulator
Three-dimensional coordinates of two ends of each insulator are obtained through a point cloud target detection algorithm Wherein S, E represents a start point and an end point, respectively, and n is the number of the insulator. The linear equation of an insulator can be expressed as/>
S6: establishing a path planning objective function based on multi-objective optimization
In order to solve the problems of low efficiency, high risk, missed detection and insufficient insulator detection angle existing in the current insulator detection method, a plurality of constraints, namely anti-collision constraints, parallel constraints with insulators and insulator detection angle maximization constraints, are added in an objective function, and the insulator detection number is limited by the maximization of the ratio.
S6.1: anti-collision restraint
The effect of this constraint is to guarantee the security in the unmanned aerial vehicle testing process, carry out step S3 and step S4, divide into three kinds with the real-time point cloud that unmanned aerial vehicle obtained, be power line point cloud, insulator point cloud, tower body point cloud respectively, acquire the nearest point of unmanned aerial vehicle this moment from these three kinds of point clouds respectively and be Q 1,Q2,Q3, the function of anticollision constraint can be expressed as: f c＝fc1+fc2+fc3, wherein:
Where R 1,R2,R3 is the expansion radius set in S4, respectively, and d (Q 1),d(Q2),d(Q3) is the distance of the drone from Q 1,Q2,Q3, respectively.
S6.2: parallel constraint with insulator
The function of the constraint is to ensure that the path is kept parallel to the insulator when the unmanned aerial vehicle detects the insulator, and the function of the constraint parallel to the insulator can be expressed as: Wherein d i represents the distance between the ith path point planned by the unmanned aerial vehicle and the linear equation of the insulator when the single insulator is detected.
S6.3: insulator detection angle maximization constraint
The insulator is cylindrical, and when unmanned aerial vehicle detects along with insulator parallel track single time, as shown in fig. 2, detects the angle this moment:
Wherein R is the radius of the insulator, and R 2 is the distance from the insulator during unmanned aerial vehicle detection, namely the expansion radius of the insulator mentioned in S4. When the unmanned aerial vehicle detects the insulator for the second time, as shown in fig. 2, the detection angle of the insulator at this time is the sum of the two detection angles minus the detection angle repeated at the time of the two detection. Assuming that in the two detections, the path distance of the two unmanned aerial vehicles is l, the calculation formula of the repetition angle of the two detections is:
the total detection angle when the drone detects for the second time is θ=2α - β. Therefore, the detection angle of the insulator under multiple detection can be calculated as
Where n represents the number of detections. Therefore, the insulator detection angle maximization constraint function can be expressed as f θ＝(2π-θ)2, and the detection angle can be maximized by minimizing the function.
S6.4: maximizing constraint of insulator detection number and duty ratio
The function of the constraint can be expressed as:
Where g represents the number of insulators detected and h represents the total number of insulators in the power tower. Minimizing this constraint function maximizes the insulator count duty cycle.
The overall objective function can be expressed as:
f＝xc*fc+xp*fp+xθ*fθ+xg*fg
Wherein f c、fp、fθ、fg represents each constraint function, and x c、xp、xθ、xg represents the weight ratio of each constraint function.
S7: solving the objective function and executing path planning
The objective function may be solved by using the existing optimized library functions such as ceres (Ceres Solver), gtsam, etc., which are the prior art, and will not be described in any more detail here, and the path planning may be performed after the objective function is solved.
The unmanned aerial vehicle path planning algorithm for detecting the electric power tower insulator has the following advantages: the invention provides a novel detection mode, and improves the efficiency, safety and integrity of insulator detection.
Drawings
Fig. 1 is an algorithm flow chart of an unmanned aerial vehicle path planning algorithm for detecting an electric power tower insulator according to the present invention.
Fig. 2 is a diagram of a detection effect of an unmanned aerial vehicle path planning algorithm for detecting an insulator of a power tower when the unmanned aerial vehicle detects the insulator at a single time.
Fig. 3 is a diagram of a detection effect of an unmanned aerial vehicle path planning algorithm for detecting an insulator of a power tower when the unmanned aerial vehicle detects the insulator twice.
Detailed Description
For a better understanding of the objects, structures and functions of the present invention, a further detailed description of an unmanned aerial vehicle path planning algorithm for detecting an insulator of a power tower is provided below, in conjunction with the accompanying drawings.
The invention identifies and classifies different obstacle types, gives different obstacle expansion radiuses according to the dangerous grade and the detection requirement, and establishes an anti-collision constraint function.
And establishing related constraint functions of insulator detection, such as parallel constraint with the insulator, insulator detection angle maximization constraint and insulator detection number ratio maximization constraint.
Examples:
An unmanned aerial vehicle path planning algorithm for detecting an electric power tower insulator, comprising the steps of:
Step S1: point cloud modeling of power towers
Before the first detection, controlling the unmanned aerial vehicle carrying the laser radar to slowly fly around the power tower, wherein the flight track of the unmanned aerial vehicle is in a spiral ascending shape; according to the point cloud data obtained when the unmanned aerial vehicle flies, performing point cloud modeling on the power tower by using a laser SLAM (simultaneous localization AND MAPPING) method, and establishing a power tower coordinate system;
Step S2: unmanned aerial vehicle laser point cloud positioning
The method comprises the steps of starting to execute an insulator detection step, obtaining single-frame point cloud data by using a laser radar in the unmanned aerial vehicle detection process, matching the single-frame point cloud obtained in real time with the power tower point cloud, and obtaining coordinates of the unmanned aerial vehicle relative to a power tower coordinate system by using a positioning algorithm based on a point cloud map;
Step S3: obstacle identification and classification
Dividing obstacle point clouds in the power tower into a power line, an insulator and a tower body by using a target detection algorithm;
step S4: setting expansion radius for different types of obstacle according to dangerous grade and detection grade
Setting the expansion radius R1 of the power line to be maximum; setting the expansion radius R2 of the insulator to be minimum; setting the expansion radius R3 of the tower body to be moderate, wherein R1 is more than R3 and more than R2;
step S5: establishing a linear function for each insulator
Three-dimensional coordinates of two ends of each insulator are obtained through a point cloud target detection algorithm Wherein S, E respectively represents a starting point and an ending point, and n is the number of the insulator; the linear equation for the insulator is expressed as:
wherein, the formula (1) is a parameter form of a linear equation, wherein x, y and z are dependent variables, t is a parameter, and other letters are introduced before and are three-dimensional coordinates of two ends of the insulator respectively;
step S6: establishing a path planning objective function based on multi-objective optimization
Adding anti-collision constraint, parallel constraint with an insulator, insulator detection angle maximization constraint and insulator detection number occupation ratio maximization constraint into an objective function;
Anti-collision constraint:
Dividing the real-time point cloud acquired by the unmanned aerial vehicle into a power line point cloud, an insulator point cloud and a tower body point cloud, respectively acquiring the closest points of the unmanned aerial vehicle to the three point clouds as Q 1,Q2,Q3, wherein the function of the anti-collision constraint can be expressed as follows:
fc＝fc1+fc2+fc3 (2)
wherein:
Wherein, R 1,R2,R3 is the expansion radius set in the step S4, and d (Q 1),d(Q2),d(Q3) is the distance between the unmanned plane and Q 1,Q2,Q3;
the parallel constraint with the insulator is expressed as:
Wherein d i represents the distance between the ith path point planned by the unmanned aerial vehicle and the linear equation of the insulator when the single insulator is detected.
Insulator detection angle maximization constraint:
The insulator is cylindrical, and when unmanned aerial vehicle detects along with insulator parallel track single time, detects the angle this moment:
Wherein R is the radius of the insulator, and R 2 is the distance from the insulator during unmanned aerial vehicle detection, namely the expansion radius of the insulator mentioned in the step S4;
When the unmanned aerial vehicle detects the insulator for the second time, the detection angle of the insulator is the sum of the two detection angles minus the repeated detection angle during the two detection; assuming that in the two detections, the path distance of the two unmanned aerial vehicles is l, the calculation formula of the repetition angle of the two detections is:
When the unmanned plane detects for the second time, the total detection angle is theta=2α -beta, so that the detection angle of the insulator under multiple detection can be calculated as
Wherein n represents the number of detections, so the insulator detection angle maximization constraint function can be expressed as f θ＝(2π-θ)2, and minimizing this function can maximize the detection angle.
The maximum constraint of the number of the detected insulators and the ratio is expressed as follows:
Wherein g represents the number of detected insulators, h represents the total number of insulators in the power tower, and minimizing the constraint function can maximize the number of detected insulators.
The overall objective function, expressed as:
f＝xc*fc+xp*fp+xθ*fθ+xg*fg
Wherein, f c、fp、fθ、fg represents the constraint function of the anti-collision term, the parallel constraint term of the path and the insulator, the maximized detection angle term of the insulator and the maximized detection number of the insulator, and x c、xp、xθ、xg represents the weight ratio of the constraint function of the anti-collision term, the parallel constraint term of the path and the insulator, the maximized detection angle term of the insulator and the maximized detection number of the insulator.
Step S7: solving the objective function and executing path planning
And the objective function is solved by using the existing optimized library function, and path planning can be executed after the objective function is solved.
It will be understood that the application has been described in terms of several embodiments, and that various changes and equivalents may be made to these features and embodiments by those skilled in the art without departing from the spirit and scope of the application. In addition, many modifications may be made to adapt a particular situation or material to the teachings of the application without departing from the essential scope thereof. Therefore, it is intended that the application not be limited to the particular embodiment disclosed, but that the application will include all embodiments falling within the scope of the appended claims.
Claims (6)
1. An unmanned aerial vehicle path planning algorithm for detecting an electric power tower insulator, comprising the steps of:
Step S1: point cloud modeling of power towers
Before the first detection, controlling the unmanned aerial vehicle carrying the laser radar to slowly fly around the power tower, wherein the flight track of the unmanned aerial vehicle is in a spiral ascending shape; according to the point cloud data obtained when the unmanned aerial vehicle flies, carrying out point cloud modeling on the power tower by using a laser simultaneous localization AND MAPPING and SLAM method, and establishing a power tower coordinate system;
Step S2: unmanned aerial vehicle laser point cloud positioning
The method comprises the steps of starting to execute an insulator detection step, obtaining single-frame point cloud data by using a laser radar in the unmanned aerial vehicle detection process, matching the single-frame point cloud obtained in real time with the power tower point cloud, and obtaining coordinates of the unmanned aerial vehicle relative to a power tower coordinate system by using a positioning algorithm based on a point cloud map;
Step S3: obstacle identification and classification
Dividing obstacle point clouds in the power tower into a power line, an insulator and a tower body by using a target detection algorithm;
Step S4: setting the expansion radius for different types of obstacles according to the dangerous grade and the detection grade, and setting the expansion radius R1 of the power line to be the maximum; setting the expansion radius R2 of the insulator to be minimum; setting the expansion radius R3 of the tower body to be moderate, wherein R1 is more than R3 and more than R2;
step S5: establishing a linear function for each insulator
Three-dimensional coordinates of two ends of each insulator are obtained through a point cloud target detection algorithm Wherein S, E respectively represents a starting point and an ending point, and n is the number of the insulator; the linear equation for the insulator is expressed as:
wherein formula (1) is a parametric form of a linear equation, where x, y, z are dependent variables and t is a parameter;
step S6: establishing a path planning objective function based on multi-objective optimization
Adding anti-collision constraint, parallel constraint with an insulator, insulator detection angle maximization constraint and insulator detection number occupation ratio maximization constraint into an objective function;
Step S7: solving the objective function and executing path planning
And the objective function is solved by using the existing optimized library function, and path planning can be executed after the objective function is solved.
2. The unmanned aerial vehicle path planning algorithm for detecting electric power tower insulators of claim 1, wherein the collision avoidance constraint in step S6:
Dividing the real-time point cloud acquired by the unmanned aerial vehicle into a power line point cloud, an insulator point cloud and a tower body point cloud, respectively acquiring the closest points of the unmanned aerial vehicle to the three point clouds as Q 1,Q2,Q3, wherein the function of the anti-collision constraint can be expressed as follows:
fc＝fc1+fc2+fc3 (2)
wherein:
Wherein R 1,R2,R3 is the expansion radius set in step S4, and d (Q 1),d(Q2),d(Q3) is the distance between the unmanned plane and Q 1,Q2,Q3, respectively.
3. The unmanned aerial vehicle path planning algorithm for detecting electric tower insulators according to claim 1, wherein the parallel constraint with the insulators in step S6 is expressed as:
Wherein d i represents the distance between the ith path point planned by the unmanned aerial vehicle and the linear equation of the insulator when the single insulator is detected.
4. The unmanned aerial vehicle path planning algorithm for detecting electric power tower insulators according to claim 1, wherein the insulator detection angle maximization constraint in step S6:
The insulator is cylindrical, and when unmanned aerial vehicle detects along with insulator parallel track single time, detects the angle this moment:
Wherein R is the radius of the insulator, and R 2 is the distance from the insulator during unmanned aerial vehicle detection, namely the expansion radius of the insulator mentioned in the step S4;
When the unmanned aerial vehicle detects the insulator for the second time, the detection angle of the insulator is the sum of the two detection angles minus the repeated detection angle during the two detection; assuming that in the two detections, the path distance of the two unmanned aerial vehicles is l, the calculation formula of the repetition angle of the two detections is:
When the unmanned plane detects for the second time, the total detection angle is theta=2α -beta, so that the detection angle of the insulator under multiple detection can be calculated as
Wherein n represents the number of detections, so the insulator detection angle maximization constraint function can be expressed as f θ＝(2π-θ)2, and minimizing this function can maximize the detection angle.
5. The unmanned aerial vehicle path planning algorithm for detecting electric power tower insulators according to claim 1, wherein the insulator detection number-to-ratio maximization constraint in step S6 is expressed as:
Wherein g represents the number of detected insulators, h represents the total number of insulators in the power tower, and minimizing the constraint function can maximize the number of detected insulators.
6. The unmanned aerial vehicle path planning algorithm for detecting electric tower insulators of claim 1, wherein the overall objective function in step S6 is expressed as:
f＝xc*fc+xp*fp+xθ*fθ+xg*fg
Wherein, f c、fp、fθ、fg represents the constraint function of the anti-collision term, the parallel constraint term of the path and the insulator, the maximized detection angle term of the insulator and the maximized detection number of the insulator, and x c、xp、xθ、xg represents the weight ratio of the constraint function of the anti-collision term, the parallel constraint term of the path and the insulator, the maximized detection angle term of the insulator and the maximized detection number of the insulator.
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN118760212A (en) * | 2024-09-06 | 2024-10-11 | 江苏讯汇科技股份有限公司 | UAV power automatic inspection control method based on MSDK technology |
| CN120066087A (en) * | 2025-04-27 | 2025-05-30 | 国网四川省电力公司电力应急中心 | A substation drone inspection path planning and processing method and system based on three-dimensional model |
| CN120066087B (en) * | 2025-04-27 | 2025-07-08 | 国网四川省电力公司电力应急中心 | Substation unmanned aerial vehicle routing inspection path planning processing method and system based on three-dimensional model |
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN118760212A (en) * | 2024-09-06 | 2024-10-11 | 江苏讯汇科技股份有限公司 | UAV power automatic inspection control method based on MSDK technology |
| CN118760212B (en) * | 2024-09-06 | 2024-12-03 | 江苏讯汇科技股份有限公司 | Msdk technology-based unmanned aerial vehicle electric power automatic inspection control method |
| CN120066087A (en) * | 2025-04-27 | 2025-05-30 | 国网四川省电力公司电力应急中心 | A substation drone inspection path planning and processing method and system based on three-dimensional model |
| CN120066087B (en) * | 2025-04-27 | 2025-07-08 | 国网四川省电力公司电力应急中心 | Substation unmanned aerial vehicle routing inspection path planning processing method and system based on three-dimensional model |
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