Visual servo control system applied to power transmission line maintenance robot

Abstract

The invention discloses a visual servo control system applied to a power transmission line maintenance robot. The visual servo control system specifically comprises a visual sensing module used for collecting high-definition images and video data of a power transmission line in real time; the data processing module is used for preprocessing the collected data; the fault detection module is used for obtaining a fault detection result through calculation; the fault grading module is used for grading the fault detection result obtained through calculation; the automatic repairing module is used for judging whether a repairing mechanism is started or not according to the identified fault level and trying to solve the fault; and when the automatic repair mechanism cannot solve the fault, the system gives an alarm and notifies a worker to carry out maintenance. According to the invention, an efficient and reliable power transmission line maintenance solution is provided by integrating a visual sensing technology, an image processing technology, a fault detection algorithm and an automatic repair mechanism.

Description

Technical Field

[0001] The present invention relates to the technical field of maintenance robots, and in particular to a visual servo control system applied to a power transmission line maintenance robot. Background Art

[0002] With the rapid development of the power system and the continuous increase in electricity demand, the safe and stable operation of transmission lines is particularly important. The operating environment of transmission lines is complex, and they are often faced with various severe weather and natural conditions such as high temperature, high pressure, wind, rain and lightning. The maintenance of transmission lines is a high-risk and high-intensity task. The traditional manual maintenance method not only has the problem of low efficiency, but also has personnel safety hazards. Therefore, the research and development and application of intelligent and automated transmission line maintenance robots have become an inevitable trend. The maintenance robot is an intelligent device that can autonomously walk, detect and repair on high-altitude transmission lines. It can replace manual high-risk and difficult line maintenance operations, greatly improving the efficiency and safety of maintenance. At present, maintenance robots have been widely used in the inspection and maintenance of various transmission lines.

[0003] These robots are usually equipped with various sensors and tools, and can walk on transmission lines for inspection and maintenance. As one of the core components of the transmission line maintenance robot, the visual servo control system is responsible for the robot's positioning, navigation and fault detection in complex environments. The existing visual servo control system has improved the efficiency and safety of transmission line maintenance to a certain extent, but the collection and transmission of high-definition image and video data need to remain stable and efficient in complex environments. Secondly, the image data of the transmission line has the characteristics of high resolution and large data volume. How to efficiently analyze this data is the key to realizing automated maintenance. Summary of the invention

[0004] The purpose of this section is to summarize some aspects of embodiments of the present invention and briefly introduce some preferred embodiments. Some simplifications or omissions may be made in this section and the specification abstract and the invention title of this application to avoid blurring the purpose of this section, the specification abstract and the invention title, and such simplifications or omissions cannot be used to limit the scope of the present invention.

[0005] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0006] In a first aspect, an embodiment of the present invention provides a visual servo control system applied to a power transmission line maintenance robot, specifically comprising:

[0007] Visual sensing module: used to collect high-definition images and video data of power transmission lines in real time;

[0008] Data processing module: pre-process the collected data;

[0009] Fault detection module: obtains fault detection results through calculation;

[0010] Fault classification module: classifies fault detection results according to their calculated levels;

[0011] Automatic repair module: determines whether to start the repair mechanism based on the identified fault level and tries to solve the fault;

[0012] Alarm and notification module: When the automatic repair mechanism cannot solve the fault, the system will issue an alarm and notify the staff to carry out maintenance.

[0013] As a preferred solution of the visual servo control system applied to the power transmission line maintenance robot described in the present invention, the preprocessing of the data includes denoising, enhancement and correction.

[0014] In a second aspect, an embodiment of the present invention further provides a visual servo control method applied to a power transmission line maintenance robot, which specifically includes the following steps:

[0015] S1, collect high-definition images and video data of power transmission lines through visual sensors;

[0016] S2, preprocessing the collected image and video data;

[0017] S3, automatically detecting faults in the transmission line through a fault detection algorithm;

[0018] S4. Classify the detected faults;

[0019] S5. According to the identified fault level, the automatic repair mechanism is started to solve the fault;

[0020] S6. When the automatic repair mechanism cannot solve the fault, the system will issue an alarm and notify the staff to carry out maintenance.

[0021] As a preferred solution of the visual servo control method for a power transmission line maintenance robot described in the present invention, the fault detection algorithm includes a fault detection index F, and the specific calculation formula of F is as follows:

[0022]

[0023] Among them A ij It is used to measure the difference between the current image pixel and the expected image pixel; B ij It is used to measure the difference of edge features of an image; C ij Indicates the adjustment of fault type weight parameters to image edge features.

[0024] As a preferred solution of the visual servo control method for a transmission line maintenance robot described in the present invention, wherein: the A ij The specific calculation formula is as follows:

[0025]

[0026] Where I represents the input image data matrix, with dimensions of M×N; represents the expected normal image data matrix, with dimensions of M×N; σ represents the sensitivity parameter of the detection algorithm.

[0027] As a preferred solution of the visual servo control method for a transmission line maintenance robot described in the present invention, wherein: the B ij The specific calculation formula is as follows:

[0028]

[0029] Where α is the image feature difference weight parameter; β is the image edge feature weight parameter.

[0030] As a preferred solution of the visual servo control method for a transmission line maintenance robot described in the present invention, wherein: the C ij The specific calculation formula is as follows:

[0031] C ij =γ·E(I ij )

[0032] Where γ is the fault type weight parameter.

[0033] As a preferred solution of the visual servo control method for a transmission line maintenance robot described in the present invention, wherein: the fault detection algorithm further includes a fault detection result D, and the specific calculation formula of D is as follows:

[0034]

[0035] σ represents the sensitivity parameter of the detection algorithm.

[0036] As a preferred solution of the visual servo control method for a transmission line maintenance robot described in the present invention, wherein: the specific evaluation criteria for the D are as follows:

[0037] D≈0: No fault;

[0038] 0<D≤0.5: Minor fault;

[0039] 0.5<D≤1.5: Severe fault;

[0040] D>1.5: Serious fault.

[0041] As a preferred solution of the visual servo control method for a power transmission line maintenance robot described in the present invention, the measures taken for the faults of different levels are as follows:

[0042] No fault: The pixel values ​​in the image are almost the same as the expected values, and the image features and edge features are normal. The system does not detect a fault and does not take any action.

[0043] Minor faults: deviations of a few pixels or slight changes in edge features in the image require further inspection to determine whether they need to be repaired immediately;

[0044] Moderate fault: deviation of more pixels in the image or obvious changes in edge features, it is recommended to carry out repairs and further diagnosis;

[0045] Serious fault: The system detects a serious fault. There are significant abnormalities in the image, and immediate repair measures are required and an alarm is issued.

[0046] Beneficial effects of the present invention:

[0047] By integrating visual sensing technology, image processing technology, fault detection algorithms and automatic repair mechanisms, an efficient and reliable transmission line maintenance solution is provided. At the same time, advanced fault detection algorithms are used to analyze the collected image and video data, which improves the accuracy of fault detection and reduces false alarms and missed alarms. Its main purpose is to realize the automated inspection and maintenance of transmission lines, improve work efficiency and safety, and ensure the reliability and stability of power grid operation through real-time fault detection and processing. The automatic repair mechanism and timely alarm prompts reduce the time and cost of fault handling. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative labor. Among them:

[0049] Figure 1 This is a structural diagram of a visual servo control system applied to a power transmission line maintenance robot proposed by the present invention;

[0050] Figure 2 A flowchart of a visual servo control method for a power transmission line maintenance robot proposed by the present invention;

[0051] Figure 3 This is a flow chart of fault level classification of the present invention. DETAILED DESCRIPTION

[0052] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the accompanying drawings.

[0053] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0054] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The term "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive with other embodiments.

[0055] Reference Figure 1-3 The present invention provides a visual servo control system for a power transmission line maintenance robot, specifically comprising:

[0056] Visual sensing module: used to collect high-definition images and video data of power transmission lines in real time;

[0057] Data processing module: pre-process the collected data;

[0058] Fault detection module: obtains fault detection results through calculation;

[0059] Fault classification module: classifies fault detection results according to their calculated levels;

[0060] Automatic repair module: determines whether to start the repair mechanism based on the identified fault level and tries to solve the fault;

[0061] Alarm and notification module: When the automatic repair mechanism cannot solve the fault, the system will issue an alarm and notify the staff to carry out maintenance.

[0062] Among them, data preprocessing includes denoising, enhancement, and correction to improve data quality and the accuracy of subsequent processing.

[0063] This embodiment also provides a visual servo control method applied to a power transmission line maintenance robot, which specifically includes the following steps:

[0064] S1, collect high-definition images and video data of power transmission lines through visual sensors;

[0065] S2, preprocessing the collected image and video data;

[0066] S3, automatically detecting faults in the transmission line through a fault detection algorithm;

[0067] S4. Classify the detected faults;

[0068] S5. According to the identified fault level, the automatic repair mechanism is started to solve the fault;

[0069] S6. When the automatic repair mechanism cannot solve the fault, the system will issue an alarm and notify the staff to carry out maintenance.

[0070] Furthermore, the fault detection algorithm includes a fault detection index F, and the specific calculation formula of F is as follows:

[0071]

[0072] Among them A ij It is used to measure the difference between the current image pixel and the expected image pixel; B ij It is used to measure the difference of edge features of an image; C ij It represents the adjustment of the fault type weight parameter to the edge features of the image. F represents the fault detection index of each pixel. The higher the value, the greater the possibility of fault. It is obtained by weighing the difference between the pixel and the expected value, the edge features and the weighted comprehensiveness of the fault type.

[0073] Furthermore, A ij The specific calculation formula is as follows:

[0074]

[0075] Where I represents the input image data matrix, with a dimension of M×N; represents the expected normal image data matrix with a dimension of M×N; σ represents the sensitivity parameter of the detection algorithm, which reflects the abnormality of the image.

[0076] Furthermore, B ij The specific calculation formula is as follows:

[0077]

[0078] Among them, α is the image feature difference weight parameter; β is the image edge feature weight parameter. The edge features are extracted by the second-order derivative, and their weights are adjusted using the exponential sweet potato, which emphasizes the importance of edge features in fault detection.

[0079] Furthermore, C ij The specific calculation formula is as follows:

[0080] C ij =γ·E(I ij )

[0081] where γ is the weight parameter of the fault type, which is weighted according to the fault type by extracting the edge feature E(I ij ), reflecting the influence of different fault types on the detection result.

[0082] Furthermore, the fault detection algorithm also includes the fault detection result D, and the specific calculation formula of D is as follows:

[0083]

[0084] σ represents the sensitivity parameter of the detection algorithm, which represents the fault detection result of the entire image. The higher the value, the greater the possibility of system failure. By integrating and accumulating the detection indexes of the entire image and performing smoothing processing through the logarithmic function, the final detection result is obtained.

[0085] Furthermore, the specific evaluation criteria of D are as follows:

[0086] D≈0: No fault;

[0087] 0<D≤0.5: Minor fault;

[0088] 0.5<D≤1.5: Severe fault;

[0089] D>1.5: Serious fault; Classify the fault level according to the calculation result of D.

[0090] Furthermore, the measures for different levels of faults are as follows:

[0091] No fault: The pixel values in the image have almost no difference from the expected values, the image features and edge features are also normal, the system does not detect any faults, and no measures are taken;

[0092] Minor fault: There are deviations in a small number of pixels or small changes in edge features in the image, and further inspection is required to determine whether immediate repair is needed;

[0093] Moderate fault: There are deviations in more pixels or obvious changes in edge features in the image. It is recommended to perform maintenance and further diagnosis;

[0094] Serious fault: The system detects a serious fault, there are significant abnormal situations in the image, immediate repair measures need to be taken and an alarm needs to be issued. Corresponding measures are taken according to different fault levels to improve the reliability and safety of the system.

[0095] 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 preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A visual servo control system for a power transmission line maintenance robot, characterized in that: Specifically include: Visual sensing module: Used to collect high-definition images and video data of the transmission line in real time; Data processing module: Preprocess the collected data; Fault detection module: Obtain the fault detection result through calculation; Fault level classification module: Classify the level according to the calculated fault detection result; Automatic repair module: Judge whether to start the repair mechanism according to the identified fault level and try to solve the fault; Alarm and notification module: When the automatic repair mechanism cannot solve the fault, the system issues an alarm and notifies the staff for maintenance.

2. According to claim 1, a visual servo control system for a power transmission line maintenance robot is characterized in that: The preprocessing of the data includes denoising, enhancement, and correction.

3. A visual servo control method for a power transmission line maintenance robot, based on a visual servo control system for a power transmission line maintenance robot according to any one of claims 1-2, characterized in that: Specifically include the following steps: S1. Collect high-definition images and video data of the transmission line through the visual sensor; S2. Preprocess the collected image and video data; S3. Automatically detect faults in the transmission line through the fault detection algorithm; S4. Classify the detected faults; S5. According to the identified fault level, start the automatic repair mechanism to solve the fault; S6. When the automatic repair mechanism cannot solve the fault, the system issues an alarm prompt and notifies the staff for maintenance.

4. The visual servo control method for a power transmission line maintenance robot according to claim 3, characterized in that: The fault detection algorithm includes a fault detection index F, and the specific calculation formula of F is as follows: Among them A ij It is used to measure the difference between the current image pixel and the expected image pixel; B ij It is used to measure the difference of edge features of an image; C ij Indicates the adjustment of fault type weight parameters to image edge features.

5. The visual servo control method for a power transmission line maintenance robot according to claim 4, characterized in that: The A ij The specific calculation formula is as follows: Where I represents the input image data matrix, with a dimension of M×N; represents the expected normal image data matrix with dimension M×N; σ represents the sensitivity parameter of the detection algorithm.

6. The visual servo control method for a power transmission line maintenance robot according to claim 5, characterized in that: B ij The specific calculation formula is as follows: Where α is the image feature difference weight parameter; β is the image edge feature weight parameter.

7. The visual servo control method for a power transmission line maintenance robot according to claim 6, characterized in that: The C ij The specific calculation formula is as follows: C ij =γ·E(I ij ) Where γ is the fault type weight parameter.

8. The visual servo control method for a power transmission line maintenance robot according to claim 7, characterized in that: The fault detection algorithm also includes a fault detection result D, and the specific calculation formula of D is as follows: σ represents the sensitivity parameter of the detection algorithm.

9. The visual servo control method for a power transmission line maintenance robot according to claim 8, characterized in that: The specific evaluation criteria of D are as follows: D≈0: No fault; 0<D≤0.5: Minor fault; 0.5<D≤1.5: Severe fault; D>1.5: Serious fault.

10. The visual servo control method for a power transmission line maintenance robot according to claim 9, characterized in that: The measures for different levels of faults are as follows: No fault: The pixel values in the image have little difference from the expected values, and the image features and edge features are also normal. The system does not detect any faults and no measures are taken; Minor fault: There are deviations in a small number of pixels or small changes in the edge features in the image, and further inspection is required to determine whether immediate repair is needed; Moderate fault: There are deviations in more pixels or obvious changes in the edge features in the image. It is recommended to carry out maintenance and further diagnosis; Serious fault: The system detects a serious fault, and there are significant abnormal situations in the image. Immediate repair measures need to be taken and an alarm is issued.

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