An outdoor visual positioning system and method for an intelligent fault elimination robot for power transmission lines

Abstract

The present invention discloses an outdoor visual positioning system and method of a power transmission line intelligent fault elimination robot, wherein the system includes an environment perception module, an illumination compensation module, a visual inertial fusion module, a vibration compensation module, an adaptive visual algorithm module, a multi-sensor fusion module, and a path planning and navigation module, wherein the environment perception module is used to collect surrounding environment data, the illumination compensation module dynamically adjusts camera parameters, the visual inertial fusion module is used to compensate for the instability of the robot's visual data during movement or vibration, the vibration compensation module is used to correct the visual positioning deviation caused by line vibration, the adaptive visual algorithm module can dynamically adjust the parameter settings of the visual system, the multi-sensor fusion module fuses sensor data, and the path planning and navigation module performs accurate path planning. The present invention can dynamically adjust camera parameters, can reduce positioning errors, and ensure the positioning accuracy and stability of the robot in a complex environment.

Description

Technical Field

[0001] The present invention relates to the technical field of fault elimination robots, and in particular to an outdoor visual positioning system and method for an intelligent fault elimination robot for power transmission lines. Background Art

[0002] In the power industry, transmission lines are important channels for power transmission, and their safety and stability are directly related to the reliable operation of the entire power grid. However, transmission lines often span a vast area, are exposed to complex outdoor environments, and are subject to long-term wind and rain erosion, temperature changes, light differences, and vibrations caused by natural and human factors. These factors may cause damage or defects to the transmission lines, thereby threatening the safety of the power grid.

[0003] Traditional power transmission line inspection and maintenance work mainly relies on manual inspection, which is not only labor-intensive and inefficient, but also difficult to carry out in harsh environmental conditions, posing great safety risks. With the advancement of science and technology, especially the development of robotics and computer vision technology, intelligent inspection robots for power transmission lines have gradually become a research hotspot. However, when existing intelligent inspection robots perform visual positioning in complex outdoor environments, they often face challenges such as lighting changes, unstable visual data, and line vibration. These problems seriously affect the positioning accuracy and operating efficiency of the robots.

[0004] Specifically, changes in lighting conditions will cause the quality of images collected by the camera to deteriorate, affecting the accuracy of visual positioning; when the robot is moving or subjected to line vibration, the data obtained by the visual sensor will become unstable and erroneous, further reducing positioning accuracy. Summary of the invention

[0005] 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.

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions: an outdoor visual positioning system for an intelligent fault elimination robot for power transmission lines, mainly comprising:

[0007] An environment perception module, which is used to collect surrounding environment data through sensors;

[0008] An illumination compensation module, which dynamically adjusts camera parameters according to illumination data provided by the environment perception module;

[0009] A visual-inertial fusion module, which is used to fuse the data of the visual sensor and the inertial sensor, and uses the visual-inertial odometer technology to compensate for the instability of the visual data of the robot during movement or vibration;

[0010] A vibration compensation module, which is used to analyze the vibration frequency and amplitude of the power transmission line monitored by the environment perception module and correct the visual positioning deviation caused by line vibration;

[0011] An adaptive visual algorithm module, which can dynamically adjust the parameter settings of the visual system according to environmental changes;

[0012] A multi-sensor fusion module, wherein the multi-sensor fusion module adopts a multi-sensor fusion algorithm to fuse sensor data;

[0013] The path planning and navigation module performs accurate path planning by analyzing the current position information of the robot on the transmission line and the surrounding environment information based on the data of multiple sensors.

[0014] As a preferred solution of the outdoor visual positioning system of the intelligent fault-eliminating robot for power transmission lines described in the present invention, the sensors include light sensors, wind speed sensors, temperature and humidity sensors, and vibration sensors. The light sensor is used to monitor the intensity and changes of light in real time. The wind speed sensor is used to collect wind speed information of the environment in which the power transmission lines are located. The temperature and humidity sensor is used to collect temperature and humidity information of the environment in which the power transmission lines are located. The vibration sensor is used to detect the vibration frequency and amplitude of the power transmission lines and determine line changes caused by wind or mechanical movement.

[0015] As a preferred solution of the outdoor visual positioning system of the intelligent power transmission line fault elimination robot described in the present invention, the camera parameters include exposure, gain and contrast.

[0016] As a preferred solution of the outdoor visual positioning system of the intelligent fault elimination robot for power transmission lines of the present invention, it also includes a feedback control module, a remote monitoring module and a fault detection module;

[0017] The feedback control module adjusts the motion parameters of the robot in real time according to the feedback data of each module;

[0018] The remote monitoring module can view the robot's operating status and positioning information on the transmission line in real time through the remote monitoring system, and cooperate with AR technology to help ground operators remotely adjust the robot;

[0019] The fault detection module is used to perform fault detection and report sensor abnormalities or data failures in a timely manner.

[0020] As a preferred solution of the outdoor visual positioning system of the intelligent power transmission line fault elimination robot of the present invention, the dynamic adjustment of camera parameters includes the following steps:

[0021] S11: The system sets a threshold for light changes. When the light intensity changes beyond the set threshold, the dynamic adjustment process is triggered;

[0022] S12: classify the current lighting conditions according to the monitored lighting data, evaluate the type and magnitude of lighting changes through a preset algorithm, and determine whether parameter adjustment is required;

[0023] S13: Calculate the required compensation amount according to the deviation between the current illumination data and the normal working illumination range, and then convert the compensation amount into adjustment values ​​of exposure time, gain and contrast according to a mapping formula;

[0024] S14: calling the control interface of the camera to adjust the exposure time, gain and contrast according to the calculated compensation value;

[0025] S15: The camera feeds back the adjusted image data to the system. The visual system uses the image analysis algorithm to check the brightness, clarity and noise level of the image to determine whether the adjustment effect reaches the expected effect. If the image feedback is not ideal, the system will continue to make fine adjustments and repeatedly adjust the exposure, gain and contrast parameters until the image quality reaches the best effect.

[0026] S16: When the lighting conditions stabilize, the system locks the current parameter settings to avoid unnecessary adjustments and ensure that the vision system continues to provide high-quality images during the lighting stability stage.

[0027] As a preferred solution of the outdoor visual positioning system of the intelligent fault elimination robot for power transmission lines of the present invention, the formula for calculating the required compensation amount is as follows:

[0028] ,

[0029] in:

[0030] Indicates at time The amount of light compensation;

[0031] Indicates the initial time;

[0032] Indicates time;

[0033] The ideal image brightness expected by the camera is set as the standard brightness required by the system;

[0034] Indicates the current time The actual image brightness captured by the lower camera;

[0035] Indicates the time The ambient light intensity is collected in real time through the light sensor;

[0036] Indicates the current time The ambient noise level, measured by an ambient noise detector;

[0037] Indicates the current time The camera gain, a parameter that can be dynamically adjusted by the system;

[0038] Indicates the current time The camera’s exposure time, a parameter that the system can dynamically adjust;

[0039] Indicates the current time The contrast of a camera image is a measure of the difference in image brightness;

[0040] is an exponential decay function, which is used to control the influence of the gain and contrast adjustment at the past moment on the current compensation amount. is the attenuation coefficient, which is a positive value and determines the degree of influence of past adjustments on the current situation. The larger the value, the smaller the influence of past adjustments. Subscripts used to denote summation terms, used for discrete adjustment calculations in the time dimension;

[0041] Represents the square of the difference between ideal brightness and actual brightness. The square amplifies the difference, ensuring that the system can respond to even small deviations in brightness.

[0042] Represents the superposition effect of ambient light intensity and noise, light intensity and noise These are two key factors that affect image quality. The larger the sum of the two, the stronger the environmental impact, and the less compensation is needed for brightness differences;

[0043] Represents the exponential function With gain and contrast The product of reflects their joint influence in the time dimension, and the exponential decay controls the influence of past time on the current compensation. and Determines how much gain and contrast adjust the image;

[0044] Indicates exposure time and ambient light intensity The square root sum of ensures the balance between exposure time and lighting conditions in the compensation calculation to avoid over-adjustment;

[0045] Represents the cumulative adjustment in the time dimension, through an exponential decay function To ensure that the influence of past gain and contrast on the current gradually decreases, the sum expresses the time accumulation effect in the camera parameter adjustment, ensuring that the system can dynamically respond to the environment over time;

[0046] Indicates that from the initial moment To the current time The cumulative compensation amount is calculated by integrating the cumulative effect of brightness difference over time, so that the system can track the changes in lighting and make dynamic adjustments;

[0047] Value range meaning:

[0048] The value range of the formula exist Inside;

[0049] when When it is close to 0, it means that the lighting conditions are ideal and the image quality is close to the expectation, so no major adjustment is needed;

[0050] when When the value increases, it means that the lighting conditions deviate from the expected ones and the system needs to increase the exposure or gain, or make contrast adjustments.

[0051] As a preferred solution of the outdoor visual positioning system of the intelligent power transmission line fault elimination robot of the present invention, the compensation for the instability of the visual data of the robot during movement or vibration comprises the following steps:

[0052] Step 21: Initialize the visual sensor and inertial sensor and ensure that their data timestamps are synchronized. Then, obtain the visual frame sequence data through the visual sensor and obtain the acceleration and angular velocity through the inertial sensor.

[0053] Step 22: Detect and extract feature points in the current image frame, track the matching relationship of feature points between adjacent frames, and record the displacement information of the feature points;

[0054] Step 23: Integrate the collected acceleration and angular velocity data to calculate the position and posture change of the robot at each moment;

[0055] Step 24: Fuse the inertial data and visual data through the Kalman filter or the extended Kalman filter to filter out noise and errors, and update the current position information of the robot according to the fusion result;

[0056] Step 25: Perform visual inertial odometry calculation for each continuous frame. According to the image data provided by the visual sensor and the motion data of the inertial sensor, calculate the posture change of the robot at continuous moments, and add the posture result of each frame with the result of the previous frame to obtain the complete movement trajectory of the robot.

[0057] Step 26: Analyze the high-frequency vibration information in the inertial sensor, identify the motion characteristics of the robot at the moment of vibration, dynamically correct the visual positioning data according to the vibration characteristics, and smooth the noise caused by the high-frequency vibration;

[0058] Step 27: Based on the corrected visual and inertial data, the robot's position information on the transmission line is updated in real time, and the current position information is fed back to the path planning and navigation module to make the next movement decision;

[0059] Step 28: Monitor the positioning accuracy in real time, provide feedback to the control system, and adjust the compensation algorithm and sensor parameters according to environmental changes.

[0060] As a preferred solution of the outdoor visual positioning system of the intelligent power transmission line fault elimination robot of the present invention, the correction of the visual positioning deviation caused by line vibration comprises the following steps:

[0061] Step 31: The sensor monitors the vibration frequency and amplitude of the transmission line in real time, and analyzes the vibration frequency and amplitude in each time period to determine the characteristics of the line vibration;

[0062] Step 32: Obtain the robot's inertial data, ensure time synchronization with the vibration data, and synchronize the inertial data with the vibration sensor data to facilitate subsequent fusion calculations;

[0063] Step 33: Perform spectrum analysis on the vibration data to determine the high-frequency vibration components, and identify the main vibration components that affect the visual positioning accuracy based on the analysis results;

[0064] Step 34: Extract feature points in the image and track them, detect feature point offsets between adjacent frames, compare data from the visual sensor and the inertial sensor, and calculate visual positioning errors;

[0065] Step 35: Establish a vibration compensation model based on frequency and amplitude, calculate the compensation amount, correct the sensor data, and filter out high-frequency interference;

[0066] Step 36: According to the vibration compensation model, adjust the position of the visual feature points in real time, correct the visual data deviation, update the corrected position information, and ensure the visual positioning stability of the robot.

[0067] As a preferred solution of the outdoor visual positioning system of the intelligent power transmission line fault elimination robot of the present invention, wherein: the establishment of a vibration compensation model based on frequency and amplitude and the calculation of the compensation amount include the following steps:

[0068] Step 351: pre-processing the data collected by the vibration sensor installed on the transmission line using a filtering algorithm, and extracting key information of frequency and amplitude from the filtered data;

[0069] Step 352: Perform Fourier transform on the collected vibration data to obtain a spectrum of vibration frequency, classify high-frequency and low-frequency vibration components according to the spectrum, and analyze changes in amplitude;

[0070] Step 353: Define vibration compensation function , construct a compensation model;

[0071] Vibration compensation function:

[0072]

[0073] in:

[0074] and Represent the amplitudes of high-frequency and low-frequency vibrations respectively;

[0075] and are the frequencies of high-frequency and low-frequency vibrations, respectively;

[0076] and is the influence coefficient, which is used to adjust the influence of high-frequency and low-frequency vibrations on the compensation amount;

[0077] and is the adjustment parameter that controls the response of the compensation function to frequency;

[0078] Step 354: Substitute the collected frequency and amplitude data into the compensation function In the process, the real-time compensation amount is calculated and used to adjust the feature points collected by the visual sensor to reduce the positioning error;

[0079] Step 355: Apply the calculated compensation amount to the visual positioning system in real time to correct the position of the feature point. According to the correction result, the feedback system automatically adjusts the compensation model parameters to ensure adaptation to different vibration conditions.

[0080] The positioning method using the outdoor visual positioning system of the above-mentioned power transmission line intelligent fault elimination robot includes the following steps:

[0081] The environmental data around the transmission line is collected through various sensors in the environmental perception module, including light intensity, wind speed, temperature and humidity, and line vibration data;

[0082] Start the visual sensor to obtain the visual image sequence, and start the inertial sensor synchronously to ensure the synchronization of the timestamps of the visual data and the inertial data;

[0083] According to the lighting data collected by the environment perception module, the lighting compensation module is triggered to dynamically adjust the camera parameters to adapt to the current lighting conditions to ensure stable image quality;

[0084] Extract visual feature points in the current image frame, track the displacement of feature points in adjacent frames, and record the displacement information of feature points for preliminary visual positioning;

[0085] Integrate the acceleration and angular velocity data of the inertial sensor, calculate the change in the robot's posture at each moment, and infer the robot's displacement and angle change;

[0086] Through the visual inertial fusion module, the visual data is fused with the inertial sensor data, and the Kalman filter or extended Kalman filter is used to filter the data to eliminate noise and errors, and update the robot's position information in real time;

[0087] Vibration sensors are used to monitor the vibration frequency and amplitude of power transmission lines, and spectrum analysis is performed on vibration data to extract high-frequency vibration components that affect visual positioning.

[0088] According to the compensation model based on frequency and amplitude constructed by the vibration compensation module, the vibration compensation amount is calculated, the visual positioning deviation caused by line vibration is corrected, and the position of feature points in the visual data is corrected;

[0089] Use a multi-sensor fusion module to fuse environmental data, visual data, inertial data, and vibration compensation data to obtain more accurate robot position information;

[0090] Based on the fused positioning data and the geographical location information of the transmission line, the robot's moving path on the transmission line is planned through the path planning and navigation module to ensure that the robot can move stably and accurately.

[0091] Through the feedback control module, the robot positioning accuracy is monitored in real time, the robot's motion parameters are adjusted when necessary, and the compensation algorithm and sensor parameters are dynamically adjusted according to environmental changes;

[0092] Through the remote monitoring module, ground operators can view the robot's positioning information and operating status in real time and make remote adjustments; at the same time, the fault detection module is used to monitor system abnormalities and issue timely alarms.

[0093] Beneficial effects of the present invention:

[0094] 1. The present invention uses an environmental perception module, an illumination compensation module, a visual-inertial fusion module, and a vibration compensation module in a comprehensive manner, so that the system can effectively handle the interference caused by illumination changes, instability of visual data, and line vibration. In addition, the illumination compensation module dynamically adjusts the camera parameters to adapt to different illumination conditions, while the visual-inertial fusion module and the vibration compensation module can reduce the positioning error caused by vibration and movement. These measures ensure the positioning accuracy and stability of the robot in a complex environment, thereby improving the accuracy and efficiency of the defect elimination task.

[0095] 2. The adaptive visual algorithm module in the system of the present invention can automatically adjust the parameter settings of the visual system, such as exposure time, contrast, etc., according to changes in the external environment to adapt to different visual environments and ensure the effectiveness and accuracy of visual information. This adaptive ability greatly enhances the adaptability of the robot to complex and changeable outdoor environments. At the same time, the multi-sensor fusion module integrates data from different sensors, which not only improves the reliability and redundancy of the data, but also enables the system to perceive and understand the surrounding environment more comprehensively, providing more accurate and rich information support for subsequent path planning and navigation. The path planning and navigation module is based on these multi-source fusion data, combined with intelligent algorithms, to achieve accurate path planning and navigation, making the robot's operation on the transmission line more efficient and safe. These features jointly improve the intelligence level of the system and provide strong technical support for the intelligent inspection and maintenance of transmission lines. BRIEF DESCRIPTION OF THE DRAWINGS

[0096] 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:

[0097] Figure 1 This is a structural block diagram of an outdoor visual positioning system of an intelligent power transmission line fault elimination robot according to the present invention. DETAILED DESCRIPTION

[0098] 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.

[0099] 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.

[0100] 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.

[0101] Secondly, the present invention is described in detail with reference to the schematic diagram. When describing the embodiments of the present invention in detail, for the sake of convenience, the cross-sectional diagrams showing the device structure will not be partially enlarged according to the general scale, and the schematic diagrams are only examples, which should not limit the scope of protection of the present invention. In addition, in actual production, the three-dimensional dimensions of length, width and depth should be included.

[0102] Reference Figure 1 , which is an embodiment of the present invention, provides an outdoor visual positioning system for an intelligent fault elimination robot for power transmission lines, mainly comprising:

[0103] Environmental perception module,The environmental perception module is used to collect surrounding environment data through sensors, including light intensity, wind speed, temperature, humidity, and vibration frequency of power transmission lines. This module provides environmental information input for parameter adjustment of the visual system and optimization of the overall positioning system.

[0104] Sensors include:

[0105] Light sensor, used to monitor light intensity and changes in real time;

[0106] Wind speed sensor, used to collect wind speed information in the environment where the transmission line is located;

[0107] Temperature and humidity sensors are used to collect temperature and humidity information of the environment where the transmission line is located;

[0108] Vibration sensors are used to detect the vibration frequency and amplitude of power transmission lines and determine line changes caused by wind or mechanical movement.

[0109] The illumination compensation module dynamically adjusts the camera parameters according to the illumination data provided by the environment perception module, including exposure, gain, contrast, etc., to ensure that the clarity and stability of the visual system can be maintained under drastic changes in illumination (such as strong light, shadow, and night). This module works in conjunction with the environment perception module to adjust the image acquisition quality in real time.

[0110] Visual-inertial fusion module: The visual-inertial fusion module is used to fuse the data of visual sensors and inertial sensors, and adopts visual-inertial odometer technology to compensate for the instability of visual data during movement or vibration of the robot; especially in the case of large vibration, position correction is performed through inertial data.

[0111] The vibration compensation module is used to analyze the vibration frequency and amplitude of the power transmission line monitored by the environmental perception module and correct the visual positioning deviation caused by line vibration. This module can effectively improve the positioning accuracy of the robot in windy weather or when the line is in mechanical motion.

[0112] Adaptive visual algorithm module, the adaptive visual algorithm module can dynamically adjust the parameter settings of the visual system according to environmental changes (such as wind speed, temperature, humidity, etc.). This module obtains real-time environmental data through the environmental perception module and adjusts the algorithm parameters to enhance the system's anti-interference ability, ensuring that it can still maintain a high visual positioning accuracy in bad weather.

[0113] Multi-sensor fusion module, the multi-sensor fusion module adopts multi-sensor fusion algorithm to fuse sensor data and improve the overall positioning accuracy of the robot. Especially when a single sensor fails or the data is unreliable, other sensors can provide effective compensation.

[0114] Path planning and navigation module: The path planning and navigation module analyzes the current position information of the robot on the transmission line and the surrounding environment information based on the data of multiple sensors to perform accurate path planning. This module ensures that the robot can avoid obstacles and move along the planned path based on high-precision positioning.

[0115] It also includes a feedback control module, a remote monitoring module and a fault detection module;

[0116] The feedback control module adjusts the robot's motion parameters, including speed, posture and position control, in real time based on the feedback data from each module, ensuring that the robot can move and operate stably and accurately in complex environments;

[0117] The remote monitoring module can view the robot's operating status and positioning information on the transmission line in real time through the remote monitoring system, and cooperate with AR technology to help ground operators remotely adjust the robot;

[0118] The fault detection module is used to perform fault detection and report sensor abnormalities or data failures in a timely manner.

[0119] The above modules work together to enhance the outdoor visual positioning accuracy and stability of the transmission line intelligent fault elimination robot.

[0120] Specifically, dynamically adjusting camera parameters includes the following steps:

[0121] S11: The system sets a threshold for light changes. When the light intensity changes beyond the set threshold, the dynamic adjustment process is triggered;

[0122] S12: Classify the current lighting conditions according to the monitored lighting data, such as strong light, weak light, backlight or shadow environment, evaluate the type and magnitude of lighting changes through a preset algorithm, and determine whether parameter adjustment is required;

[0123] S13: Calculate the required compensation amount according to the deviation between the current illumination data and the normal working illumination range, and then convert the compensation amount into adjustment values ​​of exposure time, gain and contrast according to the mapping formula; for example, how much exposure needs to be increased or how much gain needs to be reduced;

[0124] The mapping formula is as follows:

[0125]

[0126]

[0127]

[0128] in:

[0129]

[0130]

[0131]

[0132] in:

[0133] is the exposure time adjustment amount, The weight coefficient for adjusting the exposure time controls the sensitivity of exposure to light compensation. is a nonlinear mapping function, which converts the illumination compensation Mapped to the appropriate exposure time adjustment value, It is the adjustment sensitivity coefficient used to control the effect of the compensation amount on the exposure adjustment;

[0134] is the gain adjustment amount, is the weight coefficient for gain adjustment, is a nonlinear mapping function that converts the illumination compensation amount into the gain adjustment value. Control the amplitude of gain change, so that the gain increases when the light is insufficient and decreases when the light is too strong;

[0135] is the contrast adjustment amount, is the weight coefficient for contrast adjustment, As a function, the illumination compensation amount is converted into a contrast adjustment value. Control the frequency and amplitude of contrast adjustment to ensure that the contrast fluctuates within an appropriate range;

[0136] Combining all adjustments, the final exposure time, gain, and contrast values ​​can be expressed as:

[0137] , ,

[0138] in, , , They are the initial exposure time, gain and contrast setting values ​​of the camera respectively. The adjustment values ​​of the three parameters are obtained by calculating the illumination compensation value to ensure the adaptability of the camera in different illumination environments.

[0139] Weight , , It can be set according to the priority of the actual scene. Generally speaking, exposure time is adjusted first in low-light environments, while gain and contrast adjustments should be fine-tuned based on exposure adjustment. Weight adjustment can be changed in real time according to environmental conditions to ensure the accuracy of overall compensation.

[0140] S14: calling the control interface of the camera to adjust the exposure time, gain and contrast according to the calculated compensation value;

[0141] S15: The camera feeds back the adjusted image data to the system. The visual system uses the image analysis algorithm to check the brightness, clarity and noise level of the image to determine whether the adjustment effect reaches the expected effect. If the image feedback is not ideal, the system will continue to make fine adjustments and repeatedly adjust the exposure, gain and contrast parameters until the image quality reaches the best effect.

[0142] S16: When the lighting conditions stabilize, the system locks the current parameter settings to avoid unnecessary adjustments and ensure that the vision system continues to provide high-quality images during the lighting stability stage.

[0143] Furthermore, the formula for calculating the required compensation is as follows:

[0144]

[0145] in:

[0146] Indicates at time The amount of light compensation;

[0147] Indicates the initial time;

[0148] Indicates time;

[0149] The ideal image brightness expected by the camera is set as the standard brightness required by the system;

[0150] Indicates the current time The actual image brightness captured by the lower camera;

[0151] Indicates the time The ambient light intensity is collected in real time through the light sensor;

[0152] Indicates the current time The ambient noise level, measured by an ambient noise detector;

[0153] Indicates the current time The camera gain, a parameter that can be dynamically adjusted by the system;

[0154] Indicates the current time The camera’s exposure time, a parameter that the system can dynamically adjust;

[0155] Indicates the current time The contrast of a camera image is a measure of the difference in image brightness;

[0156] is an exponential decay function, which is used to control the effect of the gain and contrast adjustment at past moments on the current compensation amount. is the attenuation coefficient, usually a positive value, which determines the degree of influence of past adjustments on the current situation. The larger the value, the smaller the influence of past adjustments. Subscripts used to represent summation terms, usually used for discrete adjustment calculations in the time dimension;

[0157] Represents the square of the difference between ideal brightness and actual brightness. The square amplifies the difference, ensuring that the system can respond to even small deviations in brightness.

[0158] Represents the superposition effect of ambient light intensity and noise, light intensity and noise These are two key factors that affect image quality. The larger the sum of the two, the stronger the environmental impact, and the less compensation is needed for brightness differences;

[0159] Represents the exponential function With gain and contrast The product of reflects their joint influence in the time dimension, and the exponential decay controls the influence of past time on the current compensation. and Determines how much gain and contrast adjust the image;

[0160] Indicates exposure time and ambient light intensity The square root sum of ensures the balance between exposure time and lighting conditions in the compensation calculation to avoid over-adjustment;

[0161] Represents the cumulative adjustment in the time dimension, through an exponential decay function To ensure that the influence of past gain and contrast on the current gradually decreases, the sum expresses the time accumulation effect in the camera parameter adjustment, ensuring that the system can dynamically respond to the environment over time;

[0162] Indicates that from the initial moment To the current time The cumulative compensation amount is calculated by integrating the cumulative effect of brightness difference over time, so that the system can track the changes in lighting and make dynamic adjustments;

[0163] Value range meaning:

[0164] The value range of the formula exist Inside;

[0165] when When it is close to 0, it means that the lighting conditions are ideal and the image quality is close to the expectation, so no major adjustment is needed;

[0166] when When the value increases, it means that the lighting conditions deviate from the expected ones and the system needs to increase the exposure or gain, or make contrast adjustments.

[0167] Specifically, compensating for the instability of visual data during movement or vibration of the robot includes the following steps:

[0168] Step 21: Initialize the visual sensor and inertial sensor and ensure that their data timestamps are synchronized. Then, obtain the visual frame sequence data through the visual sensor and obtain the acceleration and angular velocity through the inertial sensor.

[0169] Step 22: Using the image data in the visual sensor, a feature extraction algorithm (such as SIFT, ORB, etc.) is used to detect and extract feature points in the current image frame, track the matching relationship of feature points between adjacent frames, and record the displacement information of the feature points;

[0170] Step 23: Use the inertial sensor to collect acceleration and angular velocity data, integrate the collected acceleration and angular velocity data, and calculate the position and posture change of the robot at each moment;

[0171] Step 24: Fuse the inertial data and visual data through the Kalman filter or the extended Kalman filter to filter out noise and errors, and update the current position information of the robot according to the fusion result;

[0172] Step 25: Perform visual inertial odometry calculation for each continuous frame. According to the image data provided by the visual sensor and the motion data of the inertial sensor, calculate the posture change of the robot at continuous moments, and add the posture result of each frame with the result of the previous frame to obtain the complete movement trajectory of the robot.

[0173] Step 26: Analyze the high-frequency vibration information in the inertial sensor, identify the motion characteristics of the robot at the moment of vibration, dynamically correct the visual positioning data according to the vibration characteristics, and smooth the noise caused by the high-frequency vibration;

[0174] Step 27: Based on the corrected visual and inertial data, the robot's position information on the transmission line is updated in real time, and the current position information is fed back to the path planning and navigation module to make the next movement decision;

[0175] Step 28: Monitor the positioning accuracy in real time, provide feedback to the control system, and adjust the compensation algorithm and sensor parameters according to environmental changes.

[0176] Through the above steps, the system can effectively integrate visual and inertial data, and use visual inertial odometry (VIO) technology to accurately calculate the robot's position information on the transmission line. Especially when the robot is subjected to external vibrations or movement, it can compensate for the errors in visual data in real time, thereby maintaining positioning accuracy.

[0177] Specifically, correcting the visual positioning deviation caused by line vibration includes the following steps:

[0178] Step 31: Install vibration sensors (such as accelerometers, gyroscopes, etc.) on the transmission line, monitor the vibration frequency and amplitude of the transmission line in real time through the vibration sensors, obtain real-time data of line vibration through the environmental sensing module, analyze the vibration frequency and amplitude in each time period, and determine the characteristics of line vibration;

[0179] Step 32: Obtain the robot's inertial data through the robot's own inertial measurement unit to ensure time synchronization with the vibration data, and synchronize the inertial data with the vibration sensor data to facilitate subsequent fusion calculations;

[0180] Step 33: Perform spectrum analysis on the vibration frequency and amplitude of the line to determine the high-frequency vibration components, and identify the main vibration components that affect the visual positioning accuracy based on the analysis results;

[0181] Step 34: Acquire real-time visual data from the robot camera, including image frames of environmental feature points, extract and track the feature points in the image, detect the offset of feature points between adjacent frames, compare the data of the visual sensor and the inertial sensor, and calculate the visual positioning error;

[0182] Step 35: According to the vibration frequency, amplitude and visual positioning error, a vibration compensation model is established to calculate the compensation amount, and the sensor data is corrected using Kalman filtering and other technologies to filter out high-frequency interference;

[0183] Step 36: According to the correction amount output by the vibration compensation model, adjust the position of the visual feature points in real time, correct the visual data deviation, update the corrected position information, and ensure the visual positioning stability of the robot.

[0184] Through the above steps, the system can effectively analyze the vibration characteristics of the transmission line and correct the visual positioning deviation caused by it. The vibration compensation module combines multi-sensor data and realizes dynamic correction of the instability of visual data through compensation models and filtering algorithms, ensuring the precise positioning of the robot in complex environments.

[0185] Furthermore, a vibration compensation model based on frequency and amplitude is established, and the calculation of the compensation amount includes the following steps:

[0186] Step 351: pre-processing the data collected by the vibration sensor installed on the transmission line using a filtering algorithm, and extracting key information of frequency and amplitude from the filtered data;

[0187] Step 352: Perform Fourier transform on the collected vibration data to obtain a spectrum of vibration frequency, classify high-frequency and low-frequency vibration components according to the spectrum, and analyze changes in amplitude;

[0188] Step 353: Define vibration compensation function , construct a compensation model;

[0189] Vibration compensation function:

[0190]

[0191] in:

[0192] and Represent the amplitudes of high-frequency and low-frequency vibrations respectively;

[0193] and are the frequencies of high-frequency and low-frequency vibrations, respectively;

[0194] and is the influence coefficient, which is used to adjust the influence of high-frequency and low-frequency vibrations on the compensation amount;

[0195] and is the adjustment parameter that controls the response of the compensation function to frequency;

[0196] Step 354: Substitute the collected frequency and amplitude data into the compensation function In the process, the real-time compensation amount is calculated and used to adjust the feature points collected by the visual sensor to reduce the positioning error;

[0197] Step 355: Apply the calculated compensation amount to the visual positioning system in real time to correct the position of the feature point. According to the correction result, the feedback system automatically adjusts the compensation model parameters to ensure adaptation to different vibration conditions.

[0198] Through the above steps, the vibration compensation model can dynamically calculate and apply the compensation amount based on the frequency and amplitude information. This can effectively reduce the visual positioning deviation caused by the vibration of the transmission line and ensure the stable positioning and operation of the robot.

[0199] In summary, the present invention uses the environment perception module, the illumination compensation module, the visual inertial fusion module and the vibration compensation module in a comprehensive manner, so that the system can effectively handle the interference caused by illumination changes, the instability of visual data and line vibration. In addition, the illumination compensation module dynamically adjusts the camera parameters to adapt to different illumination conditions, and the visual inertial fusion module and the vibration compensation module can reduce the positioning error caused by vibration and movement. These measures ensure the positioning accuracy and stability of the robot in a complex environment, thereby improving the accuracy and efficiency of the defect elimination task. The adaptive visual algorithm module in the system of the present invention can automatically adjust the parameter settings of the visual system, such as exposure time, contrast, etc., according to changes in the external environment to adapt to different visual environments and ensure the effectiveness and accuracy of visual information. This adaptive ability greatly enhances the adaptability of the robot to complex and changeable outdoor environments. At the same time, the multi-sensor fusion module integrates data from different sensors, which not only improves the reliability and redundancy of the data, but also enables the system to perceive and understand the surrounding environment more comprehensively, providing more accurate and rich information support for subsequent path planning and navigation. The path planning and navigation module is based on these multi-source fusion data and combines intelligent algorithms to achieve accurate path planning and navigation, making the robot's operation on the transmission line more efficient and safe. These features jointly improve the intelligence level of the system and provide strong technical support for the intelligent inspection and maintenance of transmission lines.

[0200] The positioning method using the outdoor visual positioning system of the above-mentioned power transmission line intelligent fault elimination robot includes the following steps:

[0201] The environmental data around the transmission line is collected through various sensors in the environmental perception module, including light intensity, wind speed, temperature and humidity, and line vibration data;

[0202] Start the visual sensor to obtain the visual image sequence, and start the inertial sensor synchronously to ensure the synchronization of the timestamps of the visual data and the inertial data;

[0203] According to the lighting data collected by the environment perception module, the lighting compensation module is triggered to dynamically adjust the camera parameters to adapt to the current lighting conditions to ensure stable image quality;

[0204] Extract visual feature points in the current image frame, track the displacement of feature points in adjacent frames, and record the displacement information of feature points for preliminary visual positioning;

[0205] Integrate the acceleration and angular velocity data of the inertial sensor, calculate the change in the robot's posture at each moment, and infer the robot's displacement and angle change;

[0206] Through the visual inertial fusion module, the visual data is fused with the inertial sensor data, and the Kalman filter or extended Kalman filter is used to filter the data to eliminate noise and errors, and update the robot's position information in real time;

[0207] Vibration sensors are used to monitor the vibration frequency and amplitude of power transmission lines, and spectrum analysis is performed on vibration data to extract high-frequency vibration components that affect visual positioning.

[0208] According to the compensation model based on frequency and amplitude constructed by the vibration compensation module, the vibration compensation amount is calculated, the visual positioning deviation caused by line vibration is corrected, and the position of feature points in the visual data is corrected;

[0209] Use a multi-sensor fusion module to fuse environmental data, visual data, inertial data, and vibration compensation data to obtain more accurate robot position information;

[0210] Based on the fused positioning data and the geographical location information of the transmission line, the robot's moving path on the transmission line is planned through the path planning and navigation module to ensure that the robot can move stably and accurately.

[0211] Through the feedback control module, the robot positioning accuracy is monitored in real time, the robot's motion parameters are adjusted when necessary, and the compensation algorithm and sensor parameters are dynamically adjusted according to environmental changes;

[0212] Through the remote monitoring module, ground operators can view the robot's positioning information and operating status in real time and make remote adjustments; at the same time, the fault detection module is used to monitor system abnormalities and issue timely alarms.

[0213] 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. An outdoor visual positioning system for an intelligent fault elimination robot for power transmission lines, characterized in that: include: An environment perception module, which is used to collect surrounding environment data through sensors; An illumination compensation module, which dynamically adjusts camera parameters according to illumination data provided by the environment perception module; The dynamic adjustment of camera parameters comprises the following steps: S11: The system sets a threshold for light changes. When the light intensity changes beyond the set threshold, the dynamic adjustment process is triggered; S12: classify the current lighting conditions according to the monitored lighting data, evaluate the type and magnitude of lighting changes through a preset algorithm, and determine whether parameter adjustment is required; S13: Calculate the required compensation amount according to the deviation between the current illumination data and the normal working illumination range, and then convert the compensation amount into adjustment values ​​of exposure time, gain and contrast according to a mapping formula; S14: calling the control interface of the camera to adjust the exposure time, gain and contrast according to the calculated compensation value; S15: The camera feeds back the adjusted image data to the system. The visual system uses the image analysis algorithm to check the brightness, clarity and noise level of the image to determine whether the adjustment effect reaches the expected effect. If the image feedback is not ideal, the system will continue to make fine adjustments and repeatedly adjust the exposure, gain and contrast parameters until the image quality reaches the best effect. S16: When the lighting conditions are stable, the system locks the current parameter settings to avoid unnecessary adjustments and ensure that the vision system continues to provide high-quality images during the lighting stability stage; A visual-inertial fusion module, which is used to fuse the data of the visual sensor and the inertial sensor, and uses the visual-inertial odometer technology to compensate for the instability of the visual data of the robot during movement or vibration; The method of compensating for the instability of visual data during movement or vibration of the robot comprises the following steps: Step 21: Initialize the visual sensor and inertial sensor and ensure that their data timestamps are synchronized. Then, obtain the visual frame sequence data through the visual sensor and obtain the acceleration and angular velocity through the inertial sensor. Step 22: Detect and extract feature points in the current image frame, track the matching relationship of feature points between adjacent frames, and record the displacement information of the feature points; Step 23: Integrate the collected acceleration and angular velocity data to calculate the position and posture change of the robot at each moment; Step 24: Fuse the inertial data and visual data through the Kalman filter or the extended Kalman filter to filter out noise and errors, and update the current position information of the robot according to the fusion result; Step 25: Perform visual inertial odometry calculation for each continuous frame. According to the image data provided by the visual sensor and the motion data of the inertial sensor, calculate the posture change of the robot at continuous moments, and add the posture result of each frame with the result of the previous frame to obtain the complete movement trajectory of the robot. Step 26: Analyze the high-frequency vibration information in the inertial sensor, identify the motion characteristics of the robot at the moment of vibration, dynamically correct the visual positioning data according to the vibration characteristics, and smooth the noise caused by the high-frequency vibration; Step 27: Based on the corrected visual and inertial data, the robot's position information on the transmission line is updated in real time, and the current position information is fed back to the path planning and navigation module to make the next movement decision; Step 28: Monitor the positioning accuracy in real time, provide feedback to the control system, and adjust the compensation algorithm and sensor parameters according to environmental changes; A vibration compensation module, which is used to analyze the vibration frequency and amplitude of the power transmission line monitored by the environment perception module and correct the visual positioning deviation caused by line vibration; The correction of visual positioning deviation caused by line vibration comprises the following steps: Step 31: The sensor monitors the vibration frequency and amplitude of the transmission line in real time, and analyzes the vibration frequency and amplitude in each time period to determine the characteristics of the line vibration; Step 32: Obtain the robot's inertial data, ensure time synchronization with the vibration data, and synchronize the inertial data with the vibration sensor data to facilitate subsequent fusion calculations; Step 33: Perform spectrum analysis on the vibration data to determine the high-frequency vibration components, and identify the main vibration components that affect the visual positioning accuracy based on the analysis results; Step 34: Extract feature points in the image and track them, detect feature point offsets between adjacent frames, compare data from the visual sensor and the inertial sensor, and calculate visual positioning errors; Step 35: Establish a vibration compensation model based on frequency and amplitude, calculate the compensation amount, correct the sensor data, and filter out high-frequency interference; Step 36: According to the vibration compensation model, adjust the position of the visual feature points in real time, correct the visual data deviation, update the corrected position information, and ensure the visual positioning stability of the robot; A multi-sensor fusion module, wherein the multi-sensor fusion module adopts a multi-sensor fusion algorithm to fuse sensor data; The path planning and navigation module performs accurate path planning by analyzing the current position information of the robot on the transmission line and the surrounding environment information based on the data of multiple sensors.

2. The outdoor visual positioning system of the intelligent fault elimination robot for power transmission lines according to claim 1, characterized in that: The sensors include a light sensor, a wind speed sensor, a temperature and humidity sensor, and a vibration sensor. The light sensor is used to monitor the light intensity and changes in real time. The wind speed sensor is used to collect wind speed information of the environment in which the transmission line is located. The temperature and humidity sensor is used to collect temperature and humidity information of the environment in which the transmission line is located. The vibration sensor is used to detect the vibration frequency and amplitude of the transmission line and determine the line changes caused by wind or mechanical movement.

3. The outdoor visual positioning system of the intelligent fault elimination robot for power transmission lines according to claim 1, characterized in that: The camera parameters include exposure, gain, and contrast.

4. The outdoor visual positioning system of the intelligent fault elimination robot for power transmission lines according to claim 1, characterized in that: It also includes a feedback control module, a remote monitoring module and a fault detection module; The feedback control module adjusts the motion parameters of the robot in real time according to the feedback data of each module; The remote monitoring module can view the robot's operating status and positioning information on the transmission line in real time through the remote monitoring system, and cooperate with AR technology to help ground operators remotely adjust the robot; The fault detection module is used to perform fault detection and report sensor abnormalities or data failures in a timely manner.

5. The outdoor visual positioning system of the intelligent fault elimination robot for power transmission lines according to claim 1, characterized in that: The formula for calculating the required compensation amount is as follows: in: ΔCompensation(t) represents the amount of illumination compensation at time t; t0 represents the initial time; t represents time; I ref The ideal image brightness expected by the camera is set as the standard brightness required by the system; I(t) represents the actual image brightness captured by the camera at the current time t; L(t) represents the ambient light intensity at time t, which is collected in real time by the light sensor; N(t) represents the ambient noise level at the current time t, measured by the ambient noise detector; G(t) represents the gain of the camera at the current time t, a parameter that can be dynamically adjusted by the system; E(t) represents the exposure time of the camera at the current time t, a parameter that can be dynamically adjusted by the system; C(t) represents the contrast of the camera image at the current time t, which is a measure of the image brightness difference; e -αn is an exponential decay function, which is used to control the influence of the gain and contrast adjustments at past moments on the current compensation amount. α is the decay coefficient, which is a positive value and determines the degree of influence of the past adjustments on the current amount. The larger the value, the smaller the influence of the past adjustments. n is used to represent the subscript of the summation term, which is used for discrete adjustment calculations in the time dimension. (I ref -I(t)) 2 Represents the square of the difference between ideal brightness and actual brightness. The square amplifies the difference, ensuring that the system can respond to even small deviations in brightness. L(t)+N(t) represents the superposition effect of ambient light intensity and noise. Light intensity L(t) and noise N(t) are two key factors affecting image quality. The larger the sum of the two, the stronger the environmental impact, and the less compensation is needed for brightness differences. e -αn G(t)C(t) represents the exponential function e -αn The product of gain G(t) and contrast C(t) reflects their joint influence in the time dimension. The exponential decay controls the influence of past time on the current compensation. G(t) and C(t) determine the adjustment strength of gain and contrast on the image. It represents the square root sum of exposure time E(t) and ambient light intensity L(t), ensuring the balance between exposure time and lighting conditions in compensation calculation to avoid over-adjustment; Represents the cumulative adjustment in the time dimension, through the exponential decay function e -αn To ensure that the influence of past gain and contrast on the current gradually decreases, the sum expresses the time accumulation effect in the camera parameter adjustment, ensuring that the system can dynamically respond to the environment over time; It represents the cumulative compensation amount from the initial time t0 to the current time t. The integral calculates the cumulative effect of the brightness difference over time, so that the system can track the changes in illumination and make dynamic adjustments; Value range meaning: The range of the formula ΔCompensation(t) is in [0,∞); When ΔCompensation(t) is close to 0, it means that the lighting conditions are ideal and the image quality is close to expectations, so no major adjustments are needed. When the ΔCompensation(t) value increases, it means that the lighting conditions deviate from expectations and the system needs to increase exposure or gain, or perform contrast adjustment.

6. The outdoor visual positioning system of the intelligent fault elimination robot for power transmission lines according to claim 1, characterized in that: The establishment of the vibration compensation model based on frequency and amplitude and the calculation of the compensation amount include the following steps: Step 351: pre-processing the data collected by the vibration sensor installed on the transmission line using a filtering algorithm, and extracting key information of frequency and amplitude from the filtered data; Step 352: Perform Fourier transform on the collected vibration data to obtain a spectrum of vibration frequency, classify high-frequency and low-frequency vibration components according to the spectrum, and analyze changes in amplitude; Step 353: define a vibration compensation function C(f,A) and construct a compensation model; Vibration compensation function: in: A high and A low Represent the amplitudes of high-frequency and low-frequency vibrations respectively; f high and f low are the frequencies of high-frequency and low-frequency vibrations, respectively; α1 and α2 are influence coefficients, which are used to adjust the influence of high-frequency and low-frequency vibrations on the compensation amount; β1 and β2 are adjustment parameters that control the response of the compensation function to frequency; Step 354: The collected frequency and amplitude data are brought into the compensation function C(f, A), and the real-time compensation amount is calculated. The compensation amount is used to adjust the feature points collected by the visual sensor to reduce the positioning error. Step 355: Apply the calculated compensation amount to the visual positioning system in real time to correct the position of the feature point. According to the correction result, the feedback system automatically adjusts the compensation model parameters to ensure adaptation to different vibration conditions.

7. The positioning method of the outdoor visual positioning system of the power transmission line intelligent fault elimination robot according to any one of claims 1 to 6, characterized in that: The following steps are involved: The environmental data around the transmission line is collected through various sensors in the environmental perception module, including light intensity, wind speed, temperature and humidity, and line vibration data; Start the visual sensor to obtain the visual image sequence, and start the inertial sensor synchronously to ensure the synchronization of the timestamps of the visual data and the inertial data; According to the lighting data collected by the environment perception module, the lighting compensation module is triggered to dynamically adjust the camera parameters to adapt to the current lighting conditions to ensure stable image quality; Extract visual feature points in the current image frame, track the displacement of feature points in adjacent frames, and record the displacement information of feature points for preliminary visual positioning; Integrate the acceleration and angular velocity data of the inertial sensor, calculate the change in the robot's posture at each moment, and infer the robot's displacement and angle change; Through the visual inertial fusion module, the visual data is fused with the inertial sensor data, and the Kalman filter or extended Kalman filter is used to filter the data to eliminate noise and errors, and update the robot's position information in real time; Vibration sensors are used to monitor the vibration frequency and amplitude of power transmission lines, and spectrum analysis is performed on vibration data to extract high-frequency vibration components that affect visual positioning. According to the compensation model based on frequency and amplitude constructed by the vibration compensation module, the vibration compensation amount is calculated, the visual positioning deviation caused by line vibration is corrected, and the position of feature points in the visual data is corrected; Use a multi-sensor fusion module to fuse environmental data, visual data, inertial data, and vibration compensation data to obtain more accurate robot position information; Based on the fused positioning data and the geographical location information of the transmission line, the robot's moving path on the transmission line is planned through the path planning and navigation module to ensure that the robot can move stably and accurately. Through the feedback control module, the robot positioning accuracy is monitored in real time, the robot's motion parameters are adjusted when necessary, and the compensation algorithm and sensor parameters are dynamically adjusted according to environmental changes; Through the remote monitoring module, ground operators can view the robot's positioning information and operating status in real time and make remote adjustments; at the same time, the fault detection module is used to monitor system abnormalities and issue timely alarms.

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