Walking optimization method and system for walking vehicle type cable coating robot

Abstract

The invention relates to the technical field of robot control, and particularly discloses a walking optimization method and system for a walking vehicle type cable coating robot, and the method comprises the steps: reconstructing the three-dimensional contour of the surface of a cable in real time, and extracting the radius of the cable and the roundness of the section of the cable in real time; spraying pressure record data, cable surface temperature field distribution and a normal component and tangential friction force of real-time wheel-cable contact force are collected; based on the parameters and the real-time walking speed of the robot, a coating thickness prediction value at the future moment is obtained; when the deviation between the coating thickness predicted value at the future moment and the corresponding target thickness is larger than a preset deviation threshold value, the walking speed adjusting amount and / or the robot driving wheel positive pressure compensation amount are / is calculated; performing walking optimization control on the robot based on the walking speed adjusting quantity and the robot driving wheel positive pressure compensation quantity to obtain a walking optimization control result; therefore, the coating thickness of the cable is closer to a target value, and the coating quality and the walking adaptability of the robot are improved.

Description

Technical Field

[0001] The present invention relates to the field of robot control technology, and in particular to a walking optimization method and system for a vehicle-type cable coating robot. Background Art

[0002] In the field of cable manufacturing and maintenance, cable coating processes play a key role in improving cable performance and service life. Carriage-type cable coating robots provide an automated and efficient solution for cable coating, ensuring coating quality while significantly improving production efficiency. With the increasing demands for cable quality in industrial production, this sector has imposed more stringent standards for cable coating uniformity and thickness consistency. With the rapid development of intelligent manufacturing technology, industrial production is placing increasing demands on the intelligence of automated equipment. The walking optimization technology for car-type cable coating robots leverages advanced sensor technology and intelligent algorithms to sense various parameters during the working process in real time and perform precise calculations and control. This intelligent walking optimization method and system not only has broad application prospects in the cable manufacturing industry but also provides a reference for other fields involving surface coating processes, driving the entire manufacturing industry towards intelligent and refined processes.

[0003] However, existing vehicle-type cable coating robot technology has incomplete information about the coating process, which leads to inaccurate prediction of the coating effect. It is also impossible to accurately calculate the walking speed adjustment amount and / or the robot drive wheel positive pressure compensation amount and effectively optimize the robot's walking control to ensure the quality and thickness consistency of the cable coating.

[0004] Therefore, the present invention proposes a walking optimization method and system for a vehicle-type cable coating robot. Summary of the Invention

[0005] The present invention provides a walking optimization method and system for a mobile cable coating robot. The method obtains the real-time cable radius and cable cross-sectional roundness by reconstructing the three-dimensional profile of the cable surface in real time, and collects spray pressure record data, cable surface temperature field distribution, and real-time wheel-cable contact force related components. The method predicts the coating thickness at a future time based on the robot's real-time walking speed. When the deviation between the predicted value and the target thickness exceeds a preset threshold, the method calculates the walking speed adjustment amount and / or the robot's driving wheel positive pressure compensation amount. Finally, the robot's walking is optimized and controlled based on these adjustment amounts to ensure that the cable coating thickness is closer to the target value. By comprehensively considering these complex factors, the robot's walking can be precisely controlled, thereby improving the quality and stability of cable coating. The method improves the coating quality and the stability and adaptability of the robot's walking.

[0006] The present invention provides a walking optimization method for a road-type cable coating robot, comprising: Reconstruct the 3D profile of the cable surface in real time and extract the real-time cable radius and cable cross-sectional roundness. Simultaneously collect spray pressure record data, cable surface temperature field distribution, and the normal component of the real-time wheel-cable contact force and tangential friction force. Based on the real-time cable radius, cable cross-section roundness, spray pressure record data, cable surface temperature field distribution, the normal component of the real-time wheel-cable contact force and the tangential friction force, as well as the real-time walking speed of the robot, the coating thickness prediction value at the future moment is obtained; When the deviation between the coating thickness prediction value at the future moment and the corresponding target thickness is greater than a preset deviation threshold, the walking speed adjustment amount and / or the robot driving wheel positive pressure compensation amount are calculated; The robot's walking optimization control is performed based on the walking speed adjustment amount and the positive pressure compensation amount of the robot's driving wheels to obtain the walking optimization control result.

[0007] Preferably, real-time reconstruction of the three-dimensional profile of the cable surface and extraction of the real-time cable radius and cable cross-sectional roundness include: The three-dimensional profile of the cable surface is reconstructed based on multiple groups of laser profile sensors arranged around the robot's walking mechanism and a triangulation ranging method; Extract real-time cable radius and cable cross-section roundness based on the 3D profile of the cable surface.

[0008] Preferably, collecting spray pressure record data, cable surface temperature field distribution, and real-time normal component of wheel-cable contact force and tangential friction force includes: The high-frequency pressure sensor and infrared temperature sensor set at the outlet of the robot spray gun synchronously collect spray pressure recording data and cable surface temperature field distribution; The normal component of the wheel-cable contact force and the tangential friction force are collected in real time based on the six-axis force sensor integrated at the driving wheel of the robot.

[0009] Preferably, based on the real-time cable radius, cable cross-sectional roundness, spray pressure record data, cable surface temperature field distribution, the normal component and tangential friction force of the real-time wheel-cable contact force, and the real-time walking speed of the robot, a coating thickness prediction value at a future time is obtained, including: Perform trend prediction on the collected real-time cable radius, cable cross-sectional roundness, spray pressure record data, cable surface temperature field distribution, and the normal component of the real-time wheel-cable contact force and the tangential friction force, and obtain the predicted values ​​of the real-time cable radius, cable cross-sectional roundness, cable surface temperature field distribution, the normal component of the real-time wheel-cable contact force and the tangential friction force at future times, as well as the predicted values ​​of the spray pressure record data to future times; Determine the average spraying pressure in the latest historical period based on the future time and the instantaneous pressure fluctuation amplitude at the future time based on the predicted value of the spraying pressure record data to the future time; Calculate the base value of coating thickness at a future moment based on the average spraying pressure in the latest historical period based on the future moment, the instantaneous pressure fluctuation amplitude at a future moment, the predicted value of the real-time cable radius at a future moment, the predicted value of the linear spraying speed of the standard coating at a future moment, and the current walking speed of the robot; Determining a roundness deviation rate at a future time based on a predicted value of the cable cross-section roundness at a future time, and determining a first correction coefficient of a coating thickness base value based on the roundness deviation rate at the future time; Determining a local temperature gradient factor at a future time based on a predicted value of the cable surface temperature field distribution at a future time, and determining a second correction coefficient of the coating thickness base value based on the local temperature gradient factor at the future time; Determining a friction coefficient at a future time based on predicted values ​​of the normal component of the real-time wheel-cable contact force and the tangential friction force at a future time, and determining a third correction coefficient for the base value of the coating thickness based on the friction coefficient at the future time; The coating thickness prediction value at the future moment is calculated based on the coating thickness base value at the future moment and the corresponding first correction coefficient, second correction coefficient, and third correction coefficient.

[0010] Preferably, determining the roundness deviation rate at a future moment based on the predicted value of the cable cross-section roundness at a future moment includes: Based on the three-dimensional profile of the cable surface, the cable cross-section circumferential center distance distribution vector at the future moment is predicted, and the maximum center distance, minimum center distance, average center distance, center distance standard deviation and roundness shape factor of the cable cross-section at the future moment are extracted from the cable cross-section circumferential center distance distribution vector; Correcting the circumferential center distance distribution vector of the cable cross section at the future moment based on the maximum center distance, minimum center distance, average center distance, center distance standard deviation, and roundness shape factor of the cable cross section at the future moment to obtain a corrected circumferential center distance distribution vector of the cable cross section at the future moment; Calculate the roundness basic deviation rate at the future moment based on the cable cross-section circumferential center distance correction distribution vector at the future moment; Performing circumferential partitioning based on the cable cross section at the future moment to obtain a plurality of circumferential partitions, and calculating an average center distance deviation of each circumferential partition based on the circumferential center distance correction distribution vector of the cable cross section at the future moment; Based on the distribution positions of all circumferential partitions whose average center distance deviations are greater than the preset center distance deviation threshold and the corresponding over-limit values ​​of the average center distance deviations, the basic roundness deviation rate at future moments is corrected to obtain the roundness deviation rate at future moments.

[0011] Preferably, determining the local temperature gradient factor at a future time based on the predicted value of the cable surface temperature field distribution at a future time includes: A 3D cable model is established based on the 3D contour of the cable surface, and the 3D cable model is spatially divided to obtain a cable space grid; Based on the predicted value of the cable surface temperature field distribution at a future time, the normalized value of the temperature gradient amplitude, the cosine value of the angle between the temperature gradient direction angle and the spraying direction, and the exponential decay term of the gradient change rate of each cable space grid are calculated. The normalized value of the temperature gradient amplitude, the cosine value of the angle between the temperature gradient direction angle and the spraying direction, and the exponential decay term of the gradient change rate of each cable space grid are weightedly summed to obtain the local temperature gradient factor of each cable space grid; The local temperature gradient factor of all cable space grids in the coating section at the future moment is weighted averaged to obtain the local temperature gradient factor at the future moment.

[0012] Preferably, when the deviation between the coating thickness prediction value at a future moment and the corresponding target thickness is greater than a preset deviation threshold, the walking speed adjustment amount and / or the robot driving wheel positive pressure compensation amount are calculated, including: Determine the coating position of the robot at a future time based on the current walking speed and current coating position of the robot, and obtain the target thickness of the coating position of the robot at the future time; When the deviation between the coating thickness prediction value at the future moment and the target thickness at the coating position of the robot at the future moment is greater than the preset deviation threshold, the basic walking speed compensation amount is calculated based on the speed adjustment coefficient, the deviation between the coating thickness prediction value at the future moment and the target thickness at the coating position of the robot at the future moment, the target thickness, the current walking speed, the standard walking speed, the current cable radius, and the nominal cable radius, and the basic walking speed compensation amount is dynamically corrected based on the dynamic correction rule to obtain the optimal walking speed compensation amount; At the same time, the positive pressure basic compensation of the robot driving wheel is calculated based on the positive pressure adjustment coefficient, the deviation between the coating thickness prediction value at the future moment and the target thickness at the coating position of the robot at the future moment, the target thickness, the current wheel-cable friction coefficient, the optimal friction coefficient, the nominal positive pressure, the current cable radius and the nominal cable radius, and the positive pressure basic compensation of the robot driving wheel is corrected by safety constraints to obtain the optimal compensation of the robot driving wheel.

[0013] Preferably, dynamically correcting the basic walking speed compensation amount based on the dynamic correction rule to obtain the optimal walking speed compensation amount includes: If the instantaneous pressure fluctuation frequency of the paint is greater than the preset fluctuation frequency threshold, a first correction coefficient of the basic walking speed compensation is determined based on the instantaneous pressure fluctuation frequency of the paint and the correlation coefficient between the instantaneous pressure fluctuation frequency of the paint and the speed compensation; If the cable cross-sectional roundness deviation rate is greater than a preset deviation rate threshold, a second correction coefficient of the basic walking speed compensation amount is determined based on the cable cross-sectional roundness deviation rate and the cable cross-sectional roundness deviation rate-speed compensation amount correlation coefficient; The basic walking speed compensation amount is dynamically corrected based on the first correction coefficient and the second correction coefficient of the basic walking speed compensation amount to obtain the optimal walking speed compensation amount.

[0014] Preferably, performing safety constraint correction on the positive pressure base compensation of the robot driving wheel to obtain the optimal compensation of the robot driving wheel includes: If the front wheel-cable friction coefficient is greater than the preset friction coefficient threshold, the product of the normal pressure base compensation of the robot driving wheel and the preset constraint correction coefficient is used as the first correction compensation of the robot driving wheel; Performing advance compensation on the first correction compensation amount of the robot driving wheel to obtain a second correction compensation amount of the robot driving wheel; The second correction compensation amount of the robot driving wheel is corrected for the third time based on the nominal positive pressure and the preset safety constraint ratio to obtain the optimal compensation amount of the robot driving wheel.

[0015] The present invention provides a walking optimization system for a road-type cable coating robot, comprising: A multi-dimensional sensing module is used to reconstruct the 3D profile of the cable surface in real time and extract the real-time cable radius and cross-sectional roundness. It also collects spray pressure data, cable surface temperature distribution, and the normal component of the real-time wheel-cable contact force and tangential friction force. The thickness prediction module is used to obtain the coating thickness prediction value at a future time based on the real-time cable radius, cable cross-section roundness, spray pressure record data, cable surface temperature field distribution, the normal component and tangential friction force of the real-time wheel-cable contact force, and the real-time walking speed of the robot; a compensation calculation module, configured to calculate a walking speed adjustment amount and / or a robot driving wheel positive pressure compensation amount when a deviation between a coating thickness prediction value at a future moment and a corresponding target thickness is greater than a preset deviation threshold; The optimization control module is used to perform walking optimization control on the robot based on the walking speed adjustment amount and the positive pressure compensation amount of the robot driving wheel to obtain the walking optimization control result.

[0016] The beneficial effects of the present invention compared to the existing technology are as follows: by reconstructing the three-dimensional profile of the cable surface in real time to obtain the real-time cable radius and cable cross-sectional roundness, and collecting spray pressure record data, cable surface temperature field distribution and real-time wheel-cable contact force related components, the coating thickness at a future moment is predicted in combination with the real-time walking speed of the robot. When the deviation between the predicted value and the target thickness exceeds a preset threshold, the walking speed adjustment amount and / or the robot driving wheel positive pressure compensation amount are calculated. Finally, based on these adjustment amounts, the robot's walking is optimized and controlled to ensure that the cable coating thickness is closer to the target value. By comprehensively considering these complex factors, precise control of the robot's walking can be achieved, thereby improving the quality and stability of the cable coating. Improve the coating quality and the stability and adaptability of the robot's walking.

[0017] Other features and advantages of the present invention will be described in the following description, and in part will become apparent from the description, or will be understood by practicing the present invention. The purpose and other advantages of the present invention can be achieved and obtained through the structures specifically pointed out in this application document.

[0018] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings: Figure 1 This is a flow chart of a walking optimization method for a mobile cable coating robot in an embodiment of the present invention; Figure 2 Schematic diagram of a walking optimization system for a road-type cable coating robot in an embodiment of the present invention. DETAILED DESCRIPTION

[0020] The preferred embodiments of the present invention are described below with reference to the accompanying drawings. It should be understood that the preferred embodiments described herein are only used to illustrate and explain the present invention, and are not used to limit the present invention.

[0021] like Figure 1 As shown, the present invention provides an embodiment of a walking optimization method for a mobile cable coating robot, comprising: Reconstruct the 3D profile of the cable surface in real time and extract the real-time cable radius and cable cross-sectional roundness. Simultaneously collect spray pressure record data, cable surface temperature field distribution, and the normal component of the real-time wheel-cable contact force and tangential friction force. Based on the real-time cable radius, cable cross-section roundness, spray pressure record data, cable surface temperature field distribution, the normal component of the real-time wheel-cable contact force and the tangential friction force, as well as the real-time walking speed of the robot, the coating thickness prediction value at the future moment is obtained; When the deviation between the coating thickness prediction value at the future moment and the corresponding target thickness is greater than a preset deviation threshold, the walking speed adjustment amount and / or the robot driving wheel positive pressure compensation amount are calculated; The robot's walking optimization control is performed based on the walking speed adjustment amount and the positive pressure compensation amount of the robot's driving wheels to obtain the walking optimization control result.

[0022] 3D profile of the cable surface: The shape of the cable surface in 3D space is reconstructed using laser profile sensors arranged circumferentially on the robot's walking mechanism and triangulation.

[0023] In this embodiment, the real-time cable radius refers to cable radius data extracted from the real-time reconstructed three-dimensional profile of the cable surface.

[0024] In this embodiment, the cable cross-section roundness is calculated based on the three-dimensional profile data of the cable surface and is used to measure the degree to which the cable cross-section is close to a circle.

[0025] In this embodiment, the spraying pressure recording data is the paint spraying pressure data collected by the high-frequency pressure sensor at the outlet of the robot spray gun.

[0026] In this embodiment, the cable surface temperature field distribution is the cable surface temperature distribution data collected by an infrared temperature sensor.

[0027] In this embodiment, the normal component of the real-time wheel-cable contact force and the tangential friction force are collected by the six-axis force sensor at the driving wheel of the robot, and refer to the forces in the vertical and tangential directions when the wheel contacts the cable, respectively.

[0028] In this embodiment, the real-time walking speed of the robot refers to the instantaneous walking speed of the robot during the cable coating process, which can be obtained by a speed sensor.

[0029] In this embodiment, the future time refers to a time point after the current data collection time point, which is used to predict parameters such as coating thickness.

[0030] In this embodiment, the coating thickness prediction value at a future time is the value of the cable coating thickness at a certain time in the future that is predicted through calculation.

[0031] In this embodiment, target thickness refers to a preset thickness standard that the cable coating is expected to achieve.

[0032] In this embodiment, the preset deviation threshold is a limit value set in advance for determining whether the deviation between the coating thickness prediction value and the target thickness at a future moment is too large.

[0033] In this embodiment, the walking speed adjustment amount is the amount calculated to adjust the robot's walking speed when the deviation between the coating thickness prediction value and the target thickness at a future moment exceeds a preset threshold.

[0034] In this embodiment, the robot driving wheel positive pressure compensation amount is: when the above-mentioned deviation exceeds the threshold, the amount of compensation for the robot driving wheel positive pressure is calculated.

[0035] In this embodiment, the walking optimization control of the robot is performed based on the walking speed adjustment amount and the positive pressure compensation amount of the robot driving wheel to obtain the walking optimization control result: the calculated walking speed adjustment amount and the positive pressure compensation amount of the robot driving wheel are used as control parameters to adjust the robot walking, and finally a control effect is obtained that makes the coating closer to the target thickness.

[0036] In order to extract the real-time cable radius and cable cross-sectional roundness and obtain the key cable geometric parameters, a real-time reconstruction of the cable surface 3D profile and extraction of the real-time cable radius and cable cross-sectional roundness is proposed, including: The three-dimensional profile of the cable surface is reconstructed based on multiple groups of laser profile sensors arranged around the robot's walking mechanism and a triangulation ranging method; Extract real-time cable radius and cable cross-section roundness based on the 3D profile of the cable surface.

[0037] In this embodiment, the three-dimensional contour of the cable surface is reconstructed based on multiple groups of laser profile sensors arranged circumferentially around the robot's walking mechanism and a triangulation ranging method: multiple groups of laser profile sensors are arranged in the circumferential direction of the robot's walking mechanism, and the principle of triangulation ranging is used to measure the position of each point on the cable surface through laser emission and reflection, thereby constructing the shape of the cable surface in three-dimensional space.

[0038] In this embodiment, the real-time cable radius and cable cross-section roundness are extracted based on the three-dimensional profile of the cable surface: the center of the cable cross-section is determined based on the reconstructed three-dimensional cable surface profile data, and the real-time cable radius is obtained by calculating the distance from the center to the surface; and the cable cross-section roundness is obtained by analyzing the radius-related parameters in the profile data.

[0039] In order to achieve accurate multi-parameter acquisition, it is proposed to collect spray pressure recording data, cable surface temperature field distribution, and real-time normal component of wheel-cable contact force and tangential friction force, including: The high-frequency pressure sensor and infrared temperature sensor set at the outlet of the robot spray gun synchronously collect spray pressure recording data and cable surface temperature field distribution; The normal component of the wheel-cable contact force and the tangential friction force are collected in real time based on the six-axis force sensor integrated at the driving wheel of the robot.

[0040] In this embodiment, a high-frequency pressure sensor and an infrared temperature sensor are installed at the outlet of the robot's spray gun. The high-frequency pressure sensor quickly and accurately measures the pressure of the paint being sprayed, while the infrared temperature sensor senses the surface temperature of the cable. These two sensors operate simultaneously to collect data on the spray pressure and the cable surface temperature distribution.

[0041] In this embodiment, a six-axis force sensor integrated in the robot's drive wheel collects the normal component of the wheel-cable contact force and the tangential friction force in real time. A six-axis force sensor is integrated where the robot contacts the cable and provides the driving force. This sensor measures the normal component of the force in the vertical direction and the friction in the tangential direction when the robot's drive wheel contacts the cable in real time, thereby obtaining real-time data on the normal component of the wheel-cable contact force and the tangential friction force.

[0042] In order to calculate the coating thickness prediction value at the future moment and provide a basis for coating thickness control, it is proposed to obtain the coating thickness prediction value at the future moment based on the real-time cable radius, cable cross-section roundness, spray pressure record data, cable surface temperature field distribution, the normal component and tangential friction force of the real-time wheel-cable contact force, and the real-time walking speed of the robot, including: Perform trend prediction on the collected real-time cable radius, cable cross-sectional roundness, spray pressure record data, cable surface temperature field distribution, and the normal component of the real-time wheel-cable contact force and the tangential friction force, and obtain the predicted values ​​of the real-time cable radius, cable cross-sectional roundness, cable surface temperature field distribution, the normal component of the real-time wheel-cable contact force and the tangential friction force at future times, as well as the predicted values ​​of the spray pressure record data to future times; Determine the average spraying pressure in the latest historical period based on the future time and the instantaneous pressure fluctuation amplitude at the future time based on the predicted value of the spraying pressure record data to the future time; Calculate the base coating thickness value at the future moment based on the average spraying pressure in the latest historical period based on the future moment, the instantaneous pressure fluctuation amplitude at the future moment, the predicted value of the real-time cable radius at the future moment, the predicted value of the linear spraying speed of the standard paint at the future moment, and the current walking speed of the robot; Determining a roundness deviation rate at a future time based on a predicted value of the cable cross-section roundness at a future time, and determining a first correction coefficient of a coating thickness base value based on the roundness deviation rate at the future time; Determining a local temperature gradient factor at a future time based on a predicted value of the cable surface temperature field distribution at a future time, and determining a second correction coefficient of the coating thickness base value based on the local temperature gradient factor at the future time; Determining a friction coefficient at a future time based on predicted values ​​of the normal component of the real-time wheel-cable contact force and the tangential friction force at a future time, and determining a third correction coefficient for the base value of the coating thickness based on the friction coefficient at the future time; The coating thickness prediction value at the future moment is calculated based on the coating thickness base value at the future moment and the corresponding first correction coefficient, second correction coefficient, and third correction coefficient.

[0043] In this embodiment, trend prediction is performed on the collected real-time cable radius, cable cross-sectional roundness, spray pressure record data, cable surface temperature field distribution, and the normal component of the real-time wheel-cable contact force and the tangential friction force to obtain the predicted values ​​of the real-time cable radius, cable cross-sectional roundness, cable surface temperature field distribution, and the normal component of the real-time wheel-cable contact force and the tangential friction force at the future time, as well as the predicted value of the spray pressure record data to the future time: For the real-time cable radius, based on the collected data points, analyze its changes in the past time period, such as whether it gradually increases, decreases, or remains relatively stable. Then, use appropriate prediction algorithms, such as time series analysis or regression models in machine learning, to infer its value at a specific moment in the future. This value is the predicted value of the real-time cable radius at the future moment.

[0044] The roundness of the cable cross section is similar. Based on the existing roundness data, its changing trend is studied, and then the corresponding prediction method is used to obtain the predicted value of the cable cross section roundness at a future time.

[0045] For the cable surface temperature field distribution, the temperature data collected previously at different locations and time points are integrated, and the evolution of the temperature distribution over time is observed. Using prediction methods specifically for spatial and time series data (for example, spatiotemporal autoregressive moving average models, long short-term memory networks-convolutional neural networks based on deep learning, kriging interpolation methods combined with time series prediction, etc.), the distribution of the cable surface temperature field at various locations at future moments is predicted, that is, the predicted value of the cable surface temperature field distribution at future moments.

[0046] The normal component of the real-time wheel-cable contact force and the tangential friction force are also analyzed based on the existing measurement data. Their changing patterns with factors such as the robot's walking are analyzed, and with the help of methods suitable for force data prediction (for example, Kalman filtering, support vector regression, grey prediction model, etc.), the predicted values ​​of the normal component and the tangential friction force at future moments are obtained.

[0047] The spraying pressure record data analyzes the pressure fluctuation pattern over time based on the recorded pressure value sequence, and uses time series prediction models and other methods to predict the pressure changes from the current time to the future time, thereby obtaining the predicted value of the spraying pressure record data to the future time.

[0048] In this embodiment, the average spraying pressure in the latest historical period based on the future time and the instantaneous pressure fluctuation amplitude at the future time are determined based on the predicted value of the spraying pressure record data to the future time: First, identify a specific moment in the future. Add up all the spray pressure prediction data from a certain point in the past to this future moment, then divide it by the total number of data points. The result is the average spray pressure over the most recent historical period based on the future moment.

[0049] To determine the instantaneous pressure fluctuation amplitude at a future moment, select a very short time period near the future moment, such as a few milliseconds. Find the maximum and minimum spray pressure values ​​within this very short time period. Subtract the minimum value from the maximum value, and the difference is the instantaneous pressure fluctuation amplitude at the future moment.

[0050] In this embodiment, the base coating thickness value at a future moment is calculated based on the average spraying pressure in the latest historical period based on the future moment, the instantaneous pressure fluctuation amplitude at the future moment, the predicted value of the real-time cable radius at the future moment, the predicted value of the linear spraying speed of the standard paint at the future moment, and the current walking speed of the robot: The basic coating thickness value is calculated using the formula: The basic coating thickness value is equal to the pressure-thickness conversion coefficient multiplied by (the average spray pressure plus 0.7 times the instantaneous pressure fluctuation amplitude) times the square root of the predicted cable radius, divided by (the robot's current walking speed times the predicted linear spray speed of the standard coating). The pressure-thickness conversion coefficient here is obtained through experimental calibration and varies for different types of coatings. For example, the coefficient for epoxy coating is 2.3×10⁻ 5 (mm・s / kg), the coefficient of polyurethane coating is 1.8×10⁻ 5 (mm・s / kg).

[0051] In this embodiment, the roundness deviation rate at the future moment is determined based on the predicted value of the cable cross-section roundness at the future moment, and the first correction coefficient of the coating thickness base value is determined based on the roundness deviation rate at the future moment: Determining the roundness deviation rate: Based on the 3D cable surface profile, the distance distribution from each point on the cable cross section to the center at future times is predicted. From this distance data, the maximum, minimum, and average distances are determined. Based on the average distance, the absolute deviation rate is calculated by subtracting the maximum distance from the minimum distance, dividing it by the average distance, and multiplying by 100%. Furthermore, based on the design nominal radius, the relative deviation rate is calculated by dividing the maximum distance from the minimum distance by the design nominal radius, and multiplying by 100%. Furthermore, the shape factor is calculated from the cross-sectional area and perimeter. The shape factor deviation term is equal to 1 minus the shape factor difference multiplied by 50%. Different weights are assigned to these three deviation rates based on cable type. For high-voltage cables (design nominal radius greater than 50 mm), the absolute deviation rate is weighted at 0.5, the relative deviation rate at 0.3, and the shape factor deviation term at 0.2. For low-voltage cables (design nominal radius ≤50 mm), the absolute deviation rate is weighted at 0.3, the relative deviation rate at 0.5, and the shape factor deviation term at 0.2. The initial roundness deviation rate is calculated by multiplying the three deviation rates by their respective weights and adding them together. If the maximum distance minus the minimum distance is greater than 0.2 times the average distance, add 10% to the initial roundness deviation rate to obtain the final roundness deviation rate.

[0052] Determine the first correction coefficient: Through experiments or empirical data, find the relationship between the roundness deviation rate and the first correction coefficient. Generally speaking, the greater the roundness deviation rate, the more uneven the coating thickness, and the first correction coefficient will change according to a certain pattern, such as linear change or other functional relationship. Substitute the roundness deviation rate at a future time into the relationship between the roundness deviation rate and the first correction coefficient to determine the first correction coefficient.

[0053] In this embodiment, the second correction coefficient of the coating thickness base value is determined based on the local temperature gradient factor at the future time: The relationship between the local temperature gradient factor and the second correction factor is determined through experiments or accumulated empirical data. Generally, a larger local temperature gradient factor indicates a greater impact of the temperature field on the coating thickness, and the second correction factor will vary according to a specific functional relationship. For example, through extensive experiments, a relationship table or functional expression corresponding to the local temperature gradient factor and the second correction factor is obtained. The second correction factor for the base coating thickness is determined based on the local temperature gradient factor at a future time and this functional relationship or expression.

[0054] In this embodiment, the friction coefficient at a future time is determined based on the normal component of the real-time wheel-cable contact force and the predicted value of the tangential friction force at a future time: Get the predicted normal component value and tangential friction force value of the real-time wheel-cable contact force at the future time.

[0055] Dividing the predicted value of the tangential friction force by the predicted value of the normal component gives us the coefficient of friction at that future moment.

[0056] In this embodiment, the third correction coefficient of the coating thickness base value is determined based on the friction coefficient at a future time: Through experimentation or experience, the relationship between the friction coefficient and the third correction factor is determined. Generally speaking, when the friction coefficient deviates from the optimal value, it will affect the coating thickness, and the third correction factor will change according to a certain pattern with the friction coefficient. For example, when the friction coefficient exceeds a certain value, the third correction factor will be reduced by a certain proportion to correct the base coating thickness. Substituting the friction coefficient at a future time into the relationship between the friction coefficient and the third correction factor, the third correction factor for the base coating thickness is obtained.

[0057] In this embodiment, the coating thickness prediction value at the future moment is calculated based on the coating thickness base value at the future moment and the corresponding first correction coefficient, second correction coefficient, and third correction coefficient: The coating thickness prediction value at a future moment is equal to the coating thickness base value multiplied by the first correction coefficient multiplied by the second correction coefficient multiplied by the third correction coefficient, thereby obtaining a coating thickness prediction value at a future moment that is closer to the actual situation.

[0058] In order to obtain the roundness deviation rate at a future moment, thereby more accurately reflecting the impact of cable roundness changes on coating thickness, it is proposed to determine the roundness deviation rate at a future moment based on the predicted value of the cable cross-section roundness at a future moment, including: Based on the three-dimensional profile of the cable surface, the cable cross-section circumferential center distance distribution vector at the future moment is predicted, and the maximum center distance, minimum center distance, average center distance, center distance standard deviation and roundness shape factor of the cable cross-section at the future moment are extracted from the cable cross-section circumferential center distance distribution vector; Correcting the circumferential center distance distribution vector of the cable cross section at the future moment based on the maximum center distance, minimum center distance, average center distance, center distance standard deviation, and roundness shape factor of the cable cross section at the future moment to obtain a corrected circumferential center distance distribution vector of the cable cross section at the future moment; Calculate the roundness basic deviation rate at the future moment based on the cable cross-section circumferential center distance correction distribution vector at the future moment; Performing circumferential partitioning based on the cable cross section at the future moment to obtain a plurality of circumferential partitions, and calculating an average center distance deviation of each circumferential partition based on the circumferential center distance correction distribution vector of the cable cross section at the future moment; Based on the distribution positions of all circumferential partitions whose average center distance deviations are greater than the preset center distance deviation threshold and the corresponding over-limit values ​​of the average center distance deviations, the basic roundness deviation rate at future moments is corrected to obtain the roundness deviation rate at future moments.

[0059] In this embodiment, the cable cross-section circumferential center distance distribution vector at a future time is predicted based on the three-dimensional profile of the cable surface: Based on the real-time reconstructed 3D cable surface profile data, the cable cross-section at a certain point in the future is analyzed. The distance from each point on the cable cross-section to the center of the cable cross-section is determined along the circumference of the cable cross-section. These distances are arranged in order along the circumference to form a vector, the cable cross-section circumferential center distance distribution vector.

[0060] In this embodiment, the maximum center distance, minimum center distance, average center distance, center distance standard deviation and roundness shape factor of the cable cross section at a future time are extracted from the cable cross section circumferential center distance distribution vector: Among the obtained cable cross-section circumferential center distance distribution vectors, find the distance with the largest value, which is the maximum center distance of the cable cross-section at the future moment; find the distance with the smallest value, which is the minimum center distance of the cable cross-section at the future moment.

[0061] Add up all the distance values ​​in the vector and divide it by the number of elements in the vector. The result is the average center distance of the cable cross section at the future time.

[0062] To calculate the standard deviation of the center distance, first find the difference between each distance value and the average center distance, square these differences respectively, add them up, divide them by the number of elements, and finally take the square root of the result to get the standard deviation of the center distance.

[0063] The circularity shape factor is calculated using the area and perimeter of the cable's cross section. The formula is: 4 times pi times the cross-sectional area, divided by the square of the cross-sectional perimeter. This factor measures how close the cable's cross section is to being circular.

[0064] In this embodiment, the circumferential center distance distribution vector of the cable cross section at the future moment is corrected based on the maximum center distance, minimum center distance, average center distance, center distance standard deviation, and roundness shape factor of the cable cross section at the future moment to obtain a corrected distribution vector of the circumferential center distance of the cable cross section at the future moment: Using the obtained parameters for maximum center distance, minimum center distance, average center distance, center distance standard deviation, and roundness shape factor, the circumferential center distance distribution vector of the cable cross section is adjusted. For example, based on the center distance standard deviation and average center distance: the center distance standard deviation is 0.2mm, and with an average center distance of 10.0mm as the reference, a range of ±1.5 times the standard deviation (9.7mm-10.3mm) is considered. The data in the vector falls within this range, but values ​​near the edges can be adjusted to be more centered around the average center distance. For example, 10.3mm can be adjusted to 10.2mm, and 9.7mm can be adjusted to 9.8mm, resulting in the vector becoming [10.1, 9.9, 10.2, 9.8, 10.0, 10.2, 9.8].

[0065] Considering the maximum and minimum center distances and the circularity shape factor: While the maximum center distance of 10.3mm and the minimum center distance of 9.7mm haven't been exceeded, further fine-tuning is required to ensure a more circular shape (a circularity shape factor of 0.96 indicates a near-circular shape). For example, 10.2mm can be appropriately reduced to 10.15mm to achieve a more even data distribution. The resulting corrected distribution vector for the circumferential center distances of the cable cross section at the future time is [10.1, 9.9, 10.15, 9.8, 10.0, 10.15, 9.8]. This vector, corrected based on various parameters, more accurately reflects the true geometric characteristics of the cable cross section.

[0066] The vector obtained after correction is the cable cross-section circumferential center distance correction distribution vector at the future moment.

[0067] In this embodiment, the roundness basic deviation rate at the future moment is calculated based on the cable cross-section circumferential center distance correction distribution vector at the future moment: Taking the average center distance as the benchmark, subtract the minimum center distance from the maximum center distance, divide the difference by the average center distance, and finally multiply by 100%. In this way, a deviation rate is calculated. This deviation rate is the basic deviation rate of roundness at future moments, which preliminarily reflects the degree of deviation of the roundness of the cable section from the ideal circle.

[0068] In this embodiment, circumferential partitioning is performed based on the cable cross section at a future time to obtain multiple circumferential partitions: The cable cross section at a future moment is divided along the circumferential direction at a certain angular interval, such as 30° intervals, so that the cable cross section is divided into 12 sector-shaped areas, which are multiple circumferential partitions.

[0069] In this embodiment, the average center distance deviation of each circumferential partition is calculated based on the cable cross-section circumferential center distance correction distribution vector at the future moment: For each circumferential partition, extract the distance values ​​belonging to the partition from the cable cross-section circumferential center distance correction distribution vector. Add these distance values ​​and divide them by the number of distance values ​​in the partition to obtain the average center distance of the partition. Then subtract the average center distance of the cable cross-section from this average center distance. The difference is the average center distance deviation of the circumferential partition, which reflects the degree of deviation of each circumferential partition from the overall average of the cable cross-section.

[0070] In this embodiment, the center distance deviation threshold is preset: a preset value used as a standard for determining whether the average center distance deviation of the circumferential partitions is abnormal.

[0071] In this embodiment, the average center distance deviation exceeds the limit value: when the average center distance deviation of a circumferential zone exceeds the preset center distance deviation threshold, the portion of the deviation exceeding the threshold is the average center distance deviation exceedance value. For example, if the preset center distance deviation threshold is 0.5 and the average center distance deviation of a circumferential zone is 0.8, the exceedance value is 0.8 - 0.5 = 0.3.

[0072] In this embodiment, based on the distribution positions of all circumferential partitions whose average center distance deviations are greater than a preset center distance deviation threshold and the corresponding average center distance deviations exceeding the limit, the roundness basic deviation rate at the future moment is corrected to obtain the roundness deviation rate at the future moment: Count all circumferential zones where the mean center distance deviation exceeds the preset threshold, record their circumferential distribution, and record the mean center distance deviation exceedance value for each zone. Based on the distribution of these circumferential zones and the magnitude of the exceedance values, adjust the previously calculated basic roundness deviation rate. For example, if there are zones where the mean center distance deviation exceeds the threshold, and the exceedance values ​​vary, the basic roundness deviation rate needs to be corrected. Based on experience or pre-set rules, the deviation rates for each zone exceeding the threshold are accumulated.

[0073] Assume that the deviation rate increment corresponding to each over-limit value is set as follows: the deviation rate increases by 1% for every 0.1 mm of over-limit value.

[0074] Then the first partition increases the deviation rate by 3% (0.3mm corresponds to 3 0.1mm), the third partition increases the deviation rate by 5% (0.5mm corresponds to 5 0.1mm), and the fifth partition increases the deviation rate by 4% (0.4mm corresponds to 4 0.1mm).

[0075] The final adjusted roundness deviation rate is 10% + 3% + 5% + 4% = 22%. By adjusting the basic roundness deviation rate based on the circumferential distribution and the size of the limit, the actual roundness deviation of the cable cross section can be more accurately reflected.

[0076] In order to determine the local temperature gradient factor at a future time and quantify the influence of the temperature field on the coating thickness, it is proposed to determine the local temperature gradient factor at a future time based on the predicted value of the cable surface temperature field distribution at a future time, including: A 3D cable model is established based on the 3D contour of the cable surface, and the 3D cable model is spatially divided to obtain a cable space grid; Based on the predicted value of the cable surface temperature field distribution at a future time, the normalized value of the temperature gradient amplitude, the cosine value of the angle between the temperature gradient direction angle and the spraying direction, and the exponential decay term of the gradient change rate of each cable space grid are calculated. The normalized value of the temperature gradient amplitude, the cosine value of the angle between the temperature gradient direction angle and the spraying direction, and the exponential decay term of the gradient change rate of each cable space grid are weightedly summed to obtain the local temperature gradient factor of each cable space grid; The local temperature gradient factor of all cable space grids in the coating section at the future moment is weighted averaged to obtain the local temperature gradient factor at the future moment.

[0077] In this embodiment, a three-dimensional cable model is established based on the three-dimensional contour of the cable surface: according to the actual shape and size information of the three-dimensional contour of the cable surface, a digital model that can accurately present the geometric shape of the cable is constructed in three-dimensional space, and the cable is modeled as an entity in three-dimensional space.

[0078] In this embodiment, the three-dimensional cable model is spatially divided to obtain a cable space grid: the three-dimensional space where the established three-dimensional cable model is located is divided into many small space units, just like cutting a solid object. These units constitute the cable space grid.

[0079] In this embodiment, the normalized value of the temperature gradient amplitude of each cable spatial grid, the cosine value of the angle between the temperature gradient direction angle and the spraying direction, and the exponential decay term of the gradient change rate are calculated based on the predicted value of the cable surface temperature field distribution at the future time: Normalized temperature gradient amplitude: Based on the predicted cable surface temperature field data for the future, calculate the temperature change rate within each cable spatial grid, i.e., the temperature gradient amplitude. This value is then divided by the average temperature gradient amplitude of the entire cable to obtain the normalized value of the temperature gradient amplitude for that unit.

[0080] Cosine of the angle between the temperature gradient direction angle and the spray direction: Determine the angle between the direction of fastest temperature change within each cable spatial grid and the cable axis, i.e., the temperature gradient direction angle. This ensures that the spray direction is radially outward. Calculate the angle between the temperature gradient direction angle and the spray direction, and then find the cosine of this angle.

[0081] Exponential decay term of gradient change rate: Calculate the speed of change of the temperature gradient amplitude in each cable space grid over time or space, that is, the temperature gradient change rate. Assuming the temperature gradient change rate is r, the exponential decay term is usually calculated as e −kr (where k is a constant, for example k=0.1).

[0082] In this embodiment, the local temperature gradient factor for each cable spatial grid is obtained by weightedly summing the normalized value of the temperature gradient amplitude, the cosine value of the angle between the temperature gradient direction angle and the spraying direction, and the exponential decay term of the gradient change rate. Weights are assigned to the normalized value of the temperature gradient amplitude, the cosine value of the angle between the temperature gradient direction angle and the spraying direction, and the exponential decay term of the gradient change rate, such as 0.5, 0.3, and 0.2, respectively. These three values ​​for each cell are multiplied by their respective weights and then added together to obtain the local temperature gradient factor for each cable spatial grid.

[0083] In this embodiment, the local temperature gradient factors of all cable spatial grids within the future coating section are weighted averaged to obtain the local temperature gradient factor for the future time. For the cable coating section at the future time, the local temperature gradient factors of all cable spatial grids within that section are weighted averaged. The weight of each grid cell is determined based on its area and prediction confidence, with larger areas and higher prediction confidence receiving greater weights. The local temperature gradient factors of all cells are multiplied by their respective weights, added together, and then divided by the sum of the weights to obtain the local temperature gradient factor for the future time.

[0084] In order to obtain the walking speed adjustment amount and the robot driving wheel positive pressure compensation amount to achieve accurate compensation, it is proposed that when the deviation between the coating thickness prediction value at the future moment and the corresponding target thickness is greater than the preset deviation threshold, the walking speed adjustment amount and / or the robot driving wheel positive pressure compensation amount are calculated, including: Determine the coating position of the robot at a future time based on the current walking speed and current coating position of the robot, and obtain the target thickness of the coating position of the robot at the future time; When the deviation between the coating thickness prediction value at the future moment and the target thickness at the coating position of the robot at the future moment is greater than the preset deviation threshold, the basic walking speed compensation amount is calculated based on the speed adjustment coefficient, the deviation between the coating thickness prediction value at the future moment and the target thickness at the coating position of the robot at the future moment, the target thickness, the current walking speed, the standard walking speed, the current cable radius, and the nominal cable radius, and the basic walking speed compensation amount is dynamically corrected based on the dynamic correction rule to obtain the optimal walking speed compensation amount; At the same time, the positive pressure basic compensation of the robot driving wheel is calculated based on the positive pressure adjustment coefficient, the deviation between the coating thickness prediction value at the future moment and the target thickness at the coating position of the robot at the future moment, the target thickness, the current wheel-cable friction coefficient, the optimal friction coefficient, the nominal positive pressure, the current cable radius and the nominal cable radius, and the positive pressure basic compensation of the robot driving wheel is corrected by safety constraints to obtain the optimal compensation of the robot driving wheel.

[0085] In this embodiment, the current coating position of the robot refers to the specific position of the robot on the cable at the current moment when performing the coating operation, which can be determined by the robot's own positioning system or position marks related to the cable.

[0086] In this embodiment, the coating position of the robot at a future moment is determined based on the current walking speed and current coating position of the robot: given the walking speed of the robot at this moment and the current coating position on the cable, it is assumed that the time interval between the future moment and the current moment is t. If the current walking speed of the robot is constant, then the coating position at a future moment is equal to the current coating position plus the current walking speed multiplied by the time interval t. For example, if the current coating position is 10 meters away from the starting end of the cable, the current walking speed is 1 meter per second, and the future moment is 10 seconds later, then the coating position at a future moment will be 10+1×10=20 meters away from the starting end of the cable. If the robot is in an accelerating or decelerating state, the coating position at a future moment needs to be calculated according to the corresponding kinematic formula.

[0087] In this embodiment, the target coating thickness at the robot's coating location at a future time is obtained: a predetermined coating thickness value for the cable coating location at the future time is set based on the cable coating process requirements and quality standards. This value represents the coating thickness the robot is expected to achieve at that location and is used for comparison with the predicted coating thickness.

[0088] In this embodiment, a preset deviation threshold is a pre-set value used to determine whether the deviation between the predicted coating thickness at a future time and the target thickness is excessive. If the absolute value of the difference between the predicted coating thickness at a future time and the target thickness at the robot's coating location at that future time exceeds this preset deviation threshold, the predicted coating thickness is considered to have deviated beyond the allowable range, and the robot's travel speed or drive wheel positive pressure needs to be adjusted.

[0089] In this embodiment, the speed adjustment coefficient is a parameter used to adjust the robot's travel speed. It is set to different values ​​based on the level of deviation between the predicted coating thickness and the target thickness at a given moment. For example, when the deviation is within the first deviation range (5% < |deviation between predicted and target thickness| ≤ 10%), the speed adjustment coefficient is 0.8; when it is within the second deviation range (10% < |deviation| ≤ 15%), the speed adjustment coefficient is 1.2; and when it is within the third deviation range (|deviation| > 15%), the speed adjustment coefficient is 1.5. This coefficient is used to calculate the travel speed compensation, adjusting the robot's travel speed to bring the coating thickness closer to the target value.

[0090] In this embodiment, the standard walking speed is the standard walking speed value set when the robot performs cable coating operations under ideal conditions.

[0091] In this embodiment, the nominal cable radius refers to the standard radius value specified when the cable is designed or produced.

[0092] In this embodiment, the walking speed basic compensation is calculated based on the speed adjustment coefficient, the deviation between the coating thickness prediction value at the future time and the target thickness at the coating position of the robot at the future time, the target thickness, the current walking speed, the standard walking speed, the current cable radius, and the nominal cable radius: Calculate the deviation between the coating thickness prediction value at the future moment and the target thickness at the coating position of the robot at the future moment, and subtract the target thickness from the predicted thickness to obtain the deviation value; Then divide this deviation value by the target thickness to get the deviation ratio; Divide the current walking speed by the standard walking speed to get the speed ratio; Then divide the current cable radius by the nominal cable radius to get the radius ratio, and take the square root of it; Finally, multiply the above-obtained deviation ratio, speed ratio, square root of radius ratio and speed adjustment coefficient to get the basic compensation of walking speed. The formula is: Travel speed basic compensation = speed adjustment coefficient × (deviation between predicted thickness and target thickness ÷ target thickness) × (current travel speed ÷ standard travel speed) × (current cable radius ÷ nominal cable radius) 1 / 2 .

[0093] In this embodiment, the positive pressure adjustment coefficient is a parameter used to adjust the positive pressure applied by the robot's drive wheels. Similar to the speed adjustment coefficient, it is set to different values ​​based on the level of deviation between the predicted coating thickness and the target thickness at a given moment. For level 2 deviation, the value is 0.3; for level 3 deviation, the value is 0.5. This coefficient is used to calculate the positive pressure compensation applied by the robot's drive wheels, adjusting the positive pressure applied by the drive wheels to the cable, thereby affecting the coating thickness.

[0094] In this embodiment, the current wheel-cable friction coefficient is the friction coefficient of the robot's driving wheel and the cable in the current contact state. It reflects the magnitude of the friction force between the driving wheel and the cable. The six-axis force sensor integrated at the robot's driving wheel measures the normal component and tangential friction force of the real-time wheel-cable contact force, and then divides the tangential friction force by the normal component to obtain the coefficient.

[0095] In this embodiment, the optimal friction coefficient is determined through experimental calibration, and is the wheel-cable friction coefficient value that enables the robot to achieve optimal operation during coating operations. It is fixed at 0.45. When calculating the basic compensation for the robot's drive wheel positive pressure, the current wheel-cable friction coefficient is compared with the optimal friction coefficient to determine the positive pressure adjustment amount to ensure the stability of the robot's movement and coating operations.

[0096] In this embodiment, nominal positive pressure refers to the standard positive pressure applied by the robot's drive wheels to the cable, as specified in the robot design or cable coating process (the reference value specified in the design or process). This serves as a reference standard for calculating the robot's drive wheel positive pressure compensation.

[0097] In this embodiment, the positive pressure base compensation of the robot driving wheel is calculated based on the positive pressure adjustment coefficient, the deviation between the coating thickness prediction value at the future time and the target thickness at the coating position of the robot at the future time, the target thickness, the current wheel-cable friction coefficient, the optimal friction coefficient, the nominal positive pressure, the current cable radius and the nominal cable radius: First, calculate the deviation between the predicted coating thickness at the future moment and the target thickness at the robot's coating position at the future moment, and subtract the target thickness from the predicted thickness to obtain the deviation. Then divide this deviation value by the target thickness to get the deviation ratio; Then divide the current wheel-cable friction coefficient by the optimal friction coefficient to get the friction coefficient ratio; Then divide the current cable radius by the nominal cable radius to get the radius ratio; Finally, multiply the deviation ratio, friction coefficient ratio, radius ratio, nominal positive pressure, and positive pressure adjustment coefficient obtained above to get the basic positive pressure compensation of the robot's driving wheel. The formula is: Positive pressure basic compensation = positive pressure adjustment coefficient × (deviation between predicted thickness and target thickness ÷ target thickness) × (current wheel-cable friction coefficient ÷ optimal friction coefficient) × nominal positive pressure × (current cable radius ÷ nominal cable radius).

[0098] In order to obtain a more realistic optimal compensation amount for walking speed, a dynamic correction rule is proposed to dynamically correct the basic compensation amount for walking speed to obtain the optimal compensation amount for walking speed, including: If the instantaneous pressure fluctuation frequency of the paint is greater than the preset fluctuation frequency threshold, a first correction coefficient of the basic walking speed compensation is determined based on the instantaneous pressure fluctuation frequency of the paint and the correlation coefficient between the instantaneous pressure fluctuation frequency of the paint and the speed compensation; If the cable cross-sectional roundness deviation rate is greater than a preset deviation rate threshold, a second correction coefficient of the basic walking speed compensation amount is determined based on the cable cross-sectional roundness deviation rate and the cable cross-sectional roundness deviation rate-speed compensation amount correlation coefficient; The basic walking speed compensation amount is dynamically corrected based on the first correction coefficient and the second correction coefficient of the basic walking speed compensation amount to obtain the optimal walking speed compensation amount.

[0099] In this embodiment, the preset fluctuation frequency threshold is a pre-set numerical standard for the instantaneous pressure fluctuation frequency of the coating. During the actual coating process, the instantaneous pressure fluctuation frequency of the coating is compared with this threshold to determine whether the pressure fluctuation frequency is within the range requiring correction of the walking speed compensation amount. For example, it may be set to 10Hz.

[0100] In this embodiment, the instantaneous pressure fluctuation frequency of the paint refers to the number of times the paint pressure fluctuates per unit time during the paint spraying process, and is acquired in real time by a high-frequency pressure sensor installed at the outlet of the robot spray gun.

[0101] In this embodiment, the paint instantaneous pressure fluctuation frequency-speed compensation correlation coefficient is a coefficient used to establish the relationship between the paint instantaneous pressure fluctuation frequency and the travel speed compensation. This coefficient is determined through experimentation, experience, or theoretical analysis, and indicates the degree to which the travel speed compensation changes for each unit change in the paint instantaneous pressure fluctuation frequency. For example, if this coefficient is 0.02, it means that for every 1Hz increase in the paint instantaneous pressure fluctuation frequency, the travel speed compensation will be adjusted according to a specific rule (such as multiplying by a value related to this coefficient).

[0102] In this embodiment, the first correction coefficient of the basic walking speed compensation is determined based on the instantaneous pressure fluctuation frequency of the coating and the correlation coefficient between the instantaneous pressure fluctuation frequency of the coating and the speed compensation: When the paint's instantaneous pressure fluctuation frequency exceeds the preset frequency threshold, the paint's instantaneous pressure fluctuation frequency is multiplied by the paint's instantaneous pressure fluctuation frequency-speed compensation coefficient, then added to 1. The resulting value is the first correction coefficient for the basic walking speed compensation. For example, if the paint's instantaneous pressure fluctuation frequency is 15Hz, the paint's instantaneous pressure fluctuation frequency-speed compensation coefficient is 0.02, and the preset frequency threshold is 10Hz, then the first correction coefficient = 1 + 15 × 0.02 = 1.3. This first correction coefficient is used to correct the basic walking speed compensation to more accurately adjust the robot's walking speed and adapt to the impact of paint pressure fluctuations on coating thickness.

[0103] In this embodiment, the preset deviation rate threshold is a pre-set numerical limit for the cable cross-section roundness deviation rate. During the actual calculation and adjustment process, the cable cross-section roundness deviation rate is compared with this threshold to determine whether the cable cross-section roundness deviation has reached a level that requires further correction of the travel speed compensation. For example, it may be set to 5%.

[0104] In this embodiment, the cable cross-sectional roundness deviation rate-speed compensation correlation coefficient is a coefficient used to correlate the cable cross-sectional roundness deviation rate with the travel speed compensation amount. Similar to the paint instantaneous pressure fluctuation frequency-speed compensation correlation coefficient, this coefficient is derived through experimentation, experience, or theoretical analysis, and reflects the degree of change in the travel speed compensation amount for each unit change in the cable cross-sectional roundness deviation rate. For example, a coefficient of 0.05 indicates that for every 1% increase in the cable cross-sectional roundness deviation rate, the travel speed compensation amount will be adjusted according to a specific rule based on this coefficient.

[0105] In this embodiment, the second correction coefficient of the basic compensation amount of the walking speed is determined based on the cable cross-section roundness deviation rate and the cable cross-section roundness deviation rate-speed compensation amount correlation coefficient: When the cable cross-sectional roundness deviation rate exceeds the preset deviation rate threshold, the cable cross-sectional roundness deviation rate is multiplied by the cable cross-sectional roundness deviation rate-speed compensation correlation coefficient, and then added by 1. The resulting value is the second correction factor for the basic travel speed compensation. For example, if the cable cross-sectional roundness deviation rate is 8%, the cable cross-sectional roundness deviation rate-speed compensation correlation coefficient is 0.05, and the preset deviation rate threshold is 5%, the second correction factor = 1 + 8 × 0.05 = 1.4. This second correction factor is used to further correct the basic travel speed compensation to account for the impact of cable roundness deviation on coating thickness.

[0106] In this embodiment, the basic walking speed compensation amount is dynamically corrected based on the first correction coefficient and the second correction coefficient of the basic walking speed compensation amount to obtain the optimal walking speed compensation amount: Multiplying the basic walking speed compensation by the first and second correction coefficients respectively yields the optimal walking speed compensation. For example, if the basic walking speed compensation is 0.5, the first correction coefficient is 1.3, and the second correction coefficient is 1.4, then the optimal walking speed compensation = 0.5 × 1.3 × 1.4 = 0.91. This dynamic correction method comprehensively considers the impact of the coating's instantaneous pressure fluctuation frequency and the cable cross-section roundness deviation rate on walking speed, making the calculated walking speed compensation more consistent with actual coating requirements and helping to improve coating quality.

[0107] In order to obtain the optimal compensation amount of the robot's driving wheels and ensure that the positive pressure compensation is safe and reasonable, it is proposed to perform safety constraint correction on the positive pressure basic compensation amount of the robot's driving wheels to obtain the optimal compensation amount of the robot's driving wheels, including: If the front wheel-cable friction coefficient is greater than the preset friction coefficient threshold, the product of the normal pressure base compensation of the robot driving wheel and the preset constraint correction coefficient is used as the first correction compensation of the robot driving wheel; Performing advance compensation on the first correction compensation amount of the robot driving wheel to obtain a second correction compensation amount of the robot driving wheel; The second correction compensation amount of the robot driving wheel is corrected for the third time based on the nominal positive pressure and the preset safety constraint ratio to obtain the optimal compensation amount of the robot driving wheel.

[0108] In this embodiment, the preset friction coefficient threshold is a pre-set numerical standard for the wheel-cable friction coefficient. During the robotic coating operation, the current wheel-cable friction coefficient obtained in real time is compared with it. For example, it may be set to 0.6.

[0109] In this embodiment, the preset constraint correction coefficient is a pre-determined coefficient used to correct the base positive pressure compensation of the robot's drive wheels when the current wheel-cable friction coefficient exceeds a preset friction coefficient threshold. For example, when this occurs, the base positive pressure compensation is multiplied by the preset constraint correction coefficient to obtain a first corrected compensation for the robot's drive wheels. This is used to adjust the positive pressure compensation to better meet actual requirements. Assuming that cable coating does not require extremely high precision and the wheel-cable friction coefficient fluctuates relatively little, the preset constraint correction coefficient may be between 0.8 and 0.9. For example, a value of 0.85 is used. For special cable coating applications requiring high precision, with extremely strict requirements on coating thickness and robot walking stability, the preset constraint correction coefficient may be between 0.6 and 0.7. For example, a value of 0.65 is used. In certain complex coating environments, where the wheel-cable friction coefficient fluctuates frequently and significantly, the preset constraint correction coefficient may be between 0.9 and 1. For example, a value of 0.95 is used.

[0110] In this embodiment, the first correction compensation amount of the robot driving wheel is advanced compensated to obtain the second correction compensation amount of the robot driving wheel: Considering the pressure sensor's response delay (e.g., 20ms), to more accurately compensate for the positive pressure on the robot's drive wheels, a lead compensation is applied based on the first correction compensation. Specifically, the rate of change of the first correction compensation (the amount of change per unit time) is calculated, multiplied by 0.2, and then multiplied by the pressure sensor's response delay (20ms) to obtain a lead compensation value. This lead compensation value is added to the first correction compensation value to obtain the second correction compensation value for the robot's drive wheels. This allows the positive pressure compensation value to be adjusted in advance to accommodate any pressure change delays that may occur during the actual coating process.

[0111] In this embodiment, the preset safety constraint ratio is a pre-set proportional value used to limit the amount of positive pressure compensation on the robot's drive wheels to prevent excessive cable deformation due to excessive pressure. For example, the preset safety constraint ratio may be set to 0.3, which means that the robot's drive wheel positive pressure compensation cannot exceed 30% of the nominal positive pressure.

[0112] In this embodiment, the second correction compensation amount of the robot driving wheel is corrected for the third time based on the nominal positive pressure and the preset safety constraint ratio to obtain the optimal compensation amount of the robot driving wheel: The robot's drive wheel's second correction compensation is determined to determine whether it exceeds the nominal positive pressure multiplied by the preset safety constraint ratio. If so, the second correction compensation is adjusted to the nominal positive pressure multiplied by the preset safety constraint ratio. This is the third correction of the second correction compensation. This result is the optimal compensation for the robot's drive wheel, ensuring coating quality without causing adverse effects such as excessive compression on the cable.

[0113] like Figure 2 As shown, the present invention provides an embodiment of a walking optimization system for a road-type cable coating robot, comprising: A multi-dimensional sensing module is used to reconstruct the 3D profile of the cable surface in real time and extract the real-time cable radius and cross-sectional roundness. It also collects spray pressure data, cable surface temperature distribution, and the normal component of the real-time wheel-cable contact force and tangential friction force. The thickness prediction module is used to obtain the coating thickness prediction value at a future time based on the real-time cable radius, cable cross-section roundness, spray pressure record data, cable surface temperature field distribution, the normal component and tangential friction force of the real-time wheel-cable contact force, and the real-time walking speed of the robot; a compensation calculation module, configured to calculate a walking speed adjustment amount and / or a robot driving wheel positive pressure compensation amount when a deviation between a coating thickness prediction value at a future moment and a corresponding target thickness is greater than a preset deviation threshold; The optimization control module is used to perform walking optimization control on the robot based on the walking speed adjustment amount and the positive pressure compensation amount of the robot driving wheel to obtain the walking optimization control result.

[0114] Obviously, those skilled in the art may make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the present invention and its equivalents, the present invention is intended to include these modifications and variations.

Claims

1. A walking optimization method for a cable coating robot, characterized in that: include: Reconstruct the 3D profile of the cable surface in real time and extract the real-time cable radius and cable cross-sectional roundness. Simultaneously collect spray pressure record data, cable surface temperature field distribution, and the normal component of the real-time wheel-cable contact force and tangential friction force. Based on the real-time cable radius, cable cross-section roundness, spray pressure record data, cable surface temperature field distribution, the normal component of the real-time wheel-cable contact force and the tangential friction force, as well as the real-time walking speed of the robot, the coating thickness prediction value at the future moment is obtained; When the deviation between the coating thickness prediction value at the future moment and the corresponding target thickness is greater than a preset deviation threshold, the walking speed adjustment amount and / or the robot driving wheel positive pressure compensation amount are calculated; The robot's walking optimization control is performed based on the walking speed adjustment amount and the positive pressure compensation amount of the robot's driving wheels to obtain the walking optimization control result.

2. The walking optimization method for a vehicle-type cable coating robot according to claim 1, characterized in that: Real-time reconstruction of the cable surface 3D profile and extraction of the real-time cable radius and cable cross-sectional roundness, including: The three-dimensional profile of the cable surface is reconstructed based on multiple groups of laser profile sensors arranged around the robot's walking mechanism and a triangulation ranging method; Extract real-time cable radius and cable cross-section roundness based on the 3D profile of the cable surface.

3. The walking optimization method for a vehicle-type cable coating robot according to claim 1, characterized in that: Collect spray pressure record data, cable surface temperature field distribution, and real-time wheel-cable contact force normal component and tangential friction force, including: The high-frequency pressure sensor and infrared temperature sensor set at the outlet of the robot spray gun synchronously collect spray pressure recording data and cable surface temperature field distribution; The normal component of the wheel-cable contact force and the tangential friction force are collected in real time based on the six-axis force sensor integrated at the driving wheel of the robot.

4. The walking optimization method for a vehicle-type cable coating robot according to claim 1, characterized in that: Based on the real-time cable radius, cable cross-section roundness, spray pressure record data, cable surface temperature field distribution, the normal component of the real-time wheel-cable contact force and the tangential friction force, and the real-time walking speed of the robot, the coating thickness prediction value at the future moment is obtained, including: Perform trend prediction on the collected real-time cable radius, cable cross-sectional roundness, spray pressure record data, cable surface temperature field distribution, and the normal component of the real-time wheel-cable contact force and the tangential friction force, and obtain the predicted values ​​of the real-time cable radius, cable cross-sectional roundness, cable surface temperature field distribution, the normal component of the real-time wheel-cable contact force and the tangential friction force at future times, as well as the predicted values ​​of the spray pressure record data to future times; Determine the average spraying pressure in the latest historical period based on the future time and the instantaneous pressure fluctuation amplitude at the future time based on the predicted value of the spraying pressure record data to the future time; Calculate the base coating thickness value at the future moment based on the average spraying pressure in the latest historical period based on the future moment, the instantaneous pressure fluctuation amplitude at the future moment, the predicted value of the real-time cable radius at the future moment, the predicted value of the linear spraying speed of the standard paint at the future moment, and the current walking speed of the robot; Determining a roundness deviation rate at a future time based on a predicted value of the cable cross-section roundness at a future time, and determining a first correction coefficient of a coating thickness base value based on the roundness deviation rate at the future time; Determining a local temperature gradient factor at a future time based on a predicted value of the cable surface temperature field distribution at a future time, and determining a second correction coefficient of the coating thickness base value based on the local temperature gradient factor at the future time; Determining a friction coefficient at a future time based on predicted values ​​of the normal component of the real-time wheel-cable contact force and the tangential friction force at a future time, and determining a third correction coefficient for the base value of the coating thickness based on the friction coefficient at the future time; The coating thickness prediction value at the future moment is calculated based on the coating thickness base value at the future moment and the corresponding first correction coefficient, second correction coefficient, and third correction coefficient.

5. The walking optimization method for a vehicle-type cable coating robot according to claim 1, characterized in that: The roundness deviation rate at a future time is determined based on the predicted value of the cable cross-section roundness at a future time, including: Based on the three-dimensional profile of the cable surface, the cable cross-section circumferential center distance distribution vector at the future moment is predicted, and the maximum center distance, minimum center distance, average center distance, center distance standard deviation and roundness shape factor of the cable cross-section at the future moment are extracted from the cable cross-section circumferential center distance distribution vector; Correcting the circumferential center distance distribution vector of the cable cross section at the future moment based on the maximum center distance, minimum center distance, average center distance, center distance standard deviation, and roundness shape factor of the cable cross section at the future moment to obtain a corrected circumferential center distance distribution vector of the cable cross section at the future moment; Calculate the roundness basic deviation rate at the future moment based on the cable cross-section circumferential center distance correction distribution vector at the future moment; Performing circumferential partitioning based on the cable cross section at the future moment to obtain a plurality of circumferential partitions, and calculating an average center distance deviation of each circumferential partition based on the circumferential center distance correction distribution vector of the cable cross section at the future moment; Based on the distribution positions of all circumferential partitions whose average center distance deviations are greater than the preset center distance deviation threshold and the corresponding over-limit values ​​of the average center distance deviations, the basic roundness deviation rate at future moments is corrected to obtain the roundness deviation rate at future moments.

6. The walking optimization method for a vehicle-type cable coating robot according to claim 4, characterized in that: The local temperature gradient factor at the future moment is determined based on the predicted value of the cable surface temperature field distribution at the future moment, including: A 3D cable model is established based on the 3D contour of the cable surface, and the 3D cable model is spatially divided to obtain a cable space grid; Based on the predicted value of the cable surface temperature field distribution at a future time, the normalized value of the temperature gradient amplitude, the cosine value of the angle between the temperature gradient direction angle and the spraying direction, and the exponential decay term of the gradient change rate of each cable space grid are calculated. The normalized value of the temperature gradient amplitude, the cosine value of the angle between the temperature gradient direction angle and the spraying direction, and the exponential decay term of the gradient change rate of each cable space grid are weightedly summed to obtain the local temperature gradient factor of each cable space grid; The local temperature gradient factor of all cable space grids in the coating section at the future moment is weighted averaged to obtain the local temperature gradient factor at the future moment.

7. The walking optimization method for a vehicle-type cable coating robot according to claim 1, characterized in that: When the deviation between the predicted coating thickness at a future moment and the corresponding target thickness is greater than a preset deviation threshold, the walking speed adjustment amount and / or the robot driving wheel positive pressure compensation amount are calculated, including: Determine the coating position of the robot at a future time based on the current walking speed and current coating position of the robot, and obtain the target thickness of the coating position of the robot at the future time; When the deviation between the coating thickness prediction value at the future moment and the target thickness at the coating position of the robot at the future moment is greater than the preset deviation threshold, the basic walking speed compensation amount is calculated based on the speed adjustment coefficient, the deviation between the coating thickness prediction value at the future moment and the target thickness at the coating position of the robot at the future moment, the target thickness, the current walking speed, the standard walking speed, the current cable radius, and the nominal cable radius, and the basic walking speed compensation amount is dynamically corrected based on the dynamic correction rule to obtain the optimal walking speed compensation amount; At the same time, the positive pressure basic compensation of the robot driving wheel is calculated based on the positive pressure adjustment coefficient, the deviation between the coating thickness prediction value at the future moment and the target thickness at the coating position of the robot at the future moment, the target thickness, the current wheel-cable friction coefficient, the optimal friction coefficient, the nominal positive pressure, the current cable radius and the nominal cable radius, and the positive pressure basic compensation of the robot driving wheel is corrected by safety constraints to obtain the optimal compensation of the robot driving wheel.

8. The walking optimization method for a vehicle-type cable coating robot according to claim 7, characterized in that: Dynamically correct the basic walking speed compensation based on the dynamic correction rules to obtain the optimal walking speed compensation, including: If the instantaneous pressure fluctuation frequency of the paint is greater than the preset fluctuation frequency threshold, a first correction coefficient of the basic walking speed compensation is determined based on the instantaneous pressure fluctuation frequency of the paint and the correlation coefficient between the instantaneous pressure fluctuation frequency of the paint and the speed compensation; If the cable cross-sectional roundness deviation rate is greater than a preset deviation rate threshold, a second correction coefficient of the basic walking speed compensation amount is determined based on the cable cross-sectional roundness deviation rate and the cable cross-sectional roundness deviation rate-speed compensation amount correlation coefficient; The basic walking speed compensation amount is dynamically corrected based on the first correction coefficient and the second correction coefficient of the basic walking speed compensation amount to obtain the optimal walking speed compensation amount.

9. The walking optimization method for a vehicle-type cable coating robot according to claim 7, characterized in that: The safety constraint correction is performed on the positive pressure basic compensation of the robot's driving wheels to obtain the optimal compensation of the robot's driving wheels, including: If the front wheel-cable friction coefficient is greater than the preset friction coefficient threshold, the product of the normal pressure base compensation of the robot driving wheel and the preset constraint correction coefficient is used as the first correction compensation of the robot driving wheel; Performing advance compensation on the first correction compensation amount of the robot driving wheel to obtain a second correction compensation amount of the robot driving wheel; The second correction compensation amount of the robot driving wheel is corrected for the third time based on the nominal positive pressure and the preset safety constraint ratio to obtain the optimal compensation amount of the robot driving wheel.

10. The walking optimization system for the cable coating robot is characterized by: include: A multi-dimensional sensing module is used to reconstruct the 3D profile of the cable surface in real time and extract the real-time cable radius and cross-sectional roundness. It also collects spray pressure data, cable surface temperature distribution, and the normal component of the real-time wheel-cable contact force and tangential friction force. The thickness prediction module is used to obtain the coating thickness prediction value at a future time based on the real-time cable radius, cable cross-section roundness, spray pressure record data, cable surface temperature field distribution, the normal component and tangential friction force of the real-time wheel-cable contact force, and the real-time walking speed of the robot; a compensation calculation module, configured to calculate a walking speed adjustment amount and / or a robot driving wheel positive pressure compensation amount when a deviation between a coating thickness prediction value at a future moment and a corresponding target thickness is greater than a preset deviation threshold; The optimization control module is used to perform walking optimization control on the robot based on the walking speed adjustment amount and the positive pressure compensation amount of the robot driving wheel to obtain the walking optimization control result.

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