Walking optimization method and system for a cable coating robot

Abstract

The application relates to the technical field of robot control, and particularly discloses a walking optimization method and system for a cable coating robot of a driving type, which comprises the following steps: reconstructing a three-dimensional profile of a cable surface in real time, extracting a real-time cable radius and a cable cross-section roundness, collecting spraying pressure record data, a cable surface temperature field distribution, a normal component of a real-time wheel-cable contact force and a tangential friction force; based on the above parameters and a real-time walking speed of the robot, a coating thickness prediction value at a future time is obtained; when a deviation between the coating thickness prediction value at the future time and a corresponding target thickness is greater than a preset deviation threshold, a walking speed adjustment amount and / or a robot driving wheel normal pressure compensation amount are calculated; the robot is controlled in a walking optimization mode based on the walking speed adjustment amount and the robot driving wheel normal pressure compensation amount, and a walking optimization control result is obtained; and the coating thickness of the cable is ensured to be closer to a target value, and the coating quality and the adaptability of the robot walking are improved.

Description

Technical Field

[0001] This invention relates to the field of robot control technology, and in particular to a method and system for optimizing the movement of a line-coating robot. Background Technology

[0002] In the field of cable manufacturing and maintenance, cable coating processes play a crucial role in improving cable performance and lifespan. Oscillating cable coating robots offer automated and efficient solutions, ensuring coating quality while significantly improving production efficiency. As industrial production demands increasingly higher cable quality, this field has imposed more stringent standards on aspects such as coating uniformity and thickness consistency. With the rapid development of intelligent manufacturing technologies, industrial production is placing ever-increasing demands on the intelligence level of automated equipment. The walking optimization technology for oscillating cable coating robots, leveraging advanced sensor technology and intelligent algorithms, can perceive various parameters during the working process in real time and perform precise calculations and adjustments. This intelligent walking optimization method and system not only has broad application prospects in the cable manufacturing industry but can also provide a reference for other fields involving surface coating processes, promoting the entire manufacturing industry towards intelligent and refined development.

[0003] However, existing robotic cable coating technology for mobile vehicles lacks complete information about the coating process, leading to inaccurate predictions of the coating effect. It also fails to accurately calculate the walking speed adjustment and / or the positive pressure compensation of the robot's drive wheels, and thus cannot effectively optimize and control the robot's movement to ensure the quality and thickness consistency of the cable coating.

[0004] Therefore, this invention proposes a walking optimization method and system for a line-coating robot for line-mounted vehicles. Summary of the Invention

[0005] This invention provides a walking optimization method and system for a cable coating robot designed for vehicle-mounted applications. It obtains real-time cable radius and cross-sectional roundness by reconstructing the three-dimensional contour of the cable surface in real time, and collects spray pressure data, cable surface temperature field distribution, and real-time wheel-cable contact force components. Combined with the robot's real-time walking speed, it predicts the coating thickness at future moments. When the deviation between the predicted value and the target thickness exceeds a preset threshold, it calculates the walking speed adjustment and / or the positive pressure compensation for the robot's drive wheels. Finally, based on these adjustments, it optimizes the robot's walking control to ensure the cable coating thickness is closer to the target value. By comprehensively considering these complex factors, it achieves precise control of the robot's movement, thereby improving the quality and stability of cable coating. This enhances coating quality and the stability and adaptability of robot movement.

[0006] This invention provides a method for optimizing the movement of a line-coating robot, comprising:

[0007] The three-dimensional contour of the cable surface is reconstructed in real time, and the real-time cable radius and cable cross-sectional roundness are extracted. At the same time, the spraying pressure record data, the temperature field distribution of the cable surface, and the normal component and tangential friction force of the real-time wheel-cable contact force are collected.

[0008] Based on real-time cable radius, cable cross-section roundness, spraying pressure recording data, cable surface temperature field distribution, real-time wheel-cable contact force normal component and tangential friction force, and robot real-time walking speed, the predicted value of coating thickness at future moments is obtained.

[0009] 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 and / or the positive pressure compensation of the robot drive wheel are calculated.

[0010] The robot's walking optimization control is performed based on the walking speed adjustment and the positive pressure compensation of the robot's drive wheels to obtain the walking optimization control results.

[0011] Preferably, the real-time reconstruction of the three-dimensional contour of the cable surface and the extraction of the real-time cable radius and cable cross-sectional roundness include:

[0012] The three-dimensional profile of the cable surface is reconstructed based on multiple sets of laser profile sensors arranged circumferentially in the robot's walking mechanism and triangulation method.

[0013] Real-time cable radius and cable cross-sectional roundness are extracted based on the three-dimensional contour of the cable surface.

[0014] Preferably, the acquisition of spraying pressure recording data, cable surface temperature field distribution, and real-time wheel-cable contact force normal component and tangential friction force includes:

[0015] The high-frequency pressure sensor and infrared temperature sensor installed at the outlet of the robot spray gun synchronously collect spray pressure recording data and cable surface temperature field distribution.

[0016] The robot uses a six-axis force sensor integrated at its drive wheel to collect the normal component of the wheel-cable contact force and the tangential friction force in real time.

[0017] Preferably, based on real-time cable radius, cable cross-sectional roundness, spray pressure recording data, cable surface temperature field distribution, the normal component and tangential friction of real-time wheel-cable contact force, and the robot's real-time walking speed, the predicted coating thickness for future moments is obtained, including:

[0018] Trend prediction is performed on the collected real-time cable radius, cable cross-section roundness, spraying pressure recording data, cable surface temperature field distribution, and the normal component and tangential friction of the real-time wheel-cable contact force. The predicted values ​​of the real-time cable radius, cable cross-section roundness, cable surface temperature field distribution, and the normal component and tangential friction of the real-time wheel-cable contact force at future times are obtained, as well as the predicted values ​​of the spraying pressure recording data at future times.

[0019] The average spraying pressure and the instantaneous pressure fluctuation amplitude at the future time are determined based on the spraying pressure record data and the predicted value to the future time.

[0020] The base value of the coating thickness in the future is calculated based on the average spraying pressure in the latest historical period based on the future time, the instantaneous pressure fluctuation amplitude in the future time, the predicted value of the real-time cable radius in the future time, the predicted value of the linear spraying speed of the standard coating in the future time, and the robot's current walking speed.

[0021] The roundness deviation rate at future times is determined based on the predicted value of the cable cross-section roundness at future times, and the first correction coefficient of the base value of the coating thickness is determined based on the roundness deviation rate at future times.

[0022] The local temperature gradient factor for the future time is determined based on the predicted value of the temperature field distribution on the cable surface at the future time, and the second correction coefficient for the base value of the coating thickness is determined based on the local temperature gradient factor for the future time.

[0023] The friction coefficient at future moments is determined based on the predicted values ​​of the normal component of the real-time wheel-cable contact force and the tangential friction force at future moments, and a third correction coefficient for the base value of the coating thickness is determined based on the friction coefficient at future moments.

[0024] The predicted coating thickness for future moments is calculated based on the base value of the coating thickness at future moments and the corresponding first correction factor, second correction factor, and third correction factor.

[0025] Preferably, determining the roundness deviation rate at future times based on the predicted value of the cable cross-section roundness at future times includes:

[0026] Based on the three-dimensional profile of the cable surface, the distribution vector of the circumferential center distance of the cable cross section at future times is predicted, and the maximum center distance, minimum center distance, average center distance, standard deviation of center distance and roundness shape factor of the cable cross section at future times are extracted from the distribution vector of the circumferential center distance of the cable cross section.

[0027] The distribution vector of the circumferential center distance of the cable cross-section at future times is corrected based on the maximum center distance, minimum center distance, average center distance, standard deviation of center distance, and roundness shape factor of the cable cross-section at future times, so as to obtain the corrected distribution vector of the circumferential center distance of the cable cross-section at future times.

[0028] The roundness baseline deviation rate at future moments is calculated based on the corrected distribution vector of the circumferential center distance of the cable cross-section at future moments.

[0029] Multiple circumferential partitions are obtained by dividing the cable cross-section at future time, and the average center distance deviation of each circumferential partition is calculated based on the distribution vector of the circumferential center distance of the cable cross-section at future time.

[0030] Based on the distribution location of all circumferential partitions whose average center distance deviation is greater than the preset center distance deviation threshold and the corresponding excess value of the average center distance deviation, the roundness basic deviation rate at future time is corrected to obtain the roundness deviation rate at future time.

[0031] Preferably, the local temperature gradient factor for future times is determined based on the predicted value of the cable surface temperature field distribution at future times, including:

[0032] A three-dimensional cable model is established based on the three-dimensional contour of the cable surface, and the three-dimensional cable model is spatially divided to obtain the cable spatial mesh;

[0033] Based on the predicted values ​​of the temperature field distribution on the cable surface at future times, the normalized value of the temperature gradient amplitude, the cosine 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 spatial grid are calculated. The local temperature gradient factor of each cable spatial grid is obtained by weighted summation of the normalized value of the temperature gradient amplitude, the cosine of the angle between the temperature gradient direction angle and the spraying direction, and the exponential decay term of the gradient change rate.

[0034] The local temperature gradient factor at future time is obtained by weighted averaging of the local temperature gradient factors of all cable spatial grids within the coated section.

[0035] Preferably, when the deviation between the predicted coating thickness at a future time and the corresponding target thickness is greater than a preset deviation threshold, the walking speed adjustment and / or the positive pressure compensation of the robot drive wheels are calculated, including:

[0036] The robot's future coating position is determined based on its current walking speed and current coating position, and the target thickness at the robot's future coating position is obtained.

[0037] When the deviation between the predicted coating thickness at a future time and the target thickness at the robot's coating position at a future time is greater than a preset deviation threshold, the basic compensation amount of the walking speed is calculated based on the speed adjustment coefficient, the deviation between the predicted coating thickness at a future time and the target thickness at the robot's coating position at a future time, the target thickness, the current walking speed, the standard walking speed, the current cable radius, and the nominal cable radius. The basic compensation amount of the walking speed is then dynamically corrected based on the dynamic correction rule to obtain the optimal compensation amount of the walking speed.

[0038] Simultaneously, based on the positive pressure adjustment coefficient, the deviation between the predicted coating thickness at future moments and the target thickness at the robot's coating position at future moments, 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, the basic compensation amount of the positive pressure of the robot's drive wheel is calculated, and the basic compensation amount of the positive pressure of the robot's drive wheel is corrected by safety constraints to obtain the optimal compensation amount of the robot's drive wheel.

[0039] Preferably, the optimal walking speed compensation is obtained by dynamically correcting the basic compensation amount based on dynamic correction rules, including:

[0040] If the instantaneous pressure fluctuation frequency of the coating is greater than the preset fluctuation frequency threshold, then the first correction coefficient of the basic compensation amount of the walking speed 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 amount.

[0041] If the cable cross-section roundness deviation rate is greater than the preset deviation rate threshold, then the second correction coefficient of the walking speed basic compensation amount is determined based on the cable cross-section roundness deviation rate and the correlation coefficient between the cable cross-section roundness deviation rate and the speed compensation amount.

[0042] The basic compensation amount of walking speed is dynamically corrected based on the first and second correction coefficients to obtain the optimal compensation amount of walking speed.

[0043] Preferably, the basic compensation amount of the normal force on the robot drive wheel is corrected with safety constraints to obtain the optimal compensation amount for the robot drive wheel, including:

[0044] If the friction coefficient between the current wheel and the cable is greater than the preset friction coefficient threshold, then the product of the basic compensation amount of the normal pressure of the robot drive wheel and the preset constraint correction coefficient will be used as the first correction compensation amount of the robot drive wheel.

[0045] The first correction compensation amount of the robot drive wheel is advanced to obtain the second correction compensation amount of the robot drive wheel;

[0046] The second correction compensation amount of the robot drive wheel is corrected a third time based on the nominal positive pressure and the preset safety constraint ratio to obtain the optimal compensation amount of the robot drive wheel.

[0047] This invention provides a walking optimization system for a line-coating robot, comprising:

[0048] The multi-dimensional sensing module is used to reconstruct the three-dimensional contour of the cable surface in real time and extract the real-time cable radius and the roundness of the cable cross section. At the same time, it collects spraying pressure recording data, cable surface temperature field distribution, and the normal component and tangential friction force of the real-time wheel-cable contact force.

[0049] The thickness prediction module is used to obtain the predicted coating thickness at future moments based on real-time cable radius, cable cross-section roundness, spraying pressure recording data, cable surface temperature field distribution, the normal component and tangential friction of real-time wheel-cable contact force, and the robot's real-time walking speed.

[0050] The compensation calculation module is used to calculate the walking speed adjustment and / or the positive pressure compensation of the robot drive wheel when the deviation between the predicted coating thickness at a future time and the corresponding target thickness is greater than a preset deviation threshold.

[0051] The optimization control module is used to optimize the robot's walking control based on the walking speed adjustment and the positive pressure compensation of the robot's drive wheels, and obtain the walking optimization control results.

[0052] The beneficial effects of this invention compared to existing technologies are as follows: By reconstructing the three-dimensional contour of the cable surface in real time to obtain the real-time cable radius and cross-sectional roundness, and collecting spraying pressure data, cable surface temperature field distribution, and real-time wheel-cable contact force components, the coating thickness at future moments is predicted 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 walking speed adjustment and / or the positive pressure compensation of the robot's drive wheels are calculated. Finally, based on these adjustments, 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 movement can be achieved, thereby improving the quality and stability of cable coating. This enhances coating quality and the stability and adaptability of robot movement.

[0053] Other features and advantages of the invention will be set forth in the description which follows, and will be apparent in part from the description, or may be learned by practicing the invention. The objects and other advantages of the invention may be realized and obtained by means of the structures particularly pointed out in this application.

[0054] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. Attached Figure Description

[0055] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used in conjunction with embodiments of the invention to explain the invention and do not constitute a limitation thereof. In the drawings:

[0056] Figure 1 This is a flowchart of the walking optimization method for a line-coating robot for vehicle models according to an embodiment of the present invention;

[0057] Figure 2 This is a schematic diagram of a walking optimization system for a line-coating robot for vehicle-type cables in an embodiment of the present invention. Detailed Implementation

[0058] The preferred embodiments of the present invention will be described below with reference to the accompanying drawings. It should be understood that the preferred embodiments described herein are for illustration and explanation only and are not intended to limit the present invention.

[0059] like Figure 1 As shown, this invention provides an implementation method for a walking optimization method for a line-coating robot, comprising:

[0060] The three-dimensional contour of the cable surface is reconstructed in real time, and the real-time cable radius and cable cross-sectional roundness are extracted. At the same time, the spraying pressure record data, the temperature field distribution of the cable surface, and the normal component and tangential friction force of the real-time wheel-cable contact force are collected.

[0061] Based on real-time cable radius, cable cross-section roundness, spraying pressure recording data, cable surface temperature field distribution, real-time wheel-cable contact force normal component and tangential friction force, and robot real-time walking speed, the predicted value of coating thickness at future moments is obtained.

[0062] 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 and / or the positive pressure compensation of the robot drive wheel are calculated.

[0063] The robot's walking optimization control is performed based on the walking speed adjustment and the positive pressure compensation of the robot's drive wheels to obtain the walking optimization control results.

[0064] 3D profile of cable surface: The shape of the cable surface in three-dimensional space reconstructed using laser profile sensors arranged circumferentially on the robot's walking mechanism and triangulation.

[0065] In this embodiment, the real-time cable radius is the cable radius data extracted from the real-time reconstructed three-dimensional profile of the cable surface.

[0066] In this embodiment, the cable cross-section roundness is a measure of how close the cable cross-section is to a circle, calculated based on the three-dimensional contour data of the cable surface.

[0067] In this embodiment, the spraying pressure recording data is: paint spraying pressure data collected by the high-frequency pressure sensor at the outlet of the robot spray gun.

[0068] In this embodiment, the temperature field distribution on the cable surface is: the temperature distribution data of the cable surface collected by an infrared temperature sensor.

[0069] In this embodiment, the normal component and tangential friction force of the real-time wheel-cable contact force are collected by a six-axis force sensor at the robot drive wheel, referring to the forces perpendicular and tangential in the direction when the wheel and cable are in contact, respectively.

[0070] In this embodiment, the robot's real-time walking speed is the instantaneous walking speed of the robot during the cable coating process, which can be obtained through a speed sensor.

[0071] In this embodiment, "future moment" refers to a point in time relative to the current data acquisition time, used to predict parameters such as coating thickness.

[0072] In this embodiment, the predicted coating thickness at a future moment is the calculated and predicted value of the cable coating thickness at a certain future moment.

[0073] In this embodiment, the target thickness is the pre-defined thickness standard that the cable coating is expected to achieve.

[0074] In this embodiment, the preset deviation threshold is a pre-set boundary value used to determine whether the deviation between the predicted coating thickness and the target thickness at a future time is too large.

[0075] In this embodiment, the walking speed adjustment amount is the amount that needs to be adjusted when the predicted coating thickness at a future time deviates from the target thickness by a preset threshold.

[0076] In this embodiment, the positive pressure compensation amount of the robot drive wheel is the amount of positive pressure compensation required for the robot drive wheel when the above deviation exceeds the threshold.

[0077] In this embodiment, the robot's walking is optimized based on the walking speed adjustment amount and the positive pressure compensation amount of the robot drive wheel to obtain the walking optimization control result: the calculated walking speed adjustment amount and the positive pressure compensation amount of the robot drive wheel are used as control parameters to adjust the robot's walking, and finally obtain the control effect of making the coating closer to the target thickness.

[0078] To extract real-time cable radius and cable cross-sectional roundness to obtain key cable geometric parameters, a method is proposed that reconstructs the 3D contour of the cable surface in real time and extracts the real-time cable radius and cable cross-sectional roundness, including:

[0079] The three-dimensional profile of the cable surface is reconstructed based on multiple sets of laser profile sensors arranged circumferentially in the robot's walking mechanism and triangulation method.

[0080] Real-time cable radius and cable cross-sectional roundness are extracted based on the three-dimensional contour of the cable surface.

[0081] In this embodiment, the three-dimensional profile of the cable surface is reconstructed based on multiple sets of laser profile sensors arranged circumferentially on the robot walking mechanism and the triangulation method: multiple sets of laser profile sensors are set in the circumferential direction of the robot walking mechanism, and the position of each point on the cable surface is measured by laser emission and reflection using the principle of triangulation, thereby constructing the shape of the cable surface in three-dimensional space.

[0082] In this embodiment, the real-time cable radius and cable cross-section roundness are extracted based on the three-dimensional contour of the cable surface: the center of the cable cross-section is determined based on the reconstructed three-dimensional contour data of the cable surface, and the distance from the center to the surface is calculated to obtain the real-time cable radius; and the cable cross-section roundness is obtained by analyzing the radius-related parameters in the contour data.

[0083] To achieve accurate acquisition of multiple parameters, a method is proposed that collect data on spraying pressure, cable surface temperature field distribution, and the normal component and tangential friction of the real-time wheel-cable contact force, including:

[0084] The high-frequency pressure sensor and infrared temperature sensor installed at the outlet of the robot spray gun synchronously collect spray pressure recording data and cable surface temperature field distribution.

[0085] The robot uses a six-axis force sensor integrated at its drive wheel to collect the normal component of the wheel-cable contact force and the tangential friction force in real time.

[0086] In this embodiment, a high-frequency pressure sensor and an infrared temperature sensor are installed at the exit position of the robot's spray gun. The high-frequency pressure sensor can quickly and accurately measure the pressure of the paint being sprayed, while the infrared temperature sensor can sense the surface temperature of the cable. These two sensors work simultaneously to collect spray pressure data and cable surface temperature distribution data.

[0087] In this embodiment, a six-axis force sensor integrated at the robot's drive wheel is used to collect the normal component and tangential friction force of the wheel-cable contact force in real time. The six-axis force sensor is integrated at the point where the robot contacts the cable and provides driving force. This sensor can measure the force component in the vertical direction (normal direction) and the friction force 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 and tangential friction force of the wheel-cable contact force.

[0088] To calculate the predicted coating thickness at future moments and provide a basis for coating thickness control, a method is proposed that uses real-time cable radius, cable cross-sectional roundness, spray pressure recording data, cable surface temperature field distribution, the normal component and tangential friction of real-time wheel-cable contact force, and the robot's real-time walking speed to obtain the predicted coating thickness at future moments, including:

[0089] Trend prediction is performed on the collected real-time cable radius, cable cross-section roundness, spraying pressure recording data, cable surface temperature field distribution, and the normal component and tangential friction of the real-time wheel-cable contact force. The predicted values ​​of the real-time cable radius, cable cross-section roundness, cable surface temperature field distribution, and the normal component and tangential friction of the real-time wheel-cable contact force at future times are obtained, as well as the predicted values ​​of the spraying pressure recording data at future times.

[0090] The average spraying pressure and the instantaneous pressure fluctuation amplitude at the future time are determined based on the spraying pressure record data and the predicted value to the future time.

[0091] The base value of the coating thickness in the future is calculated based on the average spraying pressure in the latest historical period based on the future time, the instantaneous pressure fluctuation amplitude in the future time, the predicted value of the real-time cable radius in the future time, the predicted value of the linear spraying speed of the standard coating in the future time, and the robot's current walking speed.

[0092] The roundness deviation rate at future times is determined based on the predicted value of the cable cross-section roundness at future times, and the first correction coefficient of the base value of the coating thickness is determined based on the roundness deviation rate at future times.

[0093] The local temperature gradient factor for the future time is determined based on the predicted value of the temperature field distribution on the cable surface at the future time, and the second correction coefficient for the base value of the coating thickness is determined based on the local temperature gradient factor for the future time.

[0094] The friction coefficient at future moments is determined based on the predicted values ​​of the normal component of the real-time wheel-cable contact force and the tangential friction force at future moments, and a third correction coefficient for the base value of the coating thickness is determined based on the friction coefficient at future moments.

[0095] The predicted coating thickness for future moments is calculated based on the base value of the coating thickness at future moments and the corresponding first correction factor, second correction factor, and third correction factor.

[0096] In this embodiment, trend prediction is performed on the collected real-time cable radius, cable cross-sectional roundness, spraying pressure recording data, cable surface temperature field distribution, and the normal component and tangential friction of the real-time wheel-cable contact 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 and tangential friction of the real-time wheel-cable contact force at future times, as well as the predicted values ​​of the spraying pressure recording data to future times.

[0097] For real-time cable radius, based on the collected data points, we analyze its changes over past time periods, such as whether it gradually increases, decreases, or remains relatively stable. Then, using appropriate prediction algorithms, such as time series analysis or regression models in machine learning, we infer its value at a specific future moment. This value is the predicted value of the real-time cable radius at the future moment.

[0098] The same applies to the roundness of cable cross-sections. Based on existing roundness data, we study its changing trends and then use appropriate prediction methods to obtain the predicted value of cable cross-section roundness at future times.

[0099] To determine the surface temperature distribution of cables, we integrate the temperature data collected at different locations and times, observe the evolution of the temperature distribution over time, and use prediction methods specifically designed for spatial and temporal series data (e.g., spatiotemporal autoregressive moving average model, deep learning-based long short-term memory network-convolutional neural network, Kriging interpolation combined with time series prediction, etc.) to predict the future distribution of the surface temperature of cables at various locations, i.e., the predicted value of the surface temperature distribution of cables at future times.

[0100] The normal component and tangential friction of the real-time wheel-cable contact force are also based on existing measurement data. Their variation patterns with factors such as robot movement are analyzed, and the predicted values ​​of the normal component and tangential friction at future moments are obtained by using suitable force data prediction methods (e.g., Kalman filtering, support vector regression, grey prediction model, etc.).

[0101] The spraying pressure record data is analyzed based on the recorded pressure value sequence. The pressure fluctuation pattern over time is analyzed, and time series prediction models are used to predict the pressure changes from the present to the future, thereby obtaining the predicted value of the spraying pressure record data to the future.

[0102] In this embodiment, the average spraying pressure within the latest historical period based on the spraying pressure recording data up to the predicted value at a future time and the instantaneous pressure fluctuation amplitude at the future time are determined:

[0103] First, define a specific future moment. Add up all the predicted spraying pressure data from a certain point in the past up to this future moment, then divide by the total number of data points. The result is the average spraying pressure for the latest historical period based on this future moment.

[0104] To determine the instantaneous pressure fluctuation amplitude at a future moment, select an extremely short time period, such as a few milliseconds, near that future moment. Within this extremely short time period, find the maximum and minimum values ​​of the spraying pressure. Subtract the minimum value from the maximum value; the difference obtained is the instantaneous pressure fluctuation amplitude at the future moment.

[0105] In this embodiment, the base value of the coating thickness in the future is calculated based on the average spraying pressure in the latest historical period with a future time as the reference, the instantaneous pressure fluctuation amplitude in the future, the predicted value of the real-time cable radius in the future, the predicted value of the linear spraying speed of the standard coating in the future, and the robot's current walking speed.

[0106] The base value for coating thickness is calculated using the following formula: Base coating thickness equals the pressure-thickness conversion factor multiplied by (average spraying pressure plus 0.7 multiplied by the instantaneous pressure fluctuation amplitude) multiplied by the square root of the predicted cable radius, then divided by (the robot's current walking speed multiplied by the predicted linear spraying speed of the standard paint). The pressure-thickness conversion factor is obtained through experimental calibration; this factor varies depending on the type of paint. For example, the factor for epoxy paint is 2.3 × 10⁻⁻⁻⁶. 5 (mm・s / kg), the coefficient for polyurethane coatings is 1.8 × 10⁻ 5 (mm・s / kg).

[0107] In this embodiment, the roundness deviation rate at future times is determined based on the predicted value of the cable cross-section roundness at future times, and a first correction coefficient for the base value of the coating thickness is determined based on the roundness deviation rate at future times.

[0108] Determine the roundness deviation rate: Based on the three-dimensional profile of the cable surface, predict the distance distribution from each point on the cable cross-section to the center at future times. Identify the maximum, minimum, and average distances from this data. Using the average distance as a benchmark, calculate the absolute deviation rate: divide the difference between the maximum and minimum distances by the average distance, then multiply by 100%. Simultaneously, using the design nominal radius as a benchmark, calculate the relative deviation rate: divide the difference between the maximum and minimum distances by the design nominal radius, then multiply by 100%. Additionally, calculate the shape factor using the cross-sectional area and perimeter. The shape factor deviation is equal to 1 minus the difference in shape factor multiplied by 50%. Assign different weights to the three deviation rates based on the cable type. For example, for high-voltage cables (design nominal radius greater than 50 mm), the absolute deviation rate weight is 0.5, the relative deviation rate weight is 0.3, and the shape factor deviation weight is 0.2; for low-voltage cables (design nominal radius less than or equal to 50 mm), the absolute deviation rate weight is 0.3, the relative deviation rate weight is 0.5, and the shape factor deviation weight is 0.2. Multiply each of the three deviation rates by its respective weight and then sum them to obtain the initial roundness deviation rate. If the difference between the maximum distance and the minimum distance is greater than 0.2 times the average distance, increase the initial roundness deviation rate by 10% to obtain the final roundness deviation rate.

[0109] Determine the first correction factor: Find the relationship between the roundness deviation rate and the first correction factor through experimental or empirical data. Generally, the larger the roundness deviation rate, the more uneven the coating thickness, and the first correction factor will change according to a certain pattern, such as a linear change or other functional relationship. Substitute the roundness deviation rate at future time points into the relationship between the roundness deviation rate and the first correction factor to determine the first correction factor.

[0110] In this embodiment, a second correction factor is determined based on the local temperature gradient factor at future times to determine the base value of the coating thickness:

[0111] The relationship between the local temperature gradient factor and the second correction coefficient is determined through experiments or accumulated empirical data. Generally, a larger local temperature gradient factor indicates a greater influence of the temperature field on the coating thickness, and the second correction coefficient will change according to a specific functional relationship. For example, a table or functional expression corresponding to the local temperature gradient factor and the second correction coefficient can be obtained through numerous experiments. Based on the local temperature gradient factor at future times and this functional relationship or expression, the second correction coefficient for the baseline value of the coating thickness is determined.

[0112] In this embodiment, the coefficient of friction at future moments is determined based on the predicted values ​​of the normal component of the real-time wheel-cable contact force and the tangential friction force at future moments:

[0113] Obtain the normal component and tangential friction force values ​​of the real-time wheel-cable contact force at future moments.

[0114] Dividing the predicted value of the tangential friction force by the predicted value of the normal component yields the friction coefficient at future moments.

[0115] In this embodiment, a third correction factor is used to determine the base value of the coating thickness based on the friction coefficient at a future time:

[0116] Through experiments or experience, the relationship between the coefficient of friction and the third correction factor is determined. Generally, when the coefficient of friction deviates from its optimal value, it affects the coating thickness, and the third correction factor changes according to a certain pattern with the coefficient of friction. For example, when the coefficient of friction exceeds a certain value, the third correction factor decreases by a certain proportion to correct the base value of the coating thickness. The third correction factor for the base value of the coating thickness is obtained by substituting the future coefficient of friction into the relationship between the coefficient of friction and the third correction factor.

[0117] In this embodiment, the predicted coating thickness for future times is calculated based on the baseline coating thickness value at future times and the corresponding first correction coefficient, second correction coefficient, and third correction coefficient:

[0118] The predicted coating thickness at a future moment is equal to the base coating thickness multiplied by the first correction factor, the second correction factor, and the third correction factor, thus obtaining a predicted coating thickness at a future moment that is closer to the actual situation.

[0119] To obtain the roundness deviation rate at future moments and thus more accurately reflect the impact of cable roundness variations on coating thickness, a method is proposed to determine the roundness deviation rate at future moments based on the predicted value of cable cross-sectional roundness, including:

[0120] Based on the three-dimensional profile of the cable surface, the distribution vector of the circumferential center distance of the cable cross section at future times is predicted, and the maximum center distance, minimum center distance, average center distance, standard deviation of center distance and roundness shape factor of the cable cross section at future times are extracted from the distribution vector of the circumferential center distance of the cable cross section.

[0121] The distribution vector of the circumferential center distance of the cable cross-section at future times is corrected based on the maximum center distance, minimum center distance, average center distance, standard deviation of center distance, and roundness shape factor of the cable cross-section at future times, so as to obtain the corrected distribution vector of the circumferential center distance of the cable cross-section at future times.

[0122] The roundness baseline deviation rate at future moments is calculated based on the corrected distribution vector of the circumferential center distance of the cable cross-section at future moments.

[0123] Multiple circumferential partitions are obtained by dividing the cable cross-section at future time, and the average center distance deviation of each circumferential partition is calculated based on the distribution vector of the circumferential center distance of the cable cross-section at future time.

[0124] Based on the distribution location of all circumferential partitions whose average center distance deviation is greater than the preset center distance deviation threshold and the corresponding excess value of the average center distance deviation, the roundness basic deviation rate at future time is corrected to obtain the roundness deviation rate at future time.

[0125] In this embodiment, the circumferential center distance distribution vector of the cable cross-section at future times is predicted based on the three-dimensional contour of the cable surface:

[0126] Based on the 3D contour data of the cable surface obtained through real-time reconstruction, the cable cross-section at a future moment is analyzed. Along the circumference of the cable cross-section, the distance from each point on the cross-section to the center of the cable cross-section is determined. These distances are arranged in circumferential order to form a vector, namely the circumferential center distance distribution vector of the cable cross-section.

[0127] 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 future times are extracted from the circumferential center distance distribution vector of the cable cross-section.

[0128] In the obtained distribution vector of circumferential center distances of the cable cross-section, find the distance with the largest value, which is the maximum center distance of the cable cross-section in the future; find the distance with the smallest value, which is the minimum center distance of the cable cross-section in the future.

[0129] Adding all the distance values ​​in the vector together and then dividing by the number of elements in the vector gives the average center distance of the cable cross-section at future moments.

[0130] To calculate the standard deviation of the center distance, first find the difference between each distance value and the average center distance. Then square these differences, add them together, divide by the number of elements, and finally take the square root of the result. This gives you the standard deviation of the center distance.

[0131] The roundness form factor is calculated using the area and perimeter of the cable cross-section. The formula is: Roundness form factor = 4 multiplied by pi multiplied by the cross-sectional area, then divided by the square of the cross-sectional perimeter. This factor measures how close the cable cross-section is to a circle.

[0132] In this embodiment, the distribution vector of the circumferential center distance of the cable cross-section at future times is corrected based on the maximum center distance, minimum center distance, average center distance, standard deviation of center distance, and roundness shape factor of the cable cross-section at future times, to obtain the corrected distribution vector of the circumferential center distance of the cable cross-section at future times:

[0133] Using the obtained parameters such as maximum center distance, minimum center distance, average center distance, standard deviation of center distance, and roundness shape factor, the circumferential center distance distribution vector of the cable cross-section is adjusted. For example, based on the standard deviation of center distance and average center distance: the standard deviation of center distance is 0.2mm, and the average center distance is 10.0mm as the baseline, considering a range of ±1.5 times the standard deviation (9.7mm-10.3mm). The data in the vector are all within this range, but the values ​​near the boundary can be appropriately adjusted to make them more concentrated on the average center distance. For example, 10.3mm can be adjusted to 10.2mm, and 9.7mm can be adjusted to 9.8mm, at which point the vector becomes [10.1,9.9,10.2,9.8,10.0,10.2,9.8].

[0134] Referring to the maximum and minimum center distances and the roundness shape factor: although the maximum center distance of 10.3mm and the minimum center distance of 9.7mm were not exceeded, further fine-tuning was conducted to make the overall shape more circular (a roundness shape factor of 0.96 indicates a near-circular shape). For example, 10.2mm could be appropriately reduced to 10.15mm to make the data distribution more uniform. The final corrected distribution vector of the circumferential center distance of the cable cross-section at future times is [10.1, 9.9, 10.15, 9.8, 10.0, 10.15, 9.8]. This vector, after correction based on various parameters, can more accurately reflect the true geometric characteristics of the cable cross-section.

[0135] The vector obtained after this correction is the corrected distribution vector of the circumferential center distance of the cable cross-section at future times.

[0136] In this embodiment, the basic roundness deviation rate at future times is calculated based on the corrected distribution vector of the circumferential center distance of the cable cross-section at future times:

[0137] Using the average center distance as a benchmark, the minimum center distance is subtracted from the maximum center distance, and the difference is divided by the average center distance. Finally, it is multiplied by 100% to calculate a deviation rate. This deviation rate is the basic roundness deviation rate at future moments, which initially reflects the degree of deviation between the roundness of the cable cross-section and the ideal circle.

[0138] In this embodiment, multiple circumferential partitions are obtained by performing circumferential partitioning based on the cable cross-section at a future time:

[0139] Divide the cable cross-section at future moments along the circumference at certain angular intervals, such as 30° intervals. This divides the cable cross-section into 12 sector regions, which are multiple circumferential partitions.

[0140] In this embodiment, the average center distance deviation of each circumferential partition is calculated based on the distribution vector of the circumferential center distance correction of the cable cross-section at future time:

[0141] For each circumferential zone, the distance values ​​belonging to that zone are extracted from the circumferential center distance correction distribution vector of the cable cross-section. These distance values ​​are summed and then divided by the number of distance values ​​within that zone to obtain the average center distance of that zone. Then, this average center distance is subtracted from the average center distance of the cable cross-section, and the difference is the average center distance deviation of that circumferential zone, which reflects the degree of deviation of each circumferential zone from the overall average of the cable cross-section.

[0142] In this embodiment, a preset center distance deviation threshold is used as a standard to determine whether the average center distance deviation of the circumferential partition is abnormal.

[0143] In this embodiment, the excessive value of the average center distance deviation is defined as follows: when the average center distance deviation of a certain circumferential partition is greater than a preset center distance deviation threshold, the portion of this deviation exceeding the threshold is the excessive value of the average center distance deviation. For example, if the preset center distance deviation threshold is 0.5 and the average center distance deviation of a certain circumferential partition is 0.8, then the excessive value is 0.8 - 0.5 = 0.3.

[0144] In this embodiment, based on the distribution locations of all circumferential partitions whose average center distance deviation exceeds a preset center distance deviation threshold and the corresponding excess values ​​of the average center distance deviation, the roundness baseline deviation rate at future times is corrected to obtain the roundness deviation rate at future times:

[0145] Collect all circumferential partitions whose average center distance deviation exceeds a preset center distance deviation threshold, record their distribution along the circumference, and the corresponding excess value of the average center distance deviation for each partition. Based on the distribution of these circumferential partitions and the magnitude of the excess values, adjust the previously calculated basic roundness deviation rate. For example, since there are partitions with average center distance deviations exceeding the threshold, and these excess values ​​vary in size, the basic roundness deviation rate needs to be corrected. Based on experience or pre-defined rules, decide to accumulate the deviation rates corresponding to each excess partition.

[0146] Assume that the deviation rate increment for each out-of-limit value is set as follows: the deviation rate increases by 1% for every 0.1 mm of out-of-limit value.

[0147] The first zone increases the deviation rate by 3% (0.3mm corresponds to 3 x 0.1mm), the third zone increases the deviation rate by 5% (0.5mm corresponds to 5 x 0.1mm), and the fifth zone increases the deviation rate by 4% (0.4mm corresponds to 4 x 0.1mm).

[0148] The final adjusted roundness deviation rate is 10% + 3% + 5% + 4% = 22%. By adjusting the basic roundness deviation rate according to the circumferential zoning distribution and the magnitude of the out-of-limit value, the actual roundness deviation of the cable cross-section can be more accurately reflected.

[0149] To determine the local temperature gradient factor at future moments and quantify the impact of the temperature field on the coating thickness, a method is proposed to determine the local temperature gradient factor at future moments based on the predicted value of the cable surface temperature field distribution, including:

[0150] A three-dimensional cable model is established based on the three-dimensional contour of the cable surface, and the three-dimensional cable model is spatially divided to obtain the cable spatial mesh;

[0151] Based on the predicted values ​​of the temperature field distribution on the cable surface at future times, the normalized value of the temperature gradient amplitude, the cosine 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 spatial grid are calculated. The local temperature gradient factor of each cable spatial grid is obtained by weighted summation of the normalized value of the temperature gradient amplitude, the cosine of the angle between the temperature gradient direction angle and the spraying direction, and the exponential decay term of the gradient change rate.

[0152] The local temperature gradient factor at future time is obtained by weighted averaging of the local temperature gradient factors of all cable spatial grids within the coated section.

[0153] 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 represent the geometric shape of the cable is constructed in three-dimensional space, and the cable is modeled as an entity in three-dimensional space.

[0154] In this embodiment, the three-dimensional cable model is spatially divided to obtain a cable space mesh: the three-dimensional space where the established three-dimensional cable model is located is divided into many small spatial units, just like cutting a three-dimensional object. These units form the cable space mesh.

[0155] In this embodiment, the normalized value of the temperature gradient amplitude, the cosine 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 for each cable spatial grid based on the predicted value of the cable surface temperature field distribution at future times.

[0156] Normalized value of temperature gradient amplitude: Based on the predicted temperature field data of the cable surface at future times, the value of the rate of temperature change within each cable spatial grid is calculated, i.e., the temperature gradient amplitude. This value is then divided by the overall average temperature gradient amplitude of the cable to obtain the normalized value of the temperature gradient amplitude of that unit.

[0157] Cosine of the angle between the temperature gradient direction angle and the spraying direction: Determine the angle between the direction of the fastest temperature change and the cable axis within each cable space grid, i.e., the temperature gradient direction angle, and specify that the spraying direction is radially outward. Calculate the angle between the temperature gradient direction angle and the spraying direction, and then find the cosine of this angle.

[0158] The exponential decay term of the gradient rate of change: This calculates how quickly the temperature gradient magnitude changes over time or space within each cable space grid, i.e., the temperature gradient rate of change. Assuming the temperature gradient rate of change is r, the exponential decay term is typically calculated as e. −kr (where k is a constant, for example, k=0.1).

[0159] In this embodiment, the local temperature gradient factor for each cable space grid is obtained by weighted summing of the normalized value of the temperature gradient amplitude, the cosine of the angle between the temperature gradient direction angle and the spraying direction, and the exponential decay term of the gradient rate of change. Weights, such as 0.5, 0.3, and 0.2, are assigned to these three values ​​for each cell. The local temperature gradient factor for each cable space grid is then obtained by multiplying each of these three values ​​by its respective weight and summing the results.

[0160] In this embodiment, the local temperature gradient factors of all cable spatial grids within the coated section at future times are weighted and averaged to obtain the local temperature gradient factor at future times: For the section of cable to be coated at future times, the local temperature gradient factors of all cable spatial grids within it are weighted and averaged. The weight of each grid cell is determined based on its area and prediction confidence; the larger the area and the higher the prediction confidence, the greater the weight. The local temperature gradient factors of all cells are multiplied by their respective weights, summed, and then divided by the total weights to obtain the local temperature gradient factor at future times.

[0161] To obtain the walking speed adjustment and robot drive wheel normal pressure compensation for accurate compensation, it is proposed that when the deviation between the predicted coating thickness at a future time and the corresponding target thickness exceeds a preset deviation threshold, the walking speed adjustment and / or robot drive wheel normal pressure compensation are calculated, including:

[0162] The robot's future coating position is determined based on its current walking speed and current coating position, and the target thickness at the robot's future coating position is obtained.

[0163] When the deviation between the predicted coating thickness at a future time and the target thickness at the robot's coating position at a future time is greater than a preset deviation threshold, the basic compensation amount of the walking speed is calculated based on the speed adjustment coefficient, the deviation between the predicted coating thickness at a future time and the target thickness at the robot's coating position at a future time, the target thickness, the current walking speed, the standard walking speed, the current cable radius, and the nominal cable radius. The basic compensation amount of the walking speed is then dynamically corrected based on the dynamic correction rule to obtain the optimal compensation amount of the walking speed.

[0164] Simultaneously, based on the positive pressure adjustment coefficient, the deviation between the predicted coating thickness at future moments and the target thickness at the robot's coating position at future moments, 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, the basic compensation amount of the positive pressure of the robot's drive wheel is calculated, and the basic compensation amount of the positive pressure of the robot's drive wheel is corrected by safety constraints to obtain the optimal compensation amount of the robot's drive wheel.

[0165] In this embodiment, the robot's current coating position refers to the specific location of the robot at the current moment while performing coating operations on the cable. This can be determined through the robot's own positioning system or by location markers related to the cable.

[0166] In this embodiment, the robot's future coating position is determined based on its current walking speed and current coating position: Given the robot's current walking speed and coating position on the cable, assume the time interval between the future and current moments is t. If the robot's current walking speed is constant, then the future coating position equals 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 from the cable's starting point, the current walking speed is 1 meter per second, and the future moment is 10 seconds later, then the future coating position will be 10 + 1 × 10 = 20 meters from the cable's starting point. If the robot is accelerating or decelerating, the future coating position needs to be calculated using the corresponding kinematic formulas.

[0167] In this embodiment, the target thickness at the robot's coating location at a future time is obtained: based on the cable coating process requirements and quality standards, a pre-set cable coating thickness value is determined for the robot's coating location at the future time. This value represents the expected coating thickness the robot will achieve at that location and is used to compare it with the predicted coating thickness.

[0168] In this embodiment, a preset deviation threshold is a pre-defined value used to determine whether the deviation between the predicted coating thickness at a future time and the target thickness is too large. When the absolute value of the difference between the predicted coating thickness at a future time and the target thickness at the robot's coating position at the future time is greater than this preset deviation threshold, it is considered that the deviation between the predicted coating thickness and the expected target thickness exceeds the allowable range, and the robot's walking speed or the positive pressure of the drive wheels needs to be adjusted.

[0169] In this embodiment, the speed adjustment coefficient is a parameter used to adjust the robot's walking speed. Different values ​​are set based on the different levels of deviation between the predicted coating thickness and the target thickness at future moments. For example, when the deviation is in the first-level deviation range (5% < |deviation between predicted thickness and target thickness| ≤ 10%), the speed adjustment coefficient is 0.8; in the second-level deviation range (10% < |deviation| ≤ 15%), it is 1.2; and in the third-level deviation range (|deviation| > 15%), it is 1.5. This coefficient is used to calculate the walking speed compensation amount to adjust the robot's walking speed so that the coating thickness is closer to the target value.

[0170] In this embodiment, the standard walking speed is the standard walking speed value set by the robot when performing cable coating operations under ideal conditions.

[0171] In this embodiment, the nominal cable radius is the standard radius value specified in the design or production of the cable.

[0172] In this embodiment, the basic compensation amount for walking speed is calculated based on the speed adjustment coefficient, the deviation between the predicted coating thickness at a future moment and the target thickness at the robot's coating position at a future moment, the target thickness, the current walking speed, the standard walking speed, the current cable radius, and the nominal cable radius.

[0173] Calculate the deviation between the predicted coating thickness at a future time and the target thickness at the robot's coating position at the future time, and obtain the deviation by subtracting the target thickness from the predicted thickness;

[0174] Divide this deviation value by the target thickness to obtain the deviation ratio;

[0175] Divide the current walking speed by the standard walking speed to get the speed ratio;

[0176] Divide the current cable radius by the nominal cable radius to get the radius ratio, and then take the square root of the ratio.

[0177] Finally, multiplying the square roots of the deviation ratio, speed ratio, and radius ratio obtained above, along with the speed adjustment coefficient, yields the base compensation amount for walking speed. The formula is expressed as:

[0178] Walking speed base compensation = speed adjustment coefficient × (deviation between predicted thickness and target thickness ÷ target thickness) × (current walking speed ÷ standard walking speed) × (current cable radius ÷ nominal cable radius) 1 / 2 .

[0179] In this embodiment, the positive pressure adjustment coefficient is a parameter used to adjust the positive pressure of the robot drive wheel. Similar to the speed adjustment coefficient, it is set with different values ​​based on the different levels of deviation between the predicted coating thickness and the target thickness at different future times. For a level two deviation, the value is 0.3; for a level three deviation, the value is 0.5. This coefficient is used to calculate the positive pressure compensation of the robot drive wheel to adjust the positive pressure of the drive wheel on the cable, thereby affecting the coating thickness.

[0180] In this embodiment, the current wheel-cable friction coefficient is the friction coefficient of the robot drive wheel and the cable in the current contact state. It reflects the magnitude of the friction force between the drive wheel and the cable. The coefficient is obtained by measuring the normal component and tangential friction force of the wheel-cable contact force in real time by a six-axis force sensor integrated at the robot drive wheel, and then dividing the tangential friction force by the normal component.

[0181] In this embodiment, the optimal friction coefficient, obtained through experimental calibration, is the wheel-cable friction coefficient value that enables the robot to achieve optimal operating conditions during the coating operation, and is fixed at 0.45. When calculating the basic compensation amount of the normal pressure on the robot's drive wheel, the current wheel-cable friction coefficient is compared with the optimal friction coefficient to determine the adjustment amount of the normal pressure, ensuring the stability of the robot's movement and coating operation.

[0182] In this embodiment, the nominal positive pressure is the standard positive pressure (a reference value specified in the design or process) applied to the cable by the robot drive wheel, as defined in the robot design or cable coating process. It serves as a reference standard for calculating the positive pressure compensation amount of the robot drive wheel.

[0183] In this embodiment, the basic compensation amount of the normal pressure of the robot drive wheel is calculated based on the normal pressure adjustment coefficient, the deviation between the predicted coating thickness at a future time and the target thickness at the robot's coating position at a future time, the target thickness, the current wheel-cable friction coefficient, the optimal friction coefficient, the nominal normal pressure, the current cable radius, and the nominal cable radius.

[0184] First, calculate the deviation between the predicted coating thickness at a future time and the target thickness at the robot's coating position at the future time. Then, subtract the target thickness from the predicted thickness to obtain the deviation value.

[0185] Then divide this deviation value by the target thickness to obtain the deviation ratio;

[0186] Then divide the current wheel-cable friction coefficient by the optimal friction coefficient to obtain the friction coefficient ratio;

[0187] Then divide the current cable radius by the nominal cable radius to get the radius ratio;

[0188] Finally, multiplying the deviation ratio, friction coefficient ratio, radius ratio, nominal normal force, and normal force adjustment coefficient obtained above, the result is the basic compensation amount of the normal force of the robot drive wheel. The formula is expressed as:

[0189] The basic compensation amount of positive pressure = 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).

[0190] To obtain a more realistic optimal compensation amount for walking speed, a dynamic correction rule is proposed to dynamically adjust the basic compensation amount for walking speed, thereby obtaining the optimal compensation amount for walking speed, including:

[0191] If the instantaneous pressure fluctuation frequency of the coating is greater than the preset fluctuation frequency threshold, then the first correction coefficient of the basic compensation amount of the walking speed 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 amount.

[0192] If the cable cross-section roundness deviation rate is greater than the preset deviation rate threshold, then the second correction coefficient of the walking speed basic compensation amount is determined based on the cable cross-section roundness deviation rate and the correlation coefficient between the cable cross-section roundness deviation rate and the speed compensation amount.

[0193] The basic compensation amount of walking speed is dynamically corrected based on the first and second correction coefficients to obtain the optimal compensation amount of walking speed.

[0194] In this embodiment, a preset fluctuation frequency threshold is defined as 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 falls within the range requiring correction of the walking speed compensation. For example, it might be set to 10Hz.

[0195] In this embodiment, the instantaneous pressure fluctuation frequency of the coating refers to the number of times the coating pressure fluctuates per unit time during the coating spraying process. This is obtained in real time through a high-frequency pressure sensor installed at the outlet of the robot spray gun.

[0196] In this embodiment, the correlation coefficient between the instantaneous pressure fluctuation frequency and the speed compensation amount of the coating is a coefficient used to establish the relationship between the instantaneous pressure fluctuation frequency of the coating and the walking speed compensation amount. This coefficient is determined through experiments, experience, or theoretical analysis, and it indicates the degree to which the walking speed compensation amount changes for every unit change in the instantaneous pressure fluctuation frequency of the coating. For example, if the coefficient is 0.02, it means that for every 1 Hz increase in the instantaneous pressure fluctuation frequency of the coating, the walking speed compensation amount will be adjusted according to a certain rule (such as multiplying by a value related to this coefficient).

[0197] In this embodiment, the first correction coefficient for the basic compensation amount of walking speed 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 amount.

[0198] When the instantaneous pressure fluctuation frequency of the coating exceeds a preset fluctuation frequency threshold, the value obtained by multiplying the instantaneous pressure fluctuation frequency of the coating by the correlation coefficient between the instantaneous pressure fluctuation frequency and the speed compensation amount, and then adding 1, is the first correction coefficient for the basic compensation amount of the walking speed. For example, if the instantaneous pressure fluctuation frequency of the coating is 15Hz, the correlation coefficient between the instantaneous pressure fluctuation frequency and the speed compensation amount is 0.02, and the preset fluctuation 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 compensation amount of the walking speed to more accurately adjust the robot's walking speed and adapt to the influence of coating pressure fluctuations on the coating thickness.

[0199] In this embodiment, a preset deviation rate threshold is defined as a pre-set numerical limit for the roundness deviation rate of the cable cross-section. During actual calculation and adjustment, the roundness deviation rate of the cable cross-section is compared with this threshold to determine whether the roundness deviation of the cable cross-section has reached a level requiring further correction of the walking speed compensation. For example, it may be set to 5%.

[0200] In this embodiment, the cable cross-section roundness deviation rate-speed compensation correlation coefficient is a coefficient used to correlate the cable cross-section roundness deviation rate and the walking speed compensation amount. Similar to the correlation coefficient between the instantaneous pressure fluctuation frequency of paint and the speed compensation amount, it is derived through experiments, experience, or theoretical analysis, reflecting the degree of change in the walking speed compensation amount for every unit change in the cable cross-section roundness deviation rate. For example, a coefficient of 0.05 indicates that for every 1% increase in the cable cross-section roundness deviation rate, the walking speed compensation amount will be adjusted according to this coefficient and specific rules.

[0201] In this embodiment, a second correction coefficient for the basic compensation amount of walking speed is determined based on the cable cross-section roundness deviation rate and the correlation coefficient between the cable cross-section roundness deviation rate and the speed compensation amount.

[0202] When the cable cross-section roundness deviation rate exceeds the preset deviation rate threshold, the second correction coefficient for the basic walking speed compensation is obtained by multiplying the cable cross-section roundness deviation rate by the correlation coefficient between the cable cross-section roundness deviation rate and the speed compensation amount, and then adding 1. For example, if the cable cross-section roundness deviation rate is 8%, the correlation coefficient between the cable cross-section roundness deviation rate and the speed compensation amount is 0.05, and the preset deviation rate threshold is 5%, then the second correction coefficient = 1 + 8 × 0.05 = 1.4. This second correction coefficient is used to further adjust the basic walking speed compensation amount to address the impact of cable roundness deviation on the coating thickness.

[0203] In this embodiment, the basic walking speed compensation is dynamically corrected based on the first and second correction coefficients to obtain the optimal walking speed compensation.

[0204] Multiplying the basic walking speed compensation by the first and second correction factors yields the optimal walking speed compensation. For example, if the basic walking speed compensation is 0.5, the first correction factor is 1.3, and the second correction factor is 1.4, then the optimal walking speed compensation is 0.5 × 1.3 × 1.4 = 0.91. This dynamic correction method comprehensively considers the influence of the instantaneous pressure fluctuation frequency of the coating and the roundness deviation rate of the cable cross-section on the walking speed, making the calculated walking speed compensation more consistent with actual coating requirements and helping to improve coating quality.

[0205] To obtain the optimal compensation amount for the robot drive wheel and ensure the safe and reasonable normal force compensation, a safety constraint correction is proposed for the basic normal force compensation amount of the robot drive wheel to obtain the optimal compensation amount, including:

[0206] If the friction coefficient between the current wheel and the cable is greater than the preset friction coefficient threshold, then the product of the basic compensation amount of the normal pressure of the robot drive wheel and the preset constraint correction coefficient will be used as the first correction compensation amount of the robot drive wheel.

[0207] The first correction compensation amount of the robot drive wheel is advanced to obtain the second correction compensation amount of the robot drive wheel;

[0208] The second correction compensation amount of the robot drive wheel is corrected a third time based on the nominal positive pressure and the preset safety constraint ratio to obtain the optimal compensation amount of the robot drive wheel.

[0209] In this embodiment, a preset friction coefficient threshold is defined as a pre-set numerical standard for the wheel-cable friction coefficient. During the robotic coating process, the current wheel-cable friction coefficient, acquired in real time, is compared to this threshold. For example, it might be set to 0.6.

[0210] In this embodiment, a preset constraint correction coefficient is a pre-determined coefficient used to correct the basic compensation amount of the normal pressure of the robot drive wheel when the current wheel-cable friction coefficient is greater than a preset friction coefficient threshold. For example, when this occurs, the basic compensation amount of normal pressure is multiplied by the preset constraint correction coefficient to obtain the first correction compensation amount of the robot drive wheel, thereby adjusting the normal pressure compensation amount to better meet actual needs. Assuming that the precision requirements for cable coating are not extremely high and the wheel-cable friction coefficient fluctuation is relatively small, the preset constraint correction coefficient may be between 0.8 and 0.9. For example, a value of 0.85. If it is for special cable coatings with high precision requirements, where the coating thickness and robot walking stability requirements are extremely strict, the preset constraint correction coefficient may be between 0.6 and 0.7. For example, a value of 0.65. In some complex coating environments, the wheel-cable friction coefficient fluctuates frequently and significantly, and the preset constraint correction coefficient may be between 0.9 and 1. For example, a value of 0.95.

[0211] In this embodiment, the first correction compensation amount of the robot drive wheel is advanced to obtain the second correction compensation amount of the robot drive wheel:

[0212] Considering the inherent response delay of the pressure sensor (e.g., 20ms), a lead compensation is applied to the first correction compensation amount to more accurately compensate for the positive pressure on the robot drive wheel. Specifically, the rate of change of the first correction compensation amount (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 amount. This lead compensation amount is then added to the first correction compensation amount to obtain the second correction compensation amount for the robot drive wheel. This allows for advance adjustment of the positive pressure compensation amount to accommodate potential pressure change delays during the actual coating process.

[0213] In this embodiment, a preset safety constraint ratio is defined as a pre-set percentage to limit the amount of positive pressure compensation for the robot drive wheels, preventing the cable from being excessively deformed due to excessive pressure. For example, the preset safety constraint ratio might be set to 0.3, which means that the amount of positive pressure compensation for the robot drive wheels cannot exceed 30% of the nominal positive pressure.

[0214] In this embodiment, the second correction compensation amount of the robot drive wheel is corrected a third time based on the nominal normal force and the preset safety constraint ratio to obtain the optimal compensation amount of the robot drive wheel:

[0215] The second correction compensation amount for the robot drive wheel is determined to be greater than the nominal normal pressure multiplied by the preset safety constraint ratio. If it is, the second correction compensation amount is adjusted to the nominal normal pressure multiplied by the preset safety constraint ratio; this is the third correction of the second correction compensation amount. After this correction, the result is the optimal compensation amount for the robot drive wheel, ensuring that while guaranteeing coating quality, it does not cause excessive compression or other adverse effects on the cable.

[0216] like Figure 2 As shown, this invention provides an embodiment of a walking optimization system for a line-coating robot, comprising:

[0217] The multi-dimensional sensing module is used to reconstruct the three-dimensional contour of the cable surface in real time and extract the real-time cable radius and the roundness of the cable cross section. At the same time, it collects spraying pressure recording data, cable surface temperature field distribution, and the normal component and tangential friction force of the real-time wheel-cable contact force.

[0218] The thickness prediction module is used to obtain the predicted coating thickness at future moments based on real-time cable radius, cable cross-section roundness, spraying pressure recording data, cable surface temperature field distribution, the normal component and tangential friction of real-time wheel-cable contact force, and the robot's real-time walking speed.

[0219] The compensation calculation module is used to calculate the walking speed adjustment and / or the positive pressure compensation of the robot drive wheel when the deviation between the predicted coating thickness at a future time and the corresponding target thickness is greater than a preset deviation threshold.

[0220] The optimization control module is used to optimize the robot's walking control based on the walking speed adjustment and the positive pressure compensation of the robot's drive wheels, and obtain the walking optimization control results.

[0221] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of this invention and its equivalents, this invention also intends to include these modifications and variations.

Claims

1. A method for optimizing the movement of a robot for coating cables on a roll-to-roll type, characterized in that, include: The three-dimensional contour of the cable surface is reconstructed in real time, and the real-time cable radius and cable cross-sectional roundness are extracted. At the same time, the spraying pressure record data, the temperature field distribution of the cable surface, and the normal component and tangential friction force of the real-time wheel-cable contact force are collected. Based on real-time cable radius, cable cross-section roundness, spraying pressure recording data, cable surface temperature field distribution, real-time wheel-cable contact force normal component and tangential friction force, and robot real-time walking speed, the predicted value of coating thickness at future moments is obtained. 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 and / or the positive pressure compensation of the robot drive wheel are calculated. The robot's walking optimization control is performed based on the walking speed adjustment and the positive pressure compensation of the robot's drive wheels to obtain the walking optimization control results. Based on real-time cable radius, cable cross-sectional roundness, spraying pressure recording data, cable surface temperature field distribution, the normal component and tangential friction of real-time wheel-cable contact force, and the robot's real-time walking speed, the predicted coating thickness for future moments is obtained, including: Trend prediction is performed on the collected real-time cable radius, cable cross-section roundness, spraying pressure recording data, cable surface temperature field distribution, and the normal component and tangential friction of the real-time wheel-cable contact force. The predicted values ​​of the real-time cable radius, cable cross-section roundness, cable surface temperature field distribution, and the normal component and tangential friction of the real-time wheel-cable contact force at future times are obtained, as well as the predicted values ​​of the spraying pressure recording data at future times. Based on the spraying pressure record data and the predicted value to the future time, determine the average spraying pressure in the latest historical period with the future time as the reference and the instantaneous pressure fluctuation amplitude at the future time. The base value of the coating thickness in the future is calculated based on the average spraying pressure in the latest historical period based on the future time, the instantaneous pressure fluctuation amplitude in the future time, the predicted value of the real-time cable radius in the future time, the predicted value of the linear spraying speed of the standard coating in the future time, and the robot's current walking speed. The roundness deviation rate at future times is determined based on the predicted value of the cable cross-section roundness at future times, and the first correction coefficient of the base value of the coating thickness is determined based on the roundness deviation rate at future times. The local temperature gradient factor for the future time is determined based on the predicted value of the temperature field distribution on the cable surface at the future time, and the second correction coefficient for the base value of the coating thickness is determined based on the local temperature gradient factor for the future time. The friction coefficient at future moments is determined based on the predicted values ​​of the normal component of the real-time wheel-cable contact force and the tangential friction force at future moments, and a third correction coefficient for the base value of the coating thickness is determined based on the friction coefficient at future moments. The predicted coating thickness for future moments is calculated based on the baseline coating thickness value at future moments and the corresponding first, second, and third correction coefficients.

2. The method for optimizing the movement of a line-coating robot for line-type cables according to claim 1, characterized in that, Real-time reconstruction of the 3D contour of the cable surface 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 sets of laser profile sensors arranged circumferentially in the robot's walking mechanism and triangulation method. Real-time cable radius and cable cross-sectional roundness are extracted based on the three-dimensional contour of the cable surface.

3. The method for optimizing the movement of a line-coating robot for line-type cables according to claim 1, characterized in that, Data on spraying pressure, cable surface temperature field distribution, and real-time wheel-cable contact force (normal component and tangential friction) were collected, including: The high-frequency pressure sensor and infrared temperature sensor installed at the outlet of the robot spray gun synchronously collect spray pressure recording data and cable surface temperature field distribution. The robot uses a six-axis force sensor integrated at its drive wheel to collect the normal component of the wheel-cable contact force and the tangential friction force in real time.

4. The method for optimizing the movement of a line-coating robot for line-type cables according to claim 1, characterized in that, The future roundness deviation rate is determined based on the predicted value of the cable cross-section roundness at future times, including: Based on the three-dimensional profile of the cable surface, the distribution vector of the circumferential center distance of the cable cross section at future times is predicted, and the maximum center distance, minimum center distance, average center distance, standard deviation of center distance and roundness shape factor of the cable cross section at future times are extracted from the distribution vector of the circumferential center distance of the cable cross section. The distribution vector of the circumferential center distance of the cable cross-section at future times is corrected based on the maximum center distance, minimum center distance, average center distance, standard deviation of center distance, and roundness shape factor of the cable cross-section at future times, so as to obtain the corrected distribution vector of the circumferential center distance of the cable cross-section at future times. The roundness baseline deviation rate at future moments is calculated based on the corrected distribution vector of the circumferential center distance of the cable cross-section at future moments. Multiple circumferential partitions are obtained by dividing the cable cross-section at future time, and the average center distance deviation of each circumferential partition is calculated based on the distribution vector of the circumferential center distance of the cable cross-section at future time. Based on the distribution location of all circumferential partitions whose average center distance deviation is greater than the preset center distance deviation threshold and the corresponding excess value of the average center distance deviation, the roundness basic deviation rate at future time is corrected to obtain the roundness deviation rate at future time.

5. The method for optimizing the movement of a line-coating robot for line-type cables according to claim 1, characterized in that, The local temperature gradient factor for future times is determined based on the predicted values ​​of the cable surface temperature field distribution at future times, including: A three-dimensional cable model is established based on the three-dimensional contour of the cable surface, and the three-dimensional cable model is spatially divided to obtain the cable spatial mesh; Based on the predicted values ​​of the temperature field distribution on the cable surface at future times, the normalized value of the temperature gradient amplitude, the cosine 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 spatial grid are calculated. The local temperature gradient factor of each cable spatial grid is obtained by weighted summation of the normalized value of the temperature gradient amplitude, the cosine of the angle between the temperature gradient direction angle and the spraying direction, and the exponential decay term of the gradient change rate. The local temperature gradient factor at future time is obtained by weighted averaging of the local temperature gradient factors of all cable space grids within the coated section.

6. The method for optimizing the movement of a line-coating robot for line-type cables according to claim 1, characterized in that, When the deviation between the predicted coating thickness at a future time and the corresponding target thickness exceeds a preset deviation threshold, the walking speed adjustment and / or the positive pressure compensation of the robot drive wheels are calculated, including: The robot's future coating position is determined based on its current walking speed and current coating position, and the target thickness at the robot's future coating position is obtained. When the deviation between the predicted coating thickness at a future time and the target thickness at the robot's coating position at a future time is greater than a preset deviation threshold, the basic compensation amount of the walking speed is calculated based on the speed adjustment coefficient, the deviation between the predicted coating thickness at a future time and the target thickness at the robot's coating position at a future time, the target thickness, the current walking speed, the standard walking speed, the current cable radius, and the nominal cable radius. The basic compensation amount of the walking speed is then dynamically corrected based on the dynamic correction rule to obtain the optimal compensation amount of the walking speed. Simultaneously, based on the positive pressure adjustment coefficient, the deviation between the predicted coating thickness at future moments and the target thickness at the robot's coating position at future moments, 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, the basic compensation amount of the positive pressure of the robot's drive wheel is calculated, and the basic compensation amount of the positive pressure of the robot's drive wheel is corrected by safety constraints to obtain the optimal compensation amount of the robot's drive wheel.

7. The method for optimizing the movement of a line-coating robot for line-type cables according to claim 6, characterized in that, The optimal compensation amount for walking speed is obtained by dynamically adjusting the base compensation amount based on dynamic correction rules, including: If the instantaneous pressure fluctuation frequency of the coating is greater than the preset fluctuation frequency threshold, then the first correction coefficient of the basic compensation amount of the walking speed 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 amount. If the cable cross-section roundness deviation rate is greater than the preset deviation rate threshold, then the second correction coefficient of the basic compensation amount of walking speed is determined based on the cable cross-section roundness deviation rate and the correlation coefficient between the cable cross-section roundness deviation rate and the speed compensation amount. The basic compensation amount of walking speed is dynamically corrected based on the first and second correction coefficients to obtain the optimal compensation amount of walking speed.

8. The method for optimizing the movement of a line-coating robot for line-type cables according to claim 6, characterized in that, The basic compensation amount of the normal force on the robot drive wheel is corrected with safety constraints to obtain the optimal compensation amount for the robot drive wheel, including: If the friction coefficient between the current wheel and the cable is greater than the preset friction coefficient threshold, then the product of the basic compensation amount of the normal pressure of the robot drive wheel and the preset constraint correction coefficient will be used as the first correction compensation amount of the robot drive wheel. The first correction compensation amount of the robot drive wheel is advanced to obtain the second correction compensation amount of the robot drive wheel; The second correction compensation amount of the robot drive wheel is corrected a third time based on the nominal positive pressure and the preset safety constraint ratio to obtain the optimal compensation amount of the robot drive wheel.

9. The method for optimizing the movement of a line-coating robot for line-type cables according to claim 1, characterized in that, The method is based on a walking optimization system for line-coating robots, including: The multi-dimensional sensing module is used to reconstruct the three-dimensional contour of the cable surface in real time and extract the real-time cable radius and cable cross-sectional roundness. At the same time, it collects spraying pressure recording data, cable surface temperature field distribution, and the normal component and tangential friction force of the real-time wheel-cable contact force. The thickness prediction module is used to obtain the predicted coating thickness at future moments based on real-time cable radius, cable cross-section roundness, spraying pressure recording data, cable surface temperature field distribution, the normal component and tangential friction of real-time wheel-cable contact force, and the robot's real-time walking speed. The compensation calculation module is used to calculate the walking speed adjustment and / or the positive pressure compensation of the robot drive wheel when the deviation between the predicted coating thickness at a future time and the corresponding target thickness is greater than a preset deviation threshold. The optimization control module is used to optimize the robot's walking control based on the walking speed adjustment and the positive pressure compensation of the robot's drive wheels, and obtain the walking optimization control results.

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