Industrial Robot Precise Positioning and Motion Control Method Based on Visual Perception

Abstract

The present invention discloses a method for precise positioning and motion control of an industrial robot based on visual perception, which relates to the field of industrial robots. The method comprises: performing a spraying operation on a target tank according to initial spraying parameters; using a multi-camera to capture images of the target tank and performing image stitching to generate a sprayed tank image; performing grayscale processing, segmenting the tank grayscale image according to a predetermined grayscale threshold, and determining multiple local areas of the tank and multiple regional grayscale values; obtaining a target grayscale value, and using the target grayscale value as a reference, performing deviation calculations on the grayscale values of multiple regions to determine multiple secondary spraying thicknesses; generating a secondary spraying path sequence and a secondary spraying parameter sequence, and performing a secondary spraying operation on the target tank. The method solves the technical problems of inaccurate positioning and poor spraying uniformity in the spraying control of existing spraying robots, and achieves the technical effects of improving positioning accuracy and achieving uniform distribution of spraying thickness.

Description

Technical Field

[0001] The present application relates to the field of industrial robots, and in particular to a method for precise positioning and motion control of industrial robots based on visual perception. Background Art

[0002] Spraying is an important process widely used in a variety of industries, including automotive, aerospace, and home appliance manufacturing. Existing methods typically rely on preset spray parameters and a fixed spray path. Due to differences in the shape, size, and surface condition of workpieces, fixed spray paths often fail to accurately match the actual position of each workpiece, resulting in poor spraying results. Furthermore, operations based on preset spray parameters cannot adjust the spray thickness in real time to suit the actual conditions of different areas. This can result in over-spraying in some areas, wasting material, while under-spraying in other areas fails to achieve the desired protective effect.

[0003] In the current related technologies, the spraying control of the spraying robot has technical problems such as inaccurate positioning and poor spraying uniformity. Summary of the Invention

[0004] The present application provides a method for precise positioning and motion control of an industrial robot based on visual perception, performs a spraying operation on a target tank according to initial spraying parameters, uses a multi-camera to capture images of the target tank after the first spraying, and performs image stitching to generate a sprayed tank image, performs grayscale processing on the first sprayed tank image, segments the tank grayscale image according to a predetermined grayscale threshold, determines multiple local areas of the tank and multiple regional grayscale values, obtains a target grayscale value of the target spraying thickness, calculates deviations of the multiple regional grayscale values based on the value, determines multiple secondary spraying thicknesses, generates a secondary spraying path sequence and a secondary spraying parameter sequence according to the multiple local areas of the tank and the multiple secondary spraying thicknesses, and controls the spray robot to perform a secondary spraying operation on the target tank, thereby achieving the technical effect of improving positioning accuracy and realizing uniform distribution of spraying thickness.

[0005] This application provides a method for precise positioning and motion control of industrial robots based on visual perception, including:

[0006] According to the initial spraying parameters, the spraying robot is controlled to perform a spraying operation on the target tank once, wherein the initial spraying parameters are set based on the primary spraying thickness, and the primary spraying thickness is 70% of the target spraying thickness; a multi-camera is used to capture images of the target tank after the primary spraying operation, and the images are stitched together to generate a primary spraying tank image; the primary spraying tank image is grayscale processed, the tank grayscale image is segmented according to a predetermined grayscale threshold, and multiple local areas of the tank and multiple regional grayscale values are determined; the target grayscale value of the target spraying thickness is obtained, and the deviation of the multiple regional grayscale values is calculated based on the target grayscale value to determine multiple secondary spraying thicknesses; a secondary spraying path sequence and a secondary spraying parameter sequence are generated according to the multiple local areas of the tank and the multiple secondary spraying thicknesses, and based on the secondary spraying path sequence and the secondary spraying parameter sequence, the spray robot is controlled to perform a secondary spraying operation on the target tank.

[0007] In a possible implementation, the grayscale image of the storage tank is segmented according to a predetermined grayscale threshold, multiple local areas of the storage tank and multiple regional grayscale values are determined, and the following processing is performed:

[0008] Obtain a predetermined grayscale threshold, wherein the predetermined grayscale threshold is set based on a minimum spraying accuracy of the spray robot; perform grayscale value extraction on the grayscale image of the storage tank to obtain a minimum grayscale value and a maximum grayscale value; and segment the grayscale image of the storage tank based on the predetermined grayscale threshold, the minimum grayscale value, and the maximum grayscale value to obtain a plurality of local areas of the storage tank and a plurality of regional grayscale values.

[0009] In a possible implementation, the grayscale image of the storage tank is segmented based on the predetermined grayscale threshold, the minimum grayscale value, and the maximum grayscale value, and the following processing is performed:

[0010] Taking the position coordinates of the minimum grayscale value as the starting point, simulate water filling into the grayscale image of the storage tank, record the first grayscale value submerged by the water surface, and perform deviation calculation between the minimum grayscale value and the first grayscale value to obtain a first grayscale difference; if the first grayscale difference is greater than or equal to the predetermined grayscale threshold, set a first segmentation line in this area; if the first grayscale difference is less than the predetermined grayscale threshold, continue to simulate water filling; iteratively fill water until the water surface submerges the maximum grayscale value, stop filling water and obtain multiple segmentation lines; use the multiple segmentation lines to segment the grayscale image of the storage tank, and obtain multiple local areas of the storage tank and multiple regional grayscale values, wherein the regional grayscale value is the grayscale median value of the local area of the tank.

[0011] In a possible implementation, a target grayscale value of the target spraying thickness is obtained, and the following processing is performed:

[0012] Obtain a standard grayscale value of the target spray thickness under standard lighting parameters; obtain the ambient lighting parameters during the single spray tank image acquisition, perform deviation calculation on the ambient lighting parameters and the standard lighting parameters, and determine the lighting parameter difference; input the lighting parameter difference into a lighting corrector and output a grayscale correction coefficient, wherein the lighting corrector is constructed based on a BP neural network and is trained to convergence through sample data; perform parameter correction on the standard grayscale value according to the grayscale correction coefficient to obtain the target grayscale value.

[0013] In a possible implementation, the target grayscale value is used as a reference, deviation calculations are performed on the grayscale values of the multiple regions respectively, multiple secondary spraying thicknesses are determined, and the following processing is performed:

[0014] Based on the target grayscale value, deviation calculation is performed on the grayscale values of the multiple regions to determine multiple regional grayscale differences; the multiple regional grayscale differences are input into a predetermined grayscale difference-secondary spraying thickness comparison table for matching, and the multiple secondary spraying thicknesses are output.

[0015] In a possible implementation, a secondary spraying path sequence and a secondary spraying parameter sequence are generated according to the multiple local areas of the storage tank and the multiple secondary spraying thicknesses, and the following processing is performed:

[0016] Arrange the multiple tank local areas from large to small according to the area area to generate a tank local area sequence; perform operation simulation of the spraying robot in the spraying operation twin space for the purpose of covering the tank local area sequence, and generate the secondary spraying path sequence based on the simulation results; generate a secondary spraying thickness sequence based on the multiple secondary spraying thickness mappings based on the tank local area sequence; generate the secondary spraying parameter sequence based on the secondary spraying thickness sequence in order to meet the secondary spraying thickness as an operation constraint.

[0017] In a possible implementation, the secondary spraying parameter sequence is generated according to the secondary spraying thickness sequence, and the following processing is performed:

[0018] Taking the secondary spraying thickness as the operation constraint, an operation simulation is performed in the spraying operation twin space based on the secondary spraying thickness sequence to generate an initial secondary spraying parameter sequence; a primary spraying operation deviation analysis is performed based on the grayscale values of the multiple local areas of the tanks and the multiple areas to determine the primary spraying thickness deviation; the primary spraying thickness deviation is input into the spraying parameter compensation model, and the spraying parameter compensation coefficient is output to compensate the initial secondary spraying parameter sequence to obtain the secondary spraying parameter sequence.

[0019] In a possible implementation, a spraying operation deviation analysis is performed based on the grayscale values of the multiple local areas of the storage tanks and the multiple areas to determine the spraying thickness deviation, and the following processing is performed:

[0020] Acquire multiple area areas of the multiple local areas of the storage tanks and calculate multiple area ratios; perform product operations on the multiple area ratios and multiple area grayscale values, and sum them to obtain a single spraying grayscale mean; input the single spraying grayscale mean into a predetermined grayscale-spraying thickness comparison table for matching, and output a single spraying thickness mean; perform deviation calculation on the single spraying thickness and the single spraying thickness mean to obtain the single spraying thickness deviation.

[0021] The method for precise positioning and motion control of an industrial robot based on visual perception proposed in this application first controls the spraying robot to perform a spraying operation on the target tank according to the initial spraying parameters, wherein the initial spraying parameters are set based on the primary spraying thickness, and the primary spraying thickness is 70% of the target spraying thickness. Then, a multi-camera is used to capture an image of the target tank after the primary spraying operation is completed, and the image is stitched to generate a primary spraying tank image. Then, the primary spraying tank image is grayscale processed, and the tank grayscale image is segmented according to a predetermined grayscale threshold to determine multiple local areas of the tank and multiple regional grayscale values. Then, the target grayscale value of the target spraying thickness is obtained. Based on the target grayscale value, the deviation of the grayscale values of the multiple regions is calculated to determine multiple secondary spraying thicknesses. Finally, a secondary spraying path sequence and a secondary spraying parameter sequence are generated based on the multiple local areas of the tank and the multiple secondary spraying thicknesses. Based on the secondary spraying path sequence and the secondary spraying parameter sequence, the spraying robot is controlled to perform a secondary spraying operation on the target tank, thereby achieving the technical effect of improving positioning accuracy and achieving uniform distribution of spraying thickness. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings of the embodiments of the present invention are briefly introduced below. Flowcharts are used in this application to illustrate the operations performed by the system according to the embodiments of the present application. It should be understood that the preceding or following operations are not necessarily performed in precise order. Instead, various steps may be processed in reverse order or simultaneously as needed. Furthermore, other operations may be added to these processes, or one or more operations may be removed from these processes.

[0023] Figure 1 A flow chart of a method for precise positioning and motion control of an industrial robot based on visual perception provided in an embodiment of the present application.

[0024] Figure 2A schematic diagram of the process of generating a secondary spraying path sequence and a secondary spraying parameter sequence in the method for precise positioning and motion control of an industrial robot based on visual perception provided in an embodiment of the present application. DETAILED DESCRIPTION

[0025] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below.

[0026] In order to make the purpose, technical solutions and advantages of this application clearer, the application will be further described in detail below with reference to the accompanying drawings. The described embodiments should not be regarded as limiting this application. All other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this application.

[0027] In the following description, reference is made to “some embodiments”, which describes a subset of all possible embodiments, but it will be understood that “some embodiments” may be the same subset or different subsets of all possible embodiments and may be combined with each other without conflict, and the terms “first\second” involved are merely used to distinguish similar objects and do not represent a specific ordering of the objects. The terms “including” and “having” and any variations are intended to cover non-exclusive inclusions. For example, a process, method, system, product, or server that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or modules that are not clearly listed or that are inherent to these processes, methods, products, or devices. Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which this application belongs. The terms used herein are for the purpose of describing the embodiments of this application only.

[0028] The present invention provides a method for precise positioning and motion control of industrial robots based on visual perception. Figure 1 As shown, the method includes:

[0029] In step S100, the spray robot is controlled to perform a single spraying operation on the target tank according to initial spraying parameters. The initial spraying parameters are set based on a single spraying thickness, which is 70% of the target spraying thickness. Specifically, the initial spraying parameters refer to various parameters used during a single spraying operation, such as spraying speed, spraying pressure, and spraying angle. These parameters are set based on 70% of the target spraying thickness (the desired final spraying thickness, which serves as the quality standard for the spraying operation) and collectively determine the thickness and uniformity of the spraying. The spray robot then performs the spraying operation on the target tank according to the preset single spraying parameters.

[0030] Step S200 uses a multi-camera system to capture images of the target tank after a spraying operation. These images are then stitched together to create a single image of the sprayed tank. Specifically, the multi-camera system captures omnidirectional images of the target tank after a spraying operation. Using image stitching technology, the images captured by the multiple cameras are stitched together to create a complete image of the sprayed tank.

[0031] Step S300 performs grayscale processing on the painted tank image. The grayscale image is segmented based on a predetermined grayscale threshold to determine multiple localized tank regions and multiple regional grayscale values. Specifically, grayscale processing converts the color image into a grayscale image, where each pixel in the grayscale image has a single brightness value. The grayscale image is segmented based on a predetermined grayscale threshold (a threshold used for image segmentation; pixels with grayscale values exceeding or falling below this threshold are classified as distinct regions). Multiple localized tank regions (different portions of the tank surface identified through image segmentation) are determined, and the grayscale value of each region is calculated.

[0032] In one possible implementation, the grayscale image of the tank is segmented based on a predetermined grayscale threshold to determine multiple localized tank regions and multiple regional grayscale values. Step S300 further includes step S310: obtaining the predetermined grayscale threshold, where the predetermined grayscale threshold is set based on the minimum spraying accuracy of the spraying robot. Specifically, the predetermined grayscale threshold is read from system parameters or a configuration file. This threshold is set based on the minimum spraying accuracy of the spraying robot and serves as a benchmark for image segmentation. The predetermined grayscale threshold is the grayscale value limit for image segmentation. When the grayscale value of a portion of the image exceeds or falls below this threshold, the portion is considered a different region or object. The minimum spraying accuracy is the minimum spray thickness adjustment unit that the spraying robot can achieve, reflecting the robot's spray control capabilities. For example, N times the minimum spraying accuracy (N is a positive integer) is set as the minimum spray adjustment thickness to ensure that spray thickness adjustment is practical. Step S320: grayscale value extraction is performed on the grayscale image of the tank to obtain the minimum and maximum grayscale values. Specifically, each pixel of the grayscale image of the tank is traversed and its grayscale value (the brightness value of each pixel in the grayscale image, ranging from 0 to 255) is extracted. During the traversal process, the minimum and maximum grayscale values are recorded and saved. Step S330, based on the predetermined grayscale threshold, minimum grayscale value, and maximum grayscale value, the grayscale image of the tank is segmented to obtain multiple local areas of the tank and multiple regional grayscale values. Specifically, based on the predetermined grayscale threshold, minimum grayscale value, and maximum grayscale value, a threshold segmentation method is used to divide the grayscale image of the tank into different areas. For each segmented area, the average or median of its grayscale value is calculated as the grayscale value of the area. This implementation method divides different spraying areas according to the grayscale value differences of the grayscale image of the tank by setting a predetermined grayscale threshold, thereby improving the accuracy of the division and thereby improving the control accuracy of the spraying thickness.

[0033] In one possible implementation, the tank grayscale image is segmented based on the predetermined grayscale threshold, minimum grayscale value, and maximum grayscale value. Step S330 further includes step S331: Starting from the coordinates of the minimum grayscale value, the tank grayscale image is simulated flooded with water. The first grayscale value submerged by the water is recorded, and the deviation between the minimum grayscale value and the first grayscale value is calculated to obtain a first grayscale difference. Specifically, the pixel corresponding to the minimum grayscale value is found in the tank grayscale image, and its coordinates are recorded as the starting point for the simulated flooding. Starting from the starting point, "simulated flooding" is performed on surrounding pixels, gradually expanding the range of grayscale values considered. During the flooding process, the grayscale value of each pixel is recorded, and the deviation from the current minimum grayscale value is calculated. Step S332: If the first grayscale difference is greater than or equal to the predetermined grayscale threshold, a first segmentation line is set in the region. If the first grayscale difference is less than the predetermined grayscale threshold, the simulated flooding continues. Specifically, when the deviation between the first grayscale value and the minimum grayscale value (the first grayscale difference) during the water filling process reaches or exceeds a predetermined grayscale threshold, this is considered a significant grayscale change point. A segmentation line is set at this significant change point to segment the image into different regions. If the first grayscale difference is less than the predetermined grayscale threshold, water filling continues to search for the next possible significant change point. In step S333, water filling is iteratively performed until the water surface exceeds the maximum grayscale value. At this point, water filling is stopped and multiple segmentation lines are obtained. Specifically, the simulated water filling is continued, gradually expanding the considered range until the water surface (i.e., the considered grayscale value range) covers the maximum grayscale value. During this process, grayscale value changes are continuously recorded, and segmentation lines are set based on the predetermined grayscale threshold. In step S334, the grayscale image of the tank is segmented using the multiple segmentation lines, obtaining multiple local tank regions and multiple regional grayscale values. The regional grayscale value is the median grayscale value of the local tank region. Specifically, the grayscale image of the tank is segmented into multiple local regions based on the multiple segmentation lines. For each local area, the median grayscale value is calculated as the regional grayscale value. This implementation uses an image segmentation method that simulates flooding. This method can adaptively handle the distribution of different grayscale values and effectively segment images with complex grayscale value variations, ensuring the accuracy of the segmentation results and providing a reliable basis for subsequent calculation and adjustment of spray thickness.

[0034] Step S400, obtain the target grayscale value of the target spraying thickness, and with the target grayscale value as a reference, perform deviation calculation on the grayscale values of the multiple regions respectively to determine multiple secondary spraying thicknesses. Specifically, the target grayscale value of the target spraying thickness is obtained through experiments or standard data. The target grayscale value is the grayscale value corresponding to the target spraying thickness and is the reference standard for the spraying operation. With the target grayscale value as a reference, perform deviation calculation on the grayscale value of each local area of the storage tank to obtain multiple regional grayscale differences (the difference between the actual grayscale value and the target grayscale value, which reflects the deviation of the spraying thickness). Based on these grayscale differences, determine the secondary spraying thickness of each region (the thickness of the additional spraying required to achieve the target spraying thickness after the first spraying) to compensate for the shortcomings of the first spraying.

[0035] In one possible implementation, the target grayscale value of the target spraying thickness is obtained, and step S400 further includes step S410, which obtains the standard grayscale value of the target spraying thickness under standard lighting parameters. Specifically, based on experimental or historical data, the grayscale value corresponding to the target spraying thickness is determined under standard lighting conditions (such as specific lighting intensity, lighting angle, and lighting color, etc.), using standard spraying equipment and materials. This grayscale value is called the standard grayscale value. Step S420, obtains the ambient lighting parameters when the spray tank image is collected once, performs deviation calculation on the ambient lighting parameters and the standard lighting parameters, and determines the lighting parameter difference. Specifically, at the actual spraying operation site, use a lighting sensor or other equipment to measure the ambient lighting parameters when collecting the spray tank image once. Compare the measured ambient lighting parameters with the standard lighting parameters, and calculate the difference between the two, that is, the lighting parameter difference. In step S430, the illumination parameter difference is input into an illumination corrector, which outputs a grayscale correction coefficient. The illumination corrector is constructed based on a BP neural network and trained with sample data until convergence. Specifically, an illumination corrector based on a BP neural network is constructed. The BP neural network is a multi-layer feedforward neural network that is trained with a back-propagation algorithm and can approximate complex nonlinear functions. The illumination corrector is trained with sample data (including grayscale values under different illumination conditions and corresponding illumination parameter differences) until the network converges. In actual applications, the calculated illumination parameter difference is input into a trained illumination corrector, which outputs a corresponding grayscale correction coefficient. The grayscale correction coefficient is a coefficient used to correct grayscale values to eliminate the influence of illumination conditions on grayscale value measurement. In step S440, the standard grayscale value is parameter-corrected according to the grayscale correction coefficient to obtain the target grayscale value. Specifically, the grayscale correction coefficient obtained in step 430 is used to correct the standard grayscale value obtained in step 410. The corrected grayscale value becomes the target grayscale value, which is then compared with the grayscale values of multiple regions in the primary spray tank image to calculate deviations and determine the secondary spray thickness. This implementation method uses the illumination corrector to correct grayscale values, eliminating the influence of lighting conditions on grayscale value measurement, thereby improving the accuracy of grayscale value measurement.

[0036] In one possible implementation, the target grayscale value is used as a reference, and the deviation calculation is performed on the grayscale values of the multiple regions respectively to determine multiple secondary spraying thicknesses. Step S400 further includes step S450, which is based on the target grayscale value and performs deviation calculation on the grayscale values of the multiple regions respectively to determine multiple regional grayscale differences. Specifically, for each local area, the grayscale value is used as a reference and the difference calculation is performed with the target grayscale value. This difference reflects the degree of deviation between the current grayscale of the area and the target grayscale, that is, the regional grayscale difference. Step S460 inputs the multiple regional grayscale differences into a predetermined grayscale difference-secondary spraying thickness comparison table for matching, and outputs the multiple secondary spraying thicknesses. Specifically, a grayscale difference-secondary spraying thickness comparison table is prepared in advance, which records the secondary spraying thicknesses corresponding to different grayscale differences. This table is obtained through experiments or historical data and reflects the correspondence between grayscale differences and secondary spraying thicknesses. For each regional grayscale difference, the corresponding spray thickness is searched in the grayscale difference-secondary spray thickness comparison table. All the spray thicknesses found are output as the secondary spray thickness to guide the secondary spray operation. This implementation method directly converts grayscale differences into secondary spray thickness through the grayscale difference-secondary spray thickness comparison table, avoiding the error accumulation problem that may occur in traditional methods and improving the accuracy of spray operations.

[0037] Step S500, generates a secondary spraying path sequence and a secondary spraying parameter sequence according to the multiple local areas of the storage tanks and the multiple secondary spraying thicknesses, and controls the spraying robot to perform secondary spraying operations on the target storage tanks based on the secondary spraying path sequence and the secondary spraying parameter sequence. Specifically, based on the determined multiple local areas of the storage tanks and the secondary spraying thicknesses, a path sequence and a parameter sequence for secondary spraying are generated. Among them, the secondary spraying path sequence refers to the order and path that the spraying robot needs to follow when performing secondary spraying. The secondary spraying parameter sequence refers to the various parameters that the spraying robot needs to follow when performing secondary spraying, such as spraying speed, spraying pressure, etc. The spraying robot performs secondary spraying operations on the target storage tank according to these paths and parameters to achieve the target spraying thickness. The embodiment of the present application adopts a method of performing a spraying operation on the target tank according to the initial spraying parameters, using a multi-camera to capture images of the target tank after the first spraying, and performing image stitching to generate a sprayed tank image, performing grayscale processing on the first sprayed tank image, segmenting the tank grayscale image according to a predetermined grayscale threshold, determining multiple local areas of the tank and multiple regional grayscale values, obtaining a target grayscale value of the target spraying thickness, and using this value as a reference to perform deviation calculation on the grayscale values of the multiple regions, determining multiple secondary spraying thicknesses, generating a secondary spraying path sequence and a secondary spraying parameter sequence according to the multiple local areas of the tank and the multiple secondary spraying thicknesses, and controlling the spray robot to perform a secondary spraying operation on the target tank, etc., thereby achieving the technical effect of improving positioning accuracy and realizing uniform distribution of spraying thickness.

[0038] like Figure 2As shown, in one possible implementation, a secondary spraying path sequence and a secondary spraying parameter sequence are generated based on the multiple tank local areas and multiple secondary spraying thicknesses. Step S500 further includes step S510, arranging the multiple tank local areas from large to small in area to generate a tank local area sequence. Specifically, the area of each tank local area is measured using image processing techniques (such as edge detection and contour extraction). These areas are sorted according to their area size. The principle of sorting is to arrange them from large to small in area, thereby ensuring that large areas are processed first and small areas are processed later in the secondary spraying operation. The sorted areas are sequentially generated into a tank local area sequence, which is used to guide secondary spraying path planning and parameter setting. Step S520, with the purpose of covering the tank local area sequence, performs operation simulation of the spraying robot in the spraying operation twin space, and generates the secondary spraying path sequence based on the simulation results. Specifically, a twin space for the spraying operation corresponding to the real-world spraying environment is constructed. This space includes the tank's geometric model, the spraying robot's motion model, and environmental parameters. Within the twin space, the spraying robot's operation is simulated with the goal of covering a sequence of local areas of the tank. This includes determining parameters such as the robot's motion trajectory to ensure complete coverage of all areas. Based on the simulation results, a spraying path sequence is generated, which includes the spraying robot's motion trajectory and related control instructions in each local area. In step S530, based on the sequence of local areas of the tank, a secondary spraying thickness sequence is generated according to the multiple secondary spraying thickness mappings. Specifically, each local area of the tank is mapped to its corresponding secondary spraying thickness by establishing a mapping table or database, which contains the identifier of each area and the corresponding spraying thickness. Based on the order of the local area sequence of the tank, the spraying thickness of each area is sequentially extracted, and a secondary spraying thickness sequence is generated. This sequence is used to guide the setting of secondary spraying parameters. In step S540, the secondary spraying parameter sequence is generated according to the secondary spraying thickness sequence, with the secondary spraying thickness being the operational constraint. Specifically, clarifying the secondary spray thickness is the main constraint of the spraying operation, that is, when generating the spray parameters, it is necessary to ensure that the spray thickness of each area meets the requirements. According to the secondary spray thickness sequence and the performance parameters of the spray robot (such as spray speed, spray flow, etc.), a set of appropriate spray parameters are generated for each area. These parameters can ensure that the required spray thickness is achieved in each area. The spray parameters of all areas are arranged in the order of the local area sequence of the tank to generate a secondary spray parameter sequence. This sequence is directly used to control the operation process of the spray robot. This implementation method reduces the number of movements and spraying time of the spray robot by processing large areas first and then small areas, thereby improving the spraying efficiency.

[0039] In one possible implementation, the secondary spraying parameter sequence is generated according to the secondary spraying thickness sequence, and step S540 further includes step S541, so as to meet the secondary spraying thickness as an operation constraint, and perform operation simulation in the spraying operation twin space based on the secondary spraying thickness sequence to generate an initial secondary spraying parameter sequence. Specifically, in the spraying operation twin space, the operation process of the spraying robot is simulated according to the secondary spraying thickness sequence, including the setting of parameters such as the spraying path, speed, and flow rate. According to the simulation results, a set of spraying parameters that can meet the secondary spraying thickness requirements is adjusted and determined to form an initial secondary spraying parameter sequence. Step S542, a spraying operation deviation analysis is performed based on the grayscale values of the multiple local areas of the storage tanks and the multiple areas, and a spraying thickness deviation is determined. Specifically, the grayscale values of the multiple areas are compared with the expected target grayscale values, and the spraying thickness deviation of each area is calculated. According to the spraying thickness deviation of all areas, the overall deviation of the single spraying operation is analyzed. Step S543, input the primary spraying thickness deviation into the spraying parameter compensation model, output the spraying parameter compensation coefficient, compensate the initial secondary spraying parameter sequence, and obtain the secondary spraying parameter sequence. Specifically, the primary spraying thickness deviation calculated in step 542 is used as input and input into the spraying parameter compensation model. The spraying parameter compensation model is a model based on machine learning technology, which is used to calculate the compensation coefficient of the spraying parameter according to the spraying thickness deviation. Using the machine learning algorithm, the model calculates the corresponding spraying parameter compensation coefficient based on the input deviation data. The calculated compensation coefficient is applied to the initial secondary spraying parameter sequence, and each parameter is adjusted accordingly to obtain the final secondary spraying parameter sequence. This implementation method accurately understands the problems existing in the spraying process through deviation analysis after a single spraying operation, and optimizes the secondary spraying parameters using the spraying parameter compensation model, thereby improving the spraying accuracy and spraying quality.

[0040] In one possible implementation, a spraying operation deviation analysis is performed based on the multiple local areas of the multiple tanks and the multiple regional grayscale values to determine the deviation of the spraying thickness. Step S542 further includes step S5421, obtaining multiple regional areas of the multiple local areas of the multiple tanks and calculating multiple regional area ratios. Specifically, the area data of each local area of the tank is extracted from the image segmentation results. The areas of all regions are added together to obtain the total area, and then the proportion of each region area to the total area is calculated to obtain multiple regional area ratios. Step S5422, multiplying the multiple regional area ratios and the multiple regional grayscale values, and summing them to obtain the grayscale mean of the spraying. Specifically, each regional area ratio is multiplied by the grayscale value of the region to obtain a weighted grayscale value. All weighted grayscale values are added together to obtain the grayscale mean of the spraying. Step S5423, inputting the grayscale mean of the spraying into a predetermined grayscale-spraying thickness comparison table for matching, and outputting the grayscale mean of the spraying thickness. Specifically, in a predetermined grayscale-spraying thickness comparison table, the grayscale value closest to the grayscale mean of the first spraying and its corresponding spraying thickness are searched, and the spraying thickness found is output as the average of the first spraying thickness. Step S5424, the deviation between the first spraying thickness and the average of the first spraying thickness is calculated to obtain the deviation of the first spraying thickness. Specifically, the first spraying thickness is subtracted from the average of the first spraying thickness to obtain the deviation of the first spraying thickness, and the deviation of the first spraying thickness is output for parameter compensation of the second spraying. This implementation method takes into account the contribution of the area of different regions to the overall grayscale when calculating the average of the first spraying grayscale, so that the result is more accurate and reliable.

[0041] The above specific embodiments do not constitute a limitation to the scope of protection of this application. It should be understood by those skilled in the art that various modifications, combinations and substitutions can be made according to design requirements and other factors. Any modifications, equivalent replacements and improvements made within the spirit and principles of this application should be included in the scope of protection of this application. In some cases, the actions or steps recorded in this application can be performed in an order different from that in the embodiments and can still achieve the desired results. In addition, the processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

Claims

1. A method for precise positioning and motion control of industrial robots based on visual perception, characterized in that: Methods include: Controlling the spraying robot to perform a spraying operation on the target tank according to the initial spraying parameters, wherein the initial spraying parameters are set based on the primary spraying thickness, and the primary spraying thickness is 70% of the target spraying thickness; Use a multi-camera to collect images of the target tank after a spraying operation, and perform image stitching to generate a sprayed tank image; Performing grayscale processing on the once-sprayed tank image, segmenting the tank grayscale image according to a predetermined grayscale threshold, and determining a plurality of tank local regions and a plurality of regional grayscale values; Obtaining a target grayscale value of the target spraying thickness, and performing deviation calculation on the grayscale values of the multiple regions based on the target grayscale value to determine multiple secondary spraying thicknesses; generating a secondary spraying path sequence and a secondary spraying parameter sequence according to the multiple local areas of the storage tanks and the multiple secondary spraying thicknesses, and controlling the spraying robot to perform a secondary spraying operation on the target storage tank based on the secondary spraying path sequence and the secondary spraying parameter sequence; Segment the tank grayscale image according to a predetermined grayscale threshold to determine multiple tank local areas and multiple regional grayscale values, including: Obtaining a predetermined grayscale threshold, wherein the predetermined grayscale threshold is set based on a minimum spraying accuracy of the spraying robot; Extracting grayscale values from the grayscale image of the storage tank to obtain a minimum grayscale value and a maximum grayscale value; Segmenting the tank grayscale image based on the predetermined grayscale threshold, the minimum grayscale value, and the maximum grayscale value to obtain a plurality of tank local regions and a plurality of regional grayscale values; Segmenting the tank grayscale image based on the predetermined grayscale threshold, the minimum grayscale value, and the maximum grayscale value includes: Taking the position coordinates of the minimum grayscale value as a starting point, simulate filling the grayscale image of the storage tank with water, record the first grayscale value submerged by the water surface, and calculate the deviation between the minimum grayscale value and the first grayscale value to obtain a first grayscale difference value; If the first grayscale difference is greater than or equal to the predetermined grayscale threshold, a first dividing line is set in the region; if the first grayscale difference is less than the predetermined grayscale threshold, the simulated watering is continued; Iteratively flooding the water until the water surface exceeds the maximum grayscale value, stopping flooding and harvesting multiple segmentation lines; The tank grayscale image is segmented using the multiple segmentation lines to obtain multiple tank local regions and multiple regional grayscale values, wherein the regional grayscale value is the grayscale median value of the tank local region.

2. The method for precise positioning and motion control of an industrial robot based on visual perception according to claim 1, characterized in that: Obtaining a target grayscale value of the target spraying thickness includes: Obtaining a standard grayscale value of the target spraying thickness under standard lighting parameters; Obtaining the ambient lighting parameters during the first spray tank image acquisition, performing deviation calculation on the ambient lighting parameters and standard lighting parameters, and determining the lighting parameter difference; Inputting the illumination parameter difference into an illumination corrector and outputting a grayscale correction coefficient, wherein the illumination corrector is constructed based on a BP neural network and trained with sample data until convergence; The standard grayscale value is parameter-corrected according to the grayscale correction coefficient to obtain the target grayscale value.

3. The method for precise positioning and motion control of an industrial robot based on visual perception according to claim 1, characterized in that: Based on the target grayscale value, deviation calculation is performed on the grayscale values of the multiple regions respectively to determine multiple secondary spraying thicknesses, including: Based on the target grayscale value, performing deviation calculation on the grayscale values of the multiple regions respectively to determine multiple regional grayscale differences; The grayscale differences of the multiple regions are input into a predetermined grayscale difference-secondary spraying thickness comparison table for matching, and the multiple secondary spraying thicknesses are output.

4. The method for precise positioning and motion control of an industrial robot based on visual perception according to claim 1, characterized in that: Generating a secondary spraying path sequence and a secondary spraying parameter sequence according to the multiple local areas of the storage tank and the multiple secondary spraying thicknesses includes: Arranging the plurality of storage tank local regions according to area from largest to smallest to generate a storage tank local region sequence; For the purpose of covering the local area sequence of the tank, the operation simulation of the spraying robot is performed in the spraying operation twin space, and the secondary spraying path sequence is generated based on the simulation results; Based on the tank local area sequence, generating a secondary spraying thickness sequence according to the plurality of secondary spraying thickness maps; Taking the secondary spraying thickness as an operation constraint, the secondary spraying parameter sequence is generated according to the secondary spraying thickness sequence.

5. The method for precise positioning and motion control of an industrial robot based on visual perception according to claim 4, characterized in that: Generating the secondary spraying parameter sequence according to the secondary spraying thickness sequence includes: Taking the secondary spraying thickness as the operation constraint, performing operation simulation in the spraying operation twin space based on the secondary spraying thickness sequence to generate an initial secondary spraying parameter sequence; Perform a spraying operation deviation analysis based on the grayscale values of the multiple local areas of the storage tanks and the multiple areas to determine the spraying thickness deviation; The primary spraying thickness deviation is input into a spraying parameter compensation model, a spraying parameter compensation coefficient is output, and the initial secondary spraying parameter sequence is compensated to obtain the secondary spraying parameter sequence.

6. The method for precise positioning and motion control of an industrial robot based on visual perception according to claim 5, characterized in that: A spraying operation deviation analysis is performed based on the grayscale values of the multiple local areas of the storage tanks and the multiple areas to determine the spraying thickness deviation, including: Acquire multiple area ratios of the local areas of the multiple storage tanks; Performing a product operation on the area ratios of the multiple regions and the grayscale values of the multiple regions, and summing the sums to obtain a spraying grayscale mean; Inputting the grayscale mean value of the spraying once into a predetermined grayscale-spraying thickness comparison table for matching, and outputting the grayscale mean value of the spraying once; Deviation calculation is performed on the primary spraying thickness and the primary spraying thickness mean to obtain the primary spraying thickness deviation.

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